Statistical Criteria Research Articles

Effect of heat stress on semen characteristics and genetics in Thai native grandparent roosters

Grandparent roosters are crucial in poultry breeding programs and significantly influence future bird generations' genetic makeup and performance. However, these roosters face considerable challenges from heat stress, which can adversely affect their reproductive performance, semen quality, and overall health and welfare. Our study aimed to investigate the effects of heat stress on the genetics of semen characteristics, identify the appropriate temperature and humidity indices (THI), and determine the threshold point of heat stress to prevent thermal stress. We analyzed data from 3,895 records of 242 Thai native grandparent roosters in conjunction with the THI using 7 THI functions and the regression method. The threshold point of heat stress, genetic parameters, rate of decline of semen characteristics per level of THI, estimated breeding values and selection index values were analyzed using the multivariate test-day model in the AIREML and BLUPF90 programs. Based on the regression coefficient and statistical criteria of the lowest -2logL and AIC values, the results showed that a THI of 78 was considered the threshold point of heat stress. The estimated heritability values ranged from 0.023 to 0.032, 0.066 to 0.069, 0.047 to 0.057, and 0.022 to 0.024 for mass movement, semen volume, sperm concentration, and the semen index, respectively. The reduction rates of mass movement, semen volume, sperm concentration, and semen index at a THI of 78 were -0.009, -0.003, -0.170, and -0.083 per THI, respectively. The genetic correlations among the semen traits were moderately to strongly positive and ranged from 0.562 to 0.797. The genetic correlations between semen traits and heat stress were negative and ranged from -0.437 to -0.749. The permanent environmental correlations among the semen traits (0.648-0.929) were positive and greater than the genetic correlations. Permanent environmental correlations between semen traits and heat stress were negative and ranged from -0.539 to -0.773. The results of the selection indices showed that the higher the selection intensity was, the greater the degree to which the selection index corresponded to genetic progress. The recommendation for animal genetic selection is that the top 10% is appropriate because it seems most preferred. Therefore, using a multivariate test-day model and selection index for the high genetic potential of semen traits and heat tolerance in Thai native grandparent roosters makes it possible to achieve genetic assessment in a large population.

A Retrospective Observational Study of Continuous Wireless Vital Sign Monitoring via a Medical Grade Wearable Device on Hospitalized Floor Patients.

Background: Continuous vital sign monitoring via wearable technology, combined with algorithm-based notifications, has been utilized for early detection of patient deterioration. In this retrospective observational study, we summarize a large-scale implementation of a continuous monitoring system in medical-surgical units of two hospitals over the course of fifteen (15) months. Methods: An FDA-cleared wireless monitoring device (BioButton®, BioIntelliSense Inc., Golden, CO, USA), was placed on each patient upon admission. The wearable device measures heart rate and respiratory rate at rest, skin temperature, and patient activity levels. High-frequency data (up to 1440 measurements per day) are transmitted to display in exception management software (BioDashboard™, version 2.9, BioIntelliSense Inc.). Algorithmic and rules-based notifications are triggered based on clinical and statistical trending criteria. We present (i) agreement of device readings with bedside charted measurements, (ii) the frequency of notifications, (iii) the occurrence of notifications prior to clinical deterioration events, and (iv) impact on clinical management, including early data on length of stay (LOS). Results: In total, 11,977 patient encounters were monitored at two sites. Bias ±95% limits of agreement were 1.8 ± 12.5 for HR and 0.4 ± 8.0 for RR. The rates of notifications were 0.97 and 0.65 per patient-day at Sites 1 and 2, respectively. Among clinical deteriorations, 73% (66%) had at least one notification within 24 h prior at Site 1 (Site 2). At Site 1, there were 114 cases for which a notification led to a new or changed physician's order. LOS in the first unit monitored by the system exhibited a decreasing trend from 3.07 days to 2.75 days over 12 months. Conclusions: Wearable continuous vital sign monitoring with the BioIntelliSense BioButton® system enables early detection of clinical deterioration.

Machine Learning-Driven Biomarker Discovery for Skeletal Complications in Type 1 Gaucher Disease Patients.

Type 1 Gaucher disease (GD1) is a rare, autosomal recessive disorder caused by glucocerebrosidase deficiency. Skeletal manifestations represent one of the most debilitating and potentially irreversible complications of GD1. Although imaging studies are the gold standard, early diagnostic/prognostic tools, such as molecular biomarkers, are needed for the rapid management of skeletal complications. This study aimed to identify potential protein biomarkers capable of predicting the early diagnosis of bone skeletal complications in GD1 patients using artificial intelligence. An in silico study was performed using the novel Therapeutic Performance Mapping System methodology to construct mathematical models of GD1-associated complications at the protein level. Pathophysiological characterization was performed before modeling, and a data science strategy was applied to the predicted protein activity for each protein in the models to identify classifiers. Statistical criteria were used to prioritize the most promising candidates, and 18 candidates were identified. Among them, PDGFB, IL1R2, PTH and CCL3 (MIP-1α) were highlighted due to their ease of measurement in blood. This study proposes a validated novel tool to discover new protein biomarkers to support clinician decision-making in an area where medical needs have not yet been met. However, confirming the results using in vitro and/or in vivo studies is necessary.

Mapping of IgE cross-reactivity of Gly m 4, Gly m 5 and Gly m 6 soybean allergens in patients with atopic diseases

Currently, a significant portion of the food consists of multiple different ingredients. Soybean proteins have been widely used for production of these combined foodstuffs. Meanwhile, soy proteins may contain a wide range of allergens, and even, if present in small amounts, they can cause severe allergic reactions due to cross-reactivity. The aims of our study were to evaluate the prevalence of sensitization to soybean allergen components Gly m 4, Gly m 5 and Gly m 6, and to map their IgE cross-reactivity with homologous proteins of Bet v1-like proteins and cupin proteins in patients with atopic disorders. We have studied IgE’s to 112 allergenic components of plant and animal origin in blood sera from 54 patients with history of allergy, using the ImmunoCap ISAC method. The results were analyzed by means of MS Excel program using parametric statistical criteria. Results: In patients with atopic diseases, we have detected serum IgE to the rGly m 4 allergic component (5 patients out of 29; 17.24%). Frequency of sIgE detection to rGly m 5 (3 patients of 29; 10.24%) and rGly m 6 (1 patient of 29; 3.44%) was less pronounced. Cross-reactive sIgE was detected only to allergens of the Bet v1 superfamily. The strongest relationship was found between sIgE level to rGly m 4 and rBet v1 (R = 0.68; p = 0.001). Another IgE cross-reaction was found between soybean rGly m 4 and alder pollen rAln g1 (R = 0.681; p = 0.000). IgE antibodies to rGly m 4 may also cross-react with kiwi rAct d8 (R = 0.59; p = 0.001). We have also found a weak correlation between sIgE to soybean rGly m 4 and two hazelnut rCor a1 isoforms: rCor a 1.01 (R = 0.42; p = 0.023), and rCor a 1.04 (R = 0.39; p = 0.036). The IgE cross-reactivity profile of the soy allergens revealed in this study is important for improvement of testing strategy for the presence of causally significant allergens. This finding will help to avoid the development of hidden cross-reactions that trigger both oral and respiratory allergic processes in subjects with allergic pathology. Moreover, this will enable administration of optimal diets and develop technologies for development of hypoallergenic food products.

Prostate-Specific Antigen Screening and Prostate Cancer Mortality: An Emulation of Target Trials in US Medicare.

No consensus about the effectiveness of prostate-specific antigen (PSA) screening exists among clinical guidelines, especially for the elderly. Randomized trials of PSA screening have yielded different results, partly because of variations in adherence, and it is unlikely that new trials will be conducted. Our objective was to estimate the effect of annual PSA screening on prostate cancer (PC) mortality in Medicare beneficiaries age 67-84 years. This is a large-scale, population-based, observational study of two screening strategies: annual PSA screening and no screening. We used data from 537,599 US Medicare (2001-2008) beneficiaries age 67-84 years who had a good life expectancy, no previous PC, and no PSA test in the 2 years before baseline. We estimated the 8-year PC mortality and incidence, treatments for PC, and treatment complications of PSA screening. In men age 67-74 years, the estimated difference in 8-year risk of PC death between PSA screening and no screening was -2.3 (95% CI, -4.1 to -1.1) deaths per 1,000 men (a negative risk difference favors screening). Treatment complications were more frequent under PSA screening than under no screening. In men age 75-84 years, risk difference estimates were closer to zero. Our estimates suggest that under conventional statistical criteria, annual PSA screening for 8 years is highly compatible with reductions of PC mortality from four to one fewer PC deaths per 1,000 screened men age 67-74 years. As with any study using real-world data, the estimates could be affected by residual confounding.

Observational constraints on FLRW, Bianchi type I and V brane models

This study explores the compatibility of Covariant Extrinsic Gravity (CEG), a braneworld scenario with an arbitrary number of non-compact extra dimensions, with current cosmological observations. We employ the chi-square statistic and Markov Chain Monte Carlo (MCMC) methods to fit the Friedmann–Lemaître–Robertson–Walker (FLRW) and Bianchi type-I and V brane models to the latest datasets, including Hubble, Pantheon+ Supernova samples, Big Bang Nucleosynthesis (BBN), Baryon Acoustic Oscillations (BAO), and the structure growth rate, fσ8(z). Parameters for FLRW universe consist Ω0(b),Ω0(cd),Ω0(k),H0,γ,σ8, while for the Bianchi model are Ω0(b),Ω0(cd),Ω0(β),H0,γ,Ω0(θ),σ8. By comparing our models to observational data, we determine the best values for cosmological parameters. For the FLRW model, these values depend on the sign of γ (which gives the time variation of gravitational constant in Hubble time unit): γ>0 yields γ=0.00008−0.00011+0.00015, and Ω0(k)=0.014−0.022+0.024 and γ<0 leads to γ=−0.0226−0.0062+0.0054, and Ω0(k)=0.023−0.041+0.039. It should be noted that in both cases Ω0(k)>0, which represents a closed universe. Similarly, for the Bianchi type-V brane model, the parameter values vary with the sign of γ, resulting in γ=0.00084−0.00021+0.00019, Ω0(β)=0.0258−0.0063+0.0052, and Ω0θ(×10−5)=4.19−0.75+0.67 (as with the density parameter of stiff matter) for γ>0, and γ=−0.00107−0.00020+0.00019, Ω0(β)=0.0259−0.0062+0.0050, and Ω0θ(×10−5)=4.17−0.98+0.91 for γ<0. In both cases Ω0(β)>0, which represents the Bianchi type-V, because in the Bianchi type-I, β=0. Subsequently, utilizing these obtained best values, we analyze the behavior of key cosmological parameters such as the Hubble parameter, deceleration parameter, distance modulus, equation of state, and density parameters that characterize both matter and the geometric component of dark energy, as functions of redshift. Our results notably show that the FLRW model with γ<0 is more compatible with observational data than the Bianchi model, based on various statistical criteria.

Optical Apparatus of the Eye under Conditions of “Dry” Immersion

The studies of the astronauts’ vision show that after space flight various changes can occur in the visual system: flattening of the eyeball, optic disc edema, hyperopic refractive shift, and others. The mechanisms of those changes are being actively studied. Among model experiments simulating aggressive factors of space flight, experiments on modeling microgravity in conditions of “dry” immersion seem to be one of the most promising in terms of studying eye changes. The aim: to evaluate the prospects of using “dry” immersion as a model for studying intraocular changes in space flight conditions. The paper presents data on the evaluation of the eye condition of the test subjects after being in the immersion bath for 5 days (10 subjects) and 21 days (6 subjects). Refraction and dynamic accommodation were evaluated in all subjects using an autorefkeratometer Righton Speedy-i k-model with dynamic accommodation assessment function. The refraction data showed a trend toward a positive refractive shift of 0.11 diopters on average for 5 days of immersion (confidence interval (–0.06)–(+0.28)) and 0.29 diopters for 21 days of immersion (c.i. (–0.28)–(+0.86)); no statistical significance of the differences was confirmed, which may be due to the small number of subjects and the limited power of the statistical criterion. However, the trend requires further investigation. A significant individual variation was shown in the results of accommodation assessment. Preliminary data suggest that “dry” immersion may be a promising model for studying the effects of reduced gravity on the human visual system. Further studies are needed.

Evaluation of underground hard rock mine pillar stability using gene expression programming and decision tree‐support vector machine models

AbstractAssessing the stability of pillars in underground mines (especially in deep underground mines) is a critical concern during both the design and the operational phases of a project. This study mainly focuses on developing two practical models to predict pillar stability status. For this purpose, two robust models were developed using a database including 236 case histories from seven underground hard rock mines, based on gene expression programming (GEP) and decision tree‐support vector machine (DT‐SVM) hybrid algorithms. The performance of the developed models was evaluated based on four common statistical criteria (sensitivity, specificity, Matthews correlation coefficient, and accuracy), receiver operating characteristic (ROC) curve, and testing data sets. The results showed that the GEP and DT‐SVM models performed exceptionally well in assessing pillar stability, showing a high level of accuracy. The DT‐SVM model, in particular, outperformed the GEP model (accuracy of 0.914, sensitivity of 0.842, specificity of 0.929, Matthews correlation coefficient of 0.767, and area under the ROC of 0.897 for the test data set). Furthermore, upon comparing the developed models with the previous ones, it was revealed that both models can effectively determine the condition of pillar stability with low uncertainty and acceptable accuracy. This suggests that these models could serve as dependable tools for project managers, aiding in the evaluation of pillar stability during the design and operational phases of mining projects, despite the inherent challenges in this domain.

How collider bias affects the relationship between skin color and heart attack using directed acyclic graphs, propensity scores, and stepwise approaches

BackgroundStatistical methods are essential in epidemiology research, but they can generate erroneous estimates when selecting variables based only on statistical criteria. The use of directed acyclic graphs (DAG) helps to understand the causal relationships between variables and to avoid biases.ObjectiveCompare the estimate of the effect of skin color on heart attack obtained from three data analysis techniques: a stepwise approach based on statistical criteria, a propensity score technique, and a graphical approach based on causal criteria.MethodsPopulation-based cross-sectional study using data from the second National Health and Nutrition Examination Survey (NHANES). The exposure variable was skin color (black or non-black) and the outcome was heart attack (yes or no). Multivariable logistic regressions were carried out using the stepwise, propensity score techniques and the DAG-based approach to identify the association between the variables. In the stepwise technique, all variables potentially related to the outcome were included in the model and a forward or backward algorithm was used. The propensity score was applied, estimating the probability of exposure based on the covariates and helping to create balanced groups for comparison. Different possible causal models were developed between the variables in the DAG-based approach, identifying confounding, mediation, and collision factors. The models were created considering self-rated health as a confounding or collider variable, and the modeling results were verified.ResultsA total of 10,351 adults were evaluated, the majority female (52.1%), aged 20 to 39 years (48.5%), and with non-black skin color (90.4%). The prevalence of heart attacks was 3.0%, and 17% rated their health as fair or poor. Using different modeling techniques, no association was found between skin color and heart attack (p > 0.05), except when self-rated health, a collider variable, was included in the stepwise models. In this case, there was an inverse and biased association between the two variables, indicating a collision bias (stepwise-backward-OR 0.48; 95%CI 0.33–0.70; stepwise-forward-OR 0.64; 95%CI 0.44–0.94).ConclusionSkin color was not associated with heart attack when controlling for appropriate confounding factors. However, when adjusting for self-rated health in stepwise techniques, a colliding variable, there was an inverse and distorted association between the two variables, indicating a collider bias. The DAG-based approach and propensity score can avoid this bias by correctly identifying confounding factors and colliders.

Assessing two decades of landscape greenness in relation to temperature and precipitation in a tropical dry forest of Northwestern Mexico

Canopy greenness is an indicator of ecosystem primary productivity, which is often limited by temperature and precipitation. Changes in vegetation greening have been reported mostly at global scales. However, we still have a poor understanding of vegetation greening patterns and drivers for major vegetation types, such as the tropical dry forest, one of the most extensive vegetation types in Mexico. Here, we analyze two decades of interannual variation in greenness and its relationship to temperature and precipitation in the northmost neotropical dry forest occurring in Northwestern Mexico. We constructed time-series linear regression models using standardized anomalies with z-scores (i.e., standard deviations away from the long-term mean) for the Normalized Difference Vegetation Index (NDVI) and the climate data from 2001 to 2021. Our best models indicate both temperature and precipitation exert positive effects on vegetation greenness, particularly from a lagged effect perspective, as retained predictors were the accumulated precipitation of two monsoon (summer) seasons and previous year mean temperature. The lowest levels of landscape greenness seem connected to prolonged droughts and extreme frost events. In fact, a switch from negative to positive NDVI anomalies was observed in the years following the February 2011 extreme frost that affected much of North America, including northern Mexico. Notable, under the stricter statistical criterion of −1.7 ≥ z score ≥ 1.7, only climatic variables presented very-extreme anomalies and these were not necessarily linked to a very-extreme response in landscape greenness. However, considering the criterion of −1.3 ≥ z score ≥ 1.3, we identified several extreme NDVI anomalies and their corresponding climatic anomaly. Therefore, more flexible statistical criteria might reveal climate extremes of ecological and social relevance. Our overall findings have implications for forest and climate risk management, particularly as extreme climatic anomalies are expected to continue increasing in light of climate change.

Experimental analysis and modeling of aprepitant (an antiemetic drug for chemotherapy) solubility in subcritical water with/without co-solvent

This action described the experimental equilibrium solubility of aprepitant (APT), an antiemetic drug for chemotherapy, in subcritical water (SW) with/without ethanol as co-solvent, acquired through a static method between 298.15 and 393.15 K and 0–15 % (w/w) of ethanol and the constant pressure of 10 bar. The mole fraction of APT was obtained in the range of 0.39×10−4 to 9.10×10−4 while its mole fraction by cosolvent varied from 0.75×10−4 to 22.98×10−4. The obtained results represented the significant effect of solvent temperature on the solubility of APT. For both studied systems, the solubility data was successfully correlated with two well-known semi-empirical temperature-based models, namely the linear and modified Apelblat models. Results from applying statistical criteria exhibited that the modified Apelblat model had the highest accordance with experimental data. Finally, using the obtained correlation results, the apparent thermodynamic analysis including enthalpy, entropy, and Gibbs free energy of dissolution for APT dissolved in SW was obtained.

Evaluation of the expression of LINCOO475 and FOXO1 genes as tumor suppressor genes in breast cancer: Correlation of bioinformatics tools and experimental approaches

Breast cancer is one of the most prevalent forms of malignancy in humans and is considered the most important life-threatening factor among women worldwide. Long non-coding RNAs (LncRNAs) are thought to be potential transcripts that regulate the expression of important genes in vital processes such as cell proliferation, apoptosis, survival, and oxidative stress. LINC00475 is an important lncRNA and is located on 9q22.31 and its expression is regulated by p53. FOXO1 gene is an important regulator in physiological processes such as cell maintenance and differentiation, stimulation of cell cycle arrest, and apoptosis. In this study, we focused on identifying a possible association between FOXO1 and LINC00475 expression levels in breast cancer. Bioinformatics expression and Microarray data analysis were utilized to find their potential role as novel biomarkers in breast cancer. The correlation of gene expression levels with the survival rate of patients was determined using GEPIA. LINC00475 and FOXO1 expression levels were validated using quantitative reverse transcription PCR (qRT-PCR) in 55 tumors and 40 normal tissues. Our experiments indicated a significant decrease in the expression level of LINC00475 and FOXO1 genes in tumor tissues compared to normal samples. Moreover, the specificity and sensitivity of all statistical criteria were significant between the two groups. Also, we revealed a positive relationship between LINC00475 and FOXO1 expression in tumor samples. Based on gene expression analysis and lncRNA data, it was shown that LINC00475 and FOXO1 could represent as potential diagnostic biomarkers for early diagnosis of breast cancer.

A new sine-inspired probability model: Theoretical features with statistical modeling of the music engineering and reliability scenarios

It is a proven authenticity that probability-schooled methodologies have a cardinal role in making decisions under the vagueness circumstances. Thus, to reach meaningful and fruitful decisions under vagueness circumstances, numerous probability-focused methodologies have been introduced, promoted, and implemented. This paper also contributes to adding another useful probability model to the existing literature for data modeling in engineering-oriented areas. The new model is called the weighted sine exponentiated exponential (WSEE) distribution, which is formed by implementing the exponentiated exponential model together with a sine-adapted method. Certain properties of the WSEE distribution are given. The parameter estimation of the WSEE distribution along with the simulation study is also provided. Last, in order, we apply the WSEE distribution for analyzing three data sets taken from the musical engineering and reliability settings. The optimality of the WSEE distribution is proved by comparing its results with other prominent modifications of the exponentiated exponential distribution. Using 4 statistical criteria as judgment tools, it was shown numerically and pictorially that the WSEE distribution is an optimal and suitable model for the musical engineering reliability data sets.

Statistical Methods to Examine Racial and Ethnic Disparities in the Surgical Literature: A Review and Recommendations for Improvement.

We characterized the quality of statistical methods for studies of racial and ethnic disparities in the surgical-relevant literature during 2021-2022. Hundreds of scientific papers are published each year describing racial and ethnic disparities in surgical access, quality, and outcomes. The content and design quality of this literature has never been systematically reviewed. We searched for 2021-2022 studies focused on describing racial and/or ethnic disparities in surgical or perioperative access, process quality, or outcomes. Identified studies were characterized in terms of three methodological criteria: 1) adjustment for variables related to both race/ethnicity and outcomes, including social determinants of health (SDOH); 2) accounting for clustering of patients within hospitals or other subunits ("providers") and; 3) distinguishing within- and between-provider effects. We identified 224 papers describing racial and/or ethnic differences. Of the 38 single institution studies, 24 (63.2%) adjusted for at least one SDOH variable. Of the 186 multisite studies, 113 (60.8%) adjusted for at least one SDOH variable, and 43 (23.1%) accounted for clustering of patients within providers using appropriate statistical methods. Only 10 (5.4%) of multi-institution studies made efforts to examine how much of overall disparities were driven by within versus between provider effects. Most recently published papers on racial and ethnic disparities in the surgical literature do not meet these important statistical design criteria and therefore may risk inaccuracy in the estimation of group differences in surgical access, quality, and outcomes. The most potent leverage points for these improvements are changes to journal publication guidelines and policies.

ScBoolSeq: Linking scRNA-seq statistics and Boolean dynamics.

Boolean networks are largely employed to model the qualitative dynamics of cell fate processes by describing the change of binary activation states of genes and transcription factors with time. Being able to bridge such qualitative states with quantitative measurements of gene expression in cells, as scRNA-seq, is a cornerstone for data-driven model construction and validation. On one hand, scRNA-seq binarisation is a key step for inferring and validating Boolean models. On the other hand, the generation of synthetic scRNA-seq data from baseline Boolean models provides an important asset to benchmark inference methods. However, linking characteristics of scRNA-seq datasets, including dropout events, with Boolean states is a challenging task. We present scBoolSeq, a method for the bidirectional linking of scRNA-seq data and Boolean activation state of genes. Given a reference scRNA-seq dataset, scBoolSeq computes statistical criteria to classify the empirical gene pseudocount distributions as either unimodal, bimodal, or zero-inflated, and fit a probabilistic model of dropouts, with gene-dependent parameters. From these learnt distributions, scBoolSeq can perform both binarisation of scRNA-seq datasets, and generate synthetic scRNA-seq datasets from Boolean traces, as issued from Boolean networks, using biased sampling and dropout simulation. We present a case study demonstrating the application of scBoolSeq's binarisation scheme in data-driven model inference. Furthermore, we compare synthetic scRNA-seq data generated by scBoolSeq with BoolODE's, data for the same Boolean Network model. The comparison shows that our method better reproduces the statistics of real scRNA-seq datasets, such as the mean-variance and mean-dropout relationships while exhibiting clearly defined trajectories in two-dimensional projections of the data.

Comprehensive analysis of distinct circadian clock subtypes of adult diffuse glioma and their associations with clinicopathological, genetic, and epigenetic profiles.

The circadian clock (CC) has biological and clinical implications in gliomas. Most studies focused on CC effects on the tumor microenvironment and the application of chronotherapy. The present study focused on CC gene expression patterns and intracellular oncogenic activities. Glioma gene expression data were collected from The Human Cancer Genome Atlas (TCGA) project. After applying inclusion and exclusion criteria, we selected 666 patients from TCGA-GBM and TCGA-LGG projects and included important clinicopathological variables. The entire cohort was subjected to clustering analysis and divided into CC1 and CC2 subtypes based on statistical, biological, and clinical criteria. CC2 gliomas showed higher expression of BMAL1 and CRY1 and lower expression of CRY2 and PER2 (adjusted P < .001). CC2 gliomas had q higher activity of cell proliferation, metabolic reprogramming, angiogenesis, hypoxia, and many oncogenic signals (P < .001). The CC2 subtype contained a higher proportion of glioblastomas (P < .001) and had a worse prognosis (P < .001). Stratified Kaplan-Meier and multivariable Cox analyses illustrated that the CC subtype is an independent prognostic factor to clinicopathological characteristics (P < .001), genetic aberrations (P = .006), and biological processes (P < .001). Thus, this study shows statistical evidence of CC subtypes and their biological, and clinicopathological significance in adult gliomas.

Forecasting the Unemployment Rate in the Philippines using Box-Jenkins Methodology: Advancing Economic Strategies for Sustainable Development Goals

Unemployment remains a critical macroeconomic issue in Southeast Asia, significantly impacting the Philippines. This study aims to forecast the unemployment rate in the Philippines using ARIMA models to inform policy decisions and economic planning. The study utilized ARMA model for time series forecasting, leveraging past values and error terms to predict future data points. The methodology involved identifying suitable models through stationarity tests and autocorrelation analyses, estimating model parameters, and conducting diagnostic checks to ensure accuracy. Finally, the models were used to forecast future values and compute confidence intervals, providing insights into potential future trends. Monthly unemployment data from 2019 to 2023 was analyzed using the ARIMA (1,1,0) model, selected for its optimal fit based on statistical criteria. Initial findings indicated non-stationary unemployment data, necessitating first differencing to achieve stationarity. The ARIMA (1,1,0) model showed robustness, evidenced by diagnostic checks and Portmanteau test results confirming white noise residuals. Forecasts from 2024 to 2050 suggest fluctuating unemployment rates until 2034, followed by a gradual decline, indicating potential economic growth and improved labor market conditions. These forecasts align with the broader goal of sustainable economic development and highlight the importance of strategic policy interventions to foster job creation and economic stability in the Philippines. The study highlights the efficacy of ARIMA models in capturing unemployment trends and aiding policymakers in mitigating the socio-economic impacts of unemployment.

Improvement of drought forecasting by means of various machine learning algorithms and wavelet transformation

AbstractDrought, which is defined as a decrease in average rainfall amounts, is one of the most insidious natural disasters. When it starts, people may not be aware of it, which is why droughts are difficult to monitor. Scientists have long been working to predict and monitor droughts. For this purpose, they have developed many methods, such as drought indices, one of which is the Standardized Precipitation Index (SPI). In this study, SPI was used to detect droughts, and machine learning algorithms, including support vector machines (SVM), artificial neural networks, random forest, and decision tree, were used to predict droughts. In addition, 3 different statistical criteria, which are correlation coefficient (r), root mean square error (RMSE), and Nash–Sutcliffe efficiency (NSE), were used to investigate model performance values. The wavelet transform (WT) was also applied to improve model performance. One of the areas most impacted by droughts in Turkey is the Konya Closed Basin, which is geographically positioned in the center of the country and is among the top grain-producing regions in Turkey. The Apa Dam is one of the most significant water resources in the area. It provides water to many fertile fields in its vicinity and is affected by droughts which is why it was selected as a study area. Meteorological data, such as monthly precipitation, that could represent the region were obtained between 1955 and 2020 from the general directorate of state water works and the general directorate of meteorology. According to the findings, the M04 model, whose input structure was developed using SPI, various time steps, data delayed up to 5 months, and monthly precipitation data from the preceding month (time t − 1), produced the best results out of all the models examined using machine learning algorithms. Among machine learning algorithms, SVM has achieved the most successful results not only before applying WT but also after WT. The best results were obtained from M04, in which SVM with WT was used (NSE = 0.9942, RMSE = 0.0764, R = 0.9971).

Automated modal parameter tracking with neural network based normalization of environmental perturbations

Automated modal parameter tracking is an essential part of vibration-based structural health monitoring of engineering structures, e.g., bridges, high-rise building, wind turbines, amongst others. In this work, we conceive a robust algorithm for accomplishing this task. First, the modal parameters are extracted with by greedy clustering the stabilization diagram, which is based on covariance-driven subspace identification and the related uncertainty quantification. After the selection of a set of relevant modal parameters to be monitored, the tracking is performed by an active search of stable modes close to the reference ones, again using statistical criteria and automated clustering. The applied strategies allow for a robust method, allowing also for relatively strong variations in the natural frequencies or the MAC without losing track of the monitored modes. Finally, when EOVs (Environmental and Operational Variables) are measured and training and testing periods are defined, a neural network is trained based on the tracked frequencies and the EOVs, with the goal of correcting the environmental influence on the estimated frequencies based on the model predictions. In doing so, a distance metric is defined for damage detection, which is robust to environmental perturbations under the measured EOVs. The whole framework is showcased on monitoring data of a full-scale bridge.

Intelligent automatic operational modal analysis

This work presents a novel methodology for the automatic output-only modal identification of linear structures under ambient vibrations named Intelligent Automatic Operational Modal Analysis (i-AOMA). The proposed methodology leverages the Covariance-based Stochastic Subspace (SSI-cov) algorithm for output-only modal parameter identification and encompasses two key phases. In the first phase, the SSI-cov algorithm is executed using quasi-randomly samples of the control parameters. The corresponding stabilization diagrams are next processed via Kernel Density Estimation in order to prepare a training database for the i-AOMA's intelligent core. This is a machine learning technique (namely a Random Forest algorithm) that predicts the optimal combinations of the control parameters involved in the SSI-cov algorithm. The second phase of i-AOMA rests on the iterative generation of quasi-random control parameter samples. If a sample is classified as feasible by the intelligent core of i-AOMA, then the SSI-cov algorithm is applied, while a new sample is considered otherwise. Such iterative procedure stops once a statistical convergence criterion is fulfilled. Hence, ultimate modal estimates are carried out from the stabilization diagrams, and relevant statistics are computed to quantify the effects of the uncertainties attributable to the variability of the control parameters. The entire Python source code for i-AOMA is made freely available through public web repositories. Some applications of the proposed methodology are finally illustrated.