Information Criterion Research Articles

C-Reactive Protein-to-Serum Chloride Ratio: A Novel Marker of All-Cause Mortality in Maintenance Haemodialysis Patients

Background and Objectives: hypochloremia is an emerging risk factor for mortality in patients with chronic kidney disease. The pathophysiological mechanisms of this finding are not very clear. Some studies suggest the influence of inflammation as a synergistic factor, so we set out to analyse the association of a novel C-reactive protein-to-serum chloride ratio (CRP/Cl−) with the prognosis of maintenance haemodialysis patients and to assess its relationship with fluid status and body composition measured by bioimpedance. Materials and Methods: the present work is a retrospective cohort study of maintenance haemodialysis patients from our chronic outpatient haemodialysis programme between 1 January 2022 and 31 December 2022. (n = 281). Survival time was collected for all patients and analysed using the Kaplan–Meier method. A Cox proportional hazards regression model was used to evaluate survival probabilities. Variables included in the model were selected using a stepwise selection procedure based on the corrected Akaike information criterion (AICc), which balances model fit and complexity. Results: during a median follow-up of 306 days, 34 patients died. Patients in the fourth quartile of the CRP/Cl− (>0.118 mg/mEq) had higher overall mortality (log-rank test, p = 0.0011). In the Cox multivariate analysis, the variables significantly associated with higher mortality were higher modified Charlson index (MCI), lower body surface area (BSA), lower interdialytic weight gain (IDWG), and higher CRP/Cl− ratio. The latter variable was independently associated with higher overall mortality (adjusted hazard ratio = 1.027; 95% confidence interval [CI], 1.000–1.055 p = 0.0469). Conclusions: Higher CRP/Cl− ratio values were associated with higher all-cause mortality in our maintenance haemodialysis patients.

Clinical phenotyping and treatment response in patients with chronic heart failure: a latent class analysis from a Japanese multicentre registry

Abstract Background There are diversities of pathophysiology and co-morbidities in patients with chronic heart failure (HF), irrespective of left ventricular ejection fraction (LVEF). Therefore, the identification of subgroups with different outcomes and treatment response patterns may help to tailor strategies to each individual HF patient. Purpose We sought to identify phenogroups of patients with different prognosis and response to medical therapies in patients with chronic HF. Methods A total of 1565 patients with a broad spectrum of LVEF in a prospective multicentre registry of patients with chronic HF were enrolled between January 2020 and December 2023. Latent class analysis was performed in each subgroup using 80 features including patients’ demographics, laboratory, and echocardiographic data. The optimal number of phenogroups was determined using the first minimum of the Bayesian information criterion. The primary outcome was a composite of all-cause death and hospitalisation due to worsening HF. Results The analysis subclassified patients into six phenogroups. Patients in phenogroup A (young non-ischaemic cardiomyopathy, n = 242, 15.5%) were the youngest and had lower LVEF and lowest prevalence of coronary artery disease (CAD) and other comorbidities. Phenogroup B (atrial fibrillation [AF] and small left atrium, n = 282, 18.0%) had higher proportion of AF but smaller left atrium volume. Phenogroup C (ischaemic cardiomyopathy, n = 204, 13.0%) had the highest proportion of CAD and lower LVEF. Phenogroup D (classical HF with preserved ejection fraction [HFpEF], n =308, 19.7%) had the highest age, proportion of female, LVEF and thickness of LV wall. Phenogroup E (elderly and AF , n = 235, 15.0%) had higher age, the highest proportion of AF and largest left atrium volume. Phenogroup F (biventricular failure and multiple comorbidities, n = 294, 18.8%) had the highest proportion of co-morbidities and the lowest LVEF, right ventricular systolic function and renal function. During a median follow-up period of 538 (interquartile range 286-701) days, the incidence of the primary outcome significantly differed among the phenogroups (P < 0.001, Figure 1). Regarding treatment response for renin-angiotensin system blockers and beta blockers, there were no significant associations between the use of renin-angiotensin system blockers and the risk of the primary outcome among the phenogroups whereas the use of beta-blockers was significantly associated with increased risk of the primary outcome in patients classified to phenogroup D (classical HFpEF) (hazard ratio 1.83 95% confidence interval 1.05-3.19) among the phenogroups (Figure 2). Conclusion We identified six phenogroups with distinct clinical outcomes in patients with chronic HF, and the use of beta-blockers might have unfavorable effect in the classical HFpEF phenogroup. This phenotyping provides novel risk stratification and may aid in clinical decision making.Clinical outcomes among the phenogroupsTreatment effect among the phenogroups

The spatial and seasonal patterns and stability of the Lake Whitefish fishery in Michigan waters of southern Lake Huron

AbstractObjectiveGiven that the harvested fish from five separate fishing grounds belong to one large mixed population, our objective is to develop an overall abundance index for Lake Whitefish Coregonus clupeaformis in Michigan waters of southern Lake Huron.MethodsWe analyzed catch‐and‐effort data for individual fishing trips to five separate fishing grounds and used Bayesian information criterion to select the best model struture.ResultThe best model included the year × area × month interaction as a random effect, representing the random component in fishery dynamics that were influenced by both fishing practices and fish abundance. The best model also included the fixed effects of area × month interaction, a result consistent with our hypothesis that the seasonal spatial migrations of Lake Whitefish provide the links among the fisheries in separate fishing grounds, and the fishery yields from these separate areas were all dependent on the production in southern Lake Huron as a whole. After adjusting for the random effect of year × area × month interaction and the fixed effects of area × month interaction, the estimated patterns for the fixed year effect indicated that the Lake Whitefish abundance in Michigan waters of southern Lake Huron declined during the late 2000s but has been relatively constant after 2012.ConclusionThe declines in annual fishery yield were due to and could be explained more by the decreases in fish abundance through 2012 but thereafter primarily reflected the declines in annual fishing effort as the fisheries continued to adjust to the changed fishing conditions. We also provided general lessons for studying fisheries without fishery independent surveys but with detailed seasonal and spatial catch‐and‐effort data.

Development and validation of a risk prediction model for worsening renal function in patients with incident heart failure

Abstract Background Worsening renal function (WRF) is one of the strongest predictors of outcome in patients with heart failure (HF). The presence of WRF often influences the decision to start, up-titrate, or discontinue disease modifying HF therapies and is one of the key determinants of suboptimal guideline-directed medical therapy. Existing WRF risk scores were developed in hospitalised HF patients to predict in-hospital WRF. Their performance among incident HF is far from ideal. It is therefore important to develop a new WRF risk prediction model in people with newly diagnosed HF. Purpose This study was to develop and validate a clinical risk prediction model for 180-day WRF post diagnosis of incident HF using data from a population-based longitudinal cohort. Methods We developed and validated a multivariable logistic regression model for WRF in the 180 days post diagnosis of incident HF. Data for individuals with incident HF between 2016 to 2021 were extracted from the NELSON study conducted in Tayside, Scotland. WRF was defined as a composite event of (i) having two consecutive eGFRs declined by 40% or greater; (ii) having an eGFR < 15mL/min/1.73m2; (iii) initiation of sustained dialysis; (iv) development of end-stage kidney disease; (v) receiving kidney transplantation; or (vi) kidney related death. Four candidate models included one model using literature knowledge; one multivariable fractional polynomial (MFP) model; and two stepwise selected models based on either Akaike or Bayesian information criterion (AIC or BIC). Calibration and discrimination were assessed, and performance stability were evaluated using optimism corrected performance via 1000 times bootstrapping. Results 4076 individuals (mean age 72.8 ± 13.2; 58% male; 1081 HF with reduced ejection fraction (HFrEF), 646 with HF with mildly reduced ejection fraction (HFmrEF), 1348 HF with preserved ejection fraction (HFpEF), and 1001 HF with unknown ejection fraction (EF)) who were WRF-free when diagnosed with HF were identified. Of these, 2095 (51.4%) were in-patients, and 1981 (48.6%) were out-patients. 285 (7%) patients developed WRF within 180 days post HF diagnosis. The selected MFP model (comprising 12 predictors, Table 1) showed good calibration (slope = 1.00 [0.86, 1.14]; intercept = 0.0 [-0.13, 0.13]) discrimination (C-statistic = 0.69 [95% CI: 0.65, 0.72]), and stability (optimism corrected: calibration slope = 0.94 [0.80, 1.07]; calibration intercept = -0.14 [-0.50, 0.20]; C-statistic = 0.67 [0.64, 0.71]). Discrimination of the developed model significantly outperformed two existing risk scores (Forman and Basel, both with a C-statistic = 0.62 [0.59 – 0.65]). Conclusions We have shown that the developed model performed better in predicting WRF in individuals with newly diagnosed HF. This could have utility in identifying at risk patients with HF early in the disease trajectory for therapies that can reduce renal decline.Calibration of predicted riskModel estimates for predicting 180d WRF

Sources of stress and outcomes in carepartners of persons with chronic heart failure

Abstract Background Serving as a carepartner (CP) for someone with chronic heart failure (HF) can be challenging and difficult and may yield adverse psychological outcomes. Sources of HF CP burden and strain may come from physical, informational, and tangential demands. Yet additional life events and sources of stress may be present and compound experiences of HF CPs. Greater understanding of the factors associated with stress sources is needed to direct CP assessment and interventions. Purposes To explore the contributions of CP perceptions of HF care burden and strain, other sources of stress from occupational type and working status, and financial concerns to CP psychological outcomes and to examine the influence of social support in mitigating these outcomes. Methods CPs of persons with chronic HF, defined as providing care for the person with HF at least 4 hours a week, were enrolled. Baseline variables and measures were demographics (age, marital status, education), sources of stress (CP perceived strain and burden [Oberst, STRAIN], caring for others, occupation type, working status, and financial concerns. Psychological outcomes were overall perceived stress (PSS) and depression (CES-D) with social support measured by the Enriched Social Support Inventory (ESSI). Relationships among stress sources and outcomes were assessed using best model subsets regression analysis (Akaike information criterion) comparing the models with and without adjusting for social support. For each model, age, caring for others, CG strain (mental and physical), CG burden (time and difficulty), financial worry, occupation type and status were considered for the model. Results CPs of persons with HF (n=127; 92% women; 58% Black; primarily spouses and adult children) were included. For stress, the best model included age, caring for others, mental strain, occupation and CP burden of difficulty and time (adjusted r2=0.381). After adjusting for social support, occupation type was not significant, and adjusted r2 increased to 0.462. For depression, caring for others and CP burden (difficulty) were included in the best model subset (adjusted r2=0.208). After adjusting for social support, caring for others was not significant, and the adjusted r2 increased to 0.286. Conclusion Sources of stress from higher perceived CP burden of the person with HF, occupation type, and having other caregiving roles contributed to overall stress with social support mitigating the effects of occupation type and caring for others. These findings support further study of comprehensive assessments of sources of stress and their contributions to adverse psychological outcomes for CPs within the context of social support to tailor CP interventions to the CP’s specific needs. The consistent role of social support in attenuating psychological stress warrants study of ways to clinically incorporate social support into interventions to improve outcomes for CPs.

Identifying clinical phenotype clusters for coronary artery disease: a novel approach using clustering by unsupervised machine learning

Abstract Background Guideline recommendations for the prevention of cardiovascular (CV) events in patients with coronary artery disease (CAD) are one-size-fits-all. Clinically identifiable phenotypes needing specific considerations might exist. Purpose To identify phenotypes in patients with CAD based on clinical characteristics and their relationship with the risk of new CV events and treatment benefits. Methods Unsupervised machine learning through latent class analysis (LCA) was performed on patients with CAD included in the UCC-SMART cohort (n = 5,888) and SWEDEHEART registry (n = 67,428), to identify patient clusters (i.e., phenotypes) based on clinical characteristics: age, sex, body-mass-index, diastolic blood pressure, history of diabetes, myocardial infarction, atrial fibrillation, polyvascular disease, current smoking, non-high density lipoprotein cholesterol, estimated glomerular filtration rate, and C-reactive protein. Number of clusters was determined using the Bayesian information criterion. Event free survival and hazard ratios (HR) for major adverse cardiovascular events (MACE) as primary endpoint, including non-fatal myocardial infarction, non-fatal stroke, and cardiovascular death, were estimated per cluster. The LCA were performed in each respective cohort and cross-validated in the other cohort to assess impact of variations in clustering variables on cluster reproducibility. In UCC-SMART, bootstrapping was performed the assess stability of the LCA model and impact on cluster assignment. Results In UCC-SMART four distinct phenotypes within the CAD population could be distinguished: Phenotype A (16%) of mostly males with isolated CAD, phenotype B (30%) of older patients with polyvascular disease, Phenotype C (28%) of only women with limited traditional risk factors and Phenotype D (27%) young, current smokers with a premature event (Figure 1). Cross-validation in SWEDEHEART identified roughly the same four phenotypes, i.e., phenotype A (39%) with 100% males and 3.8% polyvascular disease, phenotype B (8%) with average age of 71 years and 30% polyvascular disease, phenotype C (19%) with 100% women, and phenotype D (34%) 54 years and 25% current smokers. Notably, although age was comparable between phenotype A and C, MACE-free survival was worse for the female-only cluster. Similarly, although patients where on average 9 years younger in phenotype D when compared to phenotype A, MACE-free survival was worse (Figure 2). No significant changes in clustering models were observed in the bootstrap samples. Conclusion Phenotypes can be distinguished within patients with CAD. These phenotypes are related to differences in the risk of recurrent events. These findings suggest that specific guideline recommendations for these groups could be considered.Cluster interpretation in SMARTSurvival along cluster lines

Statistical Models and Evaluation Criteria Used in Poultry Farming

In this thesis study, the statistical models and evaluation criteria used in poultry breeding were examined comprehensively, taking into account live weight gain, egg weight and egg numbers. For this purpose, as an example of live weight increase; 9-week live weight gains of Ross-308 broiler line, as an example of egg weight; The average of 20-week-old egg weights of brown Lohmann laying hens and 9-week egg yields of Japanese quail were used as an example of the number of eggs. In modeling, for live weight gain; 10 different models for egg production; 11 different models and 8 different models for egg weight were considered. In evaluating the models; error mean squares, coefficient of determination, corrected coefficient of determination, Akaike information criterion, corrected Akaike information criterion, Bayesian information criterion, accuracy factor, deviation factor and Durbin-Watson autocorrelation values were taken into consideration. As a result of the study, in terms of live weight gain; The cubic piecewise regression model is the best model in terms of egg yields; It was determined that the modified Compartmental model and the logistic model gave better results than the others in terms of egg weight. The worst models are in live weight gains; Brody, egg yields; It was concluded that there was a quadratic linear and Von Bertalanffy model for egg weight.

Modeling of Some Egg Characteristics in Henna Partridges

In this study, the distributions of egg count (daily and cumulative), width, length and weight values of chukar partridges obtained in two different egg production seasons were statistically modeled. For this purpose, in modeling cumulative egg production curves; Logistic, Gompertz, Gamma, Schunute, Brody, Richard, Negative Exponential, von Bertalanffy, Cubic Piecewise and Cubic models were used, and in evaluating daily egg production curves; Gompertz, Logistic, Richard, McNally, Gamma, Cubic Piecewise, Quadratic, Quadratic Piecewise and Modified Compartmental models were used. In modeling egg width values; Gompertz, Gamma, Cubic Piecewise and Cubic models were used, in modeling length values; Logistic, Gamma, Cubic Piecewise and Cubic models were used, and in modeling weight values; McNally, Gamma, Cubic Piecewise and Cubic models were used. In all modeling studies, as comparison and evaluation criteria; Error Mean Square, Corrected Coefficient of Determination, Accuracy Factor, Deviation Factor, Durbin-Watson, Akaike Information Criterion, Corrected Akaike Information Criterion and Bayesian Information Criterion were taken into consideration. As a result of the study; Gamma was determined as the most suitable model for modeling cumulative egg yields and length values, McNally for modeling daily egg yields and weight values, and Gompertz and Gamma models for modeling egg width values.

Risk Assessment of Urban Water and Energy Supply Using Copula Function: A Water–Energy Nexus Approach in an Arid City

Planning for the future of water and energy supply systems in urban areas requires a thorough assessment of associated risks. In this study, monthly water and energy demand data from 2011 to 2022 in an arid city was used to predict the corresponding demands from 2023 to 2032 using the seasonal auto-regressive integrated moving average (SARIMA) method. The aim is to estimate future water and energy supply risks both individually and jointly, using cumulative distribution functions (CDFs) derived from historical data. The main focus is to calculate the combined risk of water and energy, referred to as the water–energy nexus (WEN) risk. Based on the interdependent relationship between water and energy, the Copula function was utilized to model the bivariate distribution between these two variables. Pearson correlation analysis indicated a strong correlation between water and energy supplies. Among the distributions fitted to the data, the log-normal and gamma distributions were the best fit for water supply and energy supply systems, respectively, with the lowest Akaike information criterion (AIC) values. The Gumbel Copula, with a parameter of 1.66, was identified as the most suitable for modeling the joint distribution, yielding the lowest AIC value. The results indicate that the risks associated with energy supply, water supply, and their joint dependency could exceed 0.8% in the future, highlighting a potentially critical situation for the city. The trend analysis revealed that forecasted water and energy demands and their corresponding risks and the WEN risk are expected to have a significant upward trend in the future. Finally, local authorities need to explore alternative sources to supply water and energy in the future to address the ever-growing water and energy demands.

Magnitude and predictors of obstetric complications during delivery among postpartum women in Ethiopia: evidence from PMA Ethiopia longitudinal survey

IntroductionThe majority of pregnancy-related complications were preventable. However, the magnitude of complications during pregnancy and childbirth was high in sub-Saharan Africa. There was limited evidence on the magnitude of obstetric complications during childbirth and its predictors among postpartum women in Ethiopia.Methods and materialsThe research used secondary data sources based on performance monitoring for action in Ethiopia’s second cohort longitudinal survey. It was a national survey that was conducted in three large, predominantly agrarian regions (Oromia, Amhara, and SNNP) and one urban region (Addis Ababa) of Ethiopia between 2021 and 2023. It was conducted to track pregnant women’s use of essential maternal and newborn health services. It involved the enrollment of pregnant women and tracking them at six weeks, six months, and one year after giving birth. The data collected at the beginning of the survey and six weeks after childbirth were used for the analysis. Sample weights were applied to account for differences in sample allocation and response rates across regions and urban/rural areas. Multi-collinearity and intra-cluster correlation were assessed before fitting the multilevel models to ensure the accuracy of the estimates. A multi-level logistic regression model was used to assess the magnitude and predictors for the occurrence of obstetric complications during delivery. The model fit was evaluated using Akaike’s Information Criterion and Bayesian Information Criterion. Adjusted odds ratio with its 95% confidence interval was used to measure the strength of association for this study.ResultsThe magnitude of obstetric complications during delivery was found to be 33.86% with 95% CI (31.56, 36.24. Among the obstetric complications that occurred during delivery, about 15.73%with 95% CI(14.02, 17.61) of women experienced bleeding, 4.14% with 95% CI (3.30, 5.17) had their membrane rupture but labor did not start within 24 h, 2.29% with 95% CI(1.67, 3.13) had their membrane rupture before 9 months, 3.95% with 95% CI(3.16, 4.93) had faced malpresentation or malposition of the baby, 12.70% with 95% CI (11.18, 14.39) had prolonged labor lasting more than 12 h, and 12.40% with 95% CI (10.83,14.17) had convulsions. The odds of occurrence of obstetric complication during delivery among women from severely food insecure households were 1.88 times [AOR = 1.88; 95% CI (1.22, 2.90)] more likely to occur than women from food secure households. Moreover, the odds of occurrence for obstetric complications during delivery among women who had complications during their pregnancy were 2.39 times [AOR = 2.39; 95% CI (1.81, 3.16)] more likely to occur as compared to those women who had no complication during their pregnancy. On the other hand, women’s who had 1–4 live births given before this delivery were 0.61 times [AOR = 0.61; 95% (0.43, 0.88)] less likely to develop complications as compared to those women’s who had no prior live birth.Conclusion and recommendationDelivery-related obstetric complications during delivery in Ethiopia were high. Approximately one-third of postpartum women in Ethiopia experienced obstetric complications during delivery. Based on this study, women’s from severely food insecure households, those women’s who had no prior live birth, and women’s who had complications during their pregnancy were more likely to develop delivery-related obstetric complications. Thus, policy makers and program implementers who were working on maternal and newborn health should give special attention for women’s from severely food insecure households, women’s who had no prior live births, and those women’s who encountered obstetric complications during their pregnancy to decrease the occurrence of obstetric complications during delivery. In Ethiopia, various governmental organizations, such as the Ministry of Health and the Ministry of Agriculture, are dedicated to addressing food insecurity and improving nutritional access. The country has launched several effective nutritional programs, including the Productive Safety Net Program, the Targeted Supplementary Feeding Program, and the National Nutrition Program, aimed at alleviating food insecurity and enhancing nutrition. Ongoing efforts are crucial to tackle the food insecurity experienced by women, which can help to reduce obstetric related complications of women’s during delivery. It is crucial for everyone involved in maternal and newborn health to prioritize addressing the factors that lead to obstetric complications during delivery in women in order to reach the goal of ending all preventable maternal and newborn deaths by 2030.

Effect of chemical modification on hydraulic conductivity of stratified porous media

ABSTRACT Understanding hydraulic conductivity is essential in porous media as it dictates the capacity of fluids to permeate through these substances. This research investigated the impact of chemical modification using fly ash on hydraulic conductivity. The coefficient of hydraulic conductivity along the bedding plane at different angles of orientations was predicted by employing machine learning techniques such as gene expression programming (GEP), artificial neural networks (ANNs), and regression tree (RT), considering various influencing parameters. As the amount of fly ash in the porous medium rises, the hydraulic conductivity decreases. The statistical results demonstrate that the proposed ANN model effectively forecasted the horizontal hydraulic conductivity. It achieved the highest R2 value (0.989), the lowest mean absolute percentage error value (5.772), the lowest scatter index value (0.040), and the lowest Akaike Information Criterion value (−2,359.914) compared to the GEP, RT, and previously established approaches. Moreover, a novel equation has been proposed for determining horizontal hydraulic conductivity, which can be used for any stratified medium, distinguishing this work from previous ones. These findings can inform global practices in mitigating groundwater contamination, enhancing irrigation efficiency, and improving the design of subsurface fluid flow systems, making it a valuable contribution to both academic and practical applications worldwide.

Sex, Racial/Ethnic, and Regional Disparities in Pulmonary Embolism Mortality Trends in the USA, 1999-2020.

While the National Institutes of Health emphasize integrating sex as a biological variable into research, specific considerations of sex-related differences in pulmonary embolism (PE) mortality trends remain scarce. This study examines sex-based PE mortality trends across regional and demographic groups in the USA from 1999 to 2020. A retrospective analysis of National Center for Health Statistics mortality data from 1999 to 2020 was conducted. Using ICD-10 code I26, PE decedents were identified. Piecewise linear regression assessed sex-based temporal trends in PE mortality by age, race/ethnicity, and census region. Annual percentage changes and average annual percentage changes were derived using Weighted Bayesian Information Criteria. The 95% confidence intervals were estimated using the empirical quantile method. From 1999 to 2020, a total of 179,273 individuals died in the USA due to PE, resulting in an age-adjusted mortality rate of 2.5 per 100,000 persons (95% CI, 2.5-2.5). While men and women exhibited comparable rates in recent time segments and across most subcategories, a higher mortality trend among males compared to females was observed among non-Hispanic White and Hispanic individuals and residents of the Western US census region. These results remained robust even after excluding data from 2020, accounting for the potential impact of the COVID-19 pandemic. Our study highlights sex-based disparities in PE mortality trends in the USA from 1999 to 2020. Despite overall stable mortality rates, higher trends among males were evident in specific demographic groups and regions. These findings emphasize the importance of targeted interventions to mitigate PE-related mortality discrepancies across diverse populations.

Integration of mathematical and experimental modeling for sustainable phycobiliprotein production via fed-batch cultures

The production of phycobiliproteins, such as cyanobacterial phycocyanins, is a growing interest due to their diverse industrial and biotechnological applications. This study focuses on optimizing phycocyanin production using the strain Potamosiphon sp. through experimental techniques and mathematical modeling in fed-batch cultures. The methodology applied includes determining the kinetic constants by linearizing the Monod equation evaluating the concentrations of biomass, C-phycocyanin (C-PC), nitrates (NO3), and phosphates (PO4). A mathematical model of periodic fed-batch feeding was subsequently established, applying mass conservation principles and evaluating the accuracy of the Monod, Contois, Moser, and Tessier models. The results indicate that phycocyanin production is highly dependent on phosphorus and nitrogen concentrations, with optimal conversion observed at specific levels of these elements (0.832 for phosphorus and 0.805 for nitrogen in terms of C-PC and biomass, respectively). The Tessier model demonstrated the highest accuracy in predicting production and optimizing operational conditions, with a Mean Squared Error (MSE) of 0.005000 for biomass production, 0.200000 for C-PC production, and 0.000010 for substrate consumption. It also achieved high R² values of 0.980 for biomass, 0.999 for C-PC production, and 0.997 for substrate consumption. It presented the lowest Akaike Information Criterion (AIC) scores, indicating its robustness and reliability in modeling these processes and manipulating cultivation conditions and providing adequate nutrition allowed for achieving growth rates of 1.23 g/L and a C-PC concentration of 37 mg/L, which are essential for industrial applications such as natural colorants and antioxidants, among others.

Multivariate Cluster Point Process to Quantify and Explore Multi-Entity Configurations: Application to Biofilm Image Data.

Clusters of similar or dissimilar objects are encountered in many fields. Frequently used approaches treat each cluster's central object as latent. Yet, often objects of one or more types cluster around objects of another type. Such arrangements are common in biomedical images of cells, in which nearby cell types likely interact. Quantifying spatial relationships may elucidate biological mechanisms. Parent-offspring statistical frameworks can be usefully applied even when central objects ("parents") differ from peripheral ones ("offspring"). We propose the novel multivariate cluster point process (MCPP) to quantify multi-object (e.g., multi-cellular) arrangements. Unlike commonly used approaches, the MCPP exploits locations of the central parent object in clusters. It accounts for possibly multilayered, multivariate clustering. The model formulation requires specification of which object types function as cluster centers and which reside peripherally. If such information is unknown, the relative roles of object types may be explored by comparing fit of different models via the deviance information criterion (DIC). In simulated data, we compared a series of models' DIC; the MCPP correctly identified simulated relationships. It also produced more accurate and precise parameter estimates than the classical univariate Neyman-Scott process model. We also used the MCPP to quantify proposed configurations and explore new ones in human dental plaque biofilm image data. MCPP models quantified simultaneous clustering of Streptococcus and Porphyromonas around Corynebacterium and of Pasteurellaceae around Streptococcus and successfully captured hypothesized structures for all taxa. Further exploration suggested the presence of clustering between Fusobacterium and Leptotrichia, a previously unreported relationship.

A novel algorithm for identifying arrival times of P and S Waves in seismic borehole surveys

The arrival times of P and S waves, originating from earthquakes, diverse seismic tests, and events, are crucial geotechnical parameters. Derived from the inversion of these travel times, VP (P-wave velocity) and VS (S-wave velocity) are pivotal in geotechnical engineering, correlating directly with dynamic soil properties and enabling calculations of Poisson's Ratio (ν), Young's modulus (E), Shear modulus (μ), and Bulk modulus (B). Both VP and VS are crucial for evaluating soil behaviour under various conditions, aiding in modelling soil for settlement, wave propagation, seismic wave interaction, liquefaction potential analysis, seismic response analysis, and many more. The selection of arrival times for seismic tests, including Crosshole, Downhole, and Uphole tests, is done manually, which is time-consuming and potentially erroneous. To address this issue, various algorithms have been developed to automate the picking process. Some of these algorithms use wavelet transforms and Bayesian information criteria, while others use machine learning techniques such as artificial neural networks. These methods vary in terms of their accuracy, yet each one possesses inherent limitations when it comes to processing data with different levels of signal-to-noise ratio. The advancement of automated algorithms for determining arrival times is an ongoing and dynamic field of research. Apart from the existing research focused on determining the arrival time of P waves, there is a dearth of studies investigating the detection of S wave arrival times. To fill this gap, this study proposes new approaches for detecting both P and S wave arrival time(s). One approach entails the utilization of an iterative optimization algorithm to accurately fit a curve to the leading edge of the P waveform. The arrival time is determined by calculating a fraction relative to the highest point obtained from the fitted peak. The second approach entails identifying the exact moment of the S wave's arrival by determining the points of intersection between the oppositely polarized S waveforms. These methods provide a promising approach for automatically detecting both P and S wave arrival time(s), which has the potential to improve the precision and efficiency in picking up arrival time(s).

Analysis of genetic characteristics associated with reduced bedaquiline susceptibility in multidrug-resistant Mycobacterium tuberculosis

Bedaquiline (BDQ) has shown efficacy in shortening treatment duration and enhancing treatment success rates for multidrug-resistant tuberculosis (MDR-TB), thereby prompting widespread adoption. However, resistance to BDQ has emerged. This study aimed to identify genetic characteristics associated with decreased susceptibility to BDQ, using a public database to aid in the detection of resistant strains. Seventy-one BDQ-resistant and 929 BDQ-susceptible isolates from the open-source CRyPTIC database were selected for analysis. Variant calling was conducted via the clockwork pipeline. Univariate logistic regression was performed for each gene mutation, followed by LASSO regression for further variant selection. Ultimately, a multiple linear regression model was developed using log2-transformed Minimum Inhibitory Concentration values as the dependent variable, with variant selection refined through stepwise regression based on the Akaike Information Criterion. Ten gene mutations were significantly associated with reduced BDQ susceptibility, including two key gene mutations: Rv0678_141_ins_1 and Rv1979c_D249E, with effect estimates of 1.76 (95 % CI: 0.67–2.84) and 1.69 (95 % CI: 0.22–3.17), respectively. Other implicated genes included Rv2699c_-84_del_1, hsaB_I179T, mmpL9_T241A, pncA_C14R, Rv0373c_G621S, Rv0893c_L27F, Rv1770_A4D, and Rv3428c_S327C. This study identified ten gene mutations linked to decreased susceptibility to BDQ, providing a reference for developing a comprehensive catalog of BDQ-resistant genes.

Serum Immunoglobulin G Levels Are Associated with Risk for Exacerbations: An Analysis of SPIROMICS.

Serum Immunoglobulin G (IgG) deficiency is associated with morbidity in chronic obstructive pulmonary disease (COPD) but it is unclear whether concentrations in the lower end of the normal range still confer risk. To determine if levels above traditional cutoffs for serum IgG deficiency are associated with exacerbations among current and former smokers with or at risk for COPD. Former and current smokers in SPIROMICS (n=1,497) were studied, n=1,026 with and n=471 at risk for COPD. In a subset (n=1,031), IgG subclasses were measured. Associations between total IgG or subclasses and prospective exacerbations were evaluated with multivariable models adjusting for demographics, current smoking, smoking history, FEV1% predicted, inhaled corticosteroids, and serum IgA. The 35th percentile (1225 mg/dL in this cohort) of IgG was the best cutoff by Akaike Information Criterion (AIC). Below this, there was increased exacerbation risk (IRR 1.28, 95% CI 1.08-1.51). Among subclasses, IgG1 and IgG2 below 35th percentile (354 and 105 mg/dL, respectively) were both associated with increased risk of severe exacerbation (IgG1: IRR 1.39, 95% CI 1.06-1.84; IgG2: IRR 1.50, 95% CI 1.14-1.1.97). These associations remained significant when additionally adjusting for history of exacerbations. Lower serum IgG is prospectively associated with exacerbations in individuals with or at risk for COPD. Among subclasses, lower IgG1 and IgG2 are prospectively associated with severe exacerbations. The optimal IgG cutoff was substantially higher than traditional cutoffs for deficiency, suggesting subtle impairment of humoral immunity may be associated with exacerbations.

Prognostic prediction and treatment options for gastric signet ring cell carcinoma: a SEER database analysis.

In recent years, the overall incidence of gastric cancer has decreased. However, the incidence of gastric signet ring cell carcinoma (SRCC) is still increasing year by year. Compared with other subtypes (non-SRCC) such as adenocarcinoma, SRCC usually exhibits a more aggressive biological behavior. Therefore, studying the prognostic differences and factors associated with SRCC is essential to improve the accuracy of diagnosis and prognosis. The purpose of this study was to investigate the prognostic factors influencing the prognosis of patients with SRCC and to develop personalized treatments for different subgroups of patients. The data on gastric SRCC patients and gastric adenocarcinoma (AC) patients from 1992 to 2020 was obtained from the Surveillance, Epidemiology, and End Results (SEER) database. The data of gastric SRCC as the external validation group was reviewed from the First Affiliated Hospital of Soochow University. The overall survival (OS) and cancer specific survival (CSS) at 1 and 2 years were predicted for SRCC patients by constructing prognostic nomograms. A series of validation methods, including Akaike information criterion (AIC), decision curve analysis (DCA), calibration curve analysis, the concordance index (C-index) and the area under the receiver operating characteristic (AUC) curve, were used to verify the accuracy and reliability of the models. In all, 549 patients with SRCC were included after propensity score matching (PSM). Multivariate Cox regression analysis showed that T stage, N stage, M stage and surgical approach were independent risk factors affecting the prognosis of SRCC patients. A prognostic nomogram was constructed and validated as an accurate model for SRCC patients after scoring by receiver operating characteristic curve (ROC) curves and calibration plots. The patients were further divided into high-risk and low-risk groups, and the Kaplan-Meier curves showed that SRCC patients in the low-risk group could receive only surgery without chemotherapy, while chemotherapy plus surgery was a better option for SRCC patients in the high-risk group. The prognosis for SRCC was less favorable than that of AC in terms of CSS. The nomograms were developed and validated to predict OS and CSS in patients with SRCC, helping in developing appropriate individualized treatment schedules.

Identification of Risk Factors for Bus Operation Based on Bayesian Network

Public transit has been continuously developing because of advocacy for low-carbon living, and concerns about its safety have gained prominence. The various factors that constitute the bus operating environment are extremely complex. Although existing research on operational security is crucial, previous studies often fail to fully represent this complexity. In this study, a novel method was proposed to identify the risk factors for bus operations based on a Bayesian network. Our research was based on monitoring data from the public transit system. First, the Tabu Search algorithm was applied to identify the optimal structure of the Bayesian network with the Bayesian Information Criterion. Second, the network parameters were calculated using bus monitoring data based on Bayesian Parameter Estimation. Finally, reasoning was conducted through prediction and diagnosis in the network. Additionally, the most probable explanation of bus operation spatial risk was identified. The results indicated that factors such as speed, traffic volume, isolation measures, intersections, bus stops, and lanes had a significant effect on the spatial risk of bus operation. In conclusion, the study findings can help avert dangers and support decision-making for the operation and management of public transit in metropolitan areas to enhance daily public transit safety.

The Improved EMS Algorithm for Latent Variable Selection in M3PL Model.

One of the main concerns in multidimensional item response theory (MIRT) is to detect the relationship between items and latent traits, which can be treated as a latent variable selection problem. An attractive method for latent variable selection in multidimensional 2-parameter logistic (M2PL) model is to minimize the observed Bayesian information criterion (BIC) by the expectation model selection (EMS) algorithm. The EMS algorithm extends the EM algorithm and allows the updates of the model (e.g., the loading structure in MIRT) in the iterations along with the parameters under the model. As an extension of the M2PL model, the multidimensional 3-parameter logistic (M3PL) model introduces an additional guessing parameter which makes the latent variable selection more challenging. In this paper, a well-designed EMS algorithm, named improved EMS (IEMS), is proposed to accurately and efficiently detect the underlying true loading structure in the M3PL model, which also works for the M2PL model. In simulation studies, we compare the IEMS algorithm with several state-of-art methods and the IEMS is of competitiveness in terms of model recovery, estimation precision, and computational efficiency. The IEMS algorithm is illustrated by its application to two real data sets.