Statistical Model Research Articles

Comparative neurofilament light chain trajectories in CSF and plasma in autosomal dominant Alzheimer’s disease

AbstractDisease-modifying therapies for Alzheimer’s disease (AD) are likely to be most beneficial when initiated in the presymptomatic phase. To track the benefit of such interventions, fluid biomarkers are of great importance, with neurofilament light chain protein (NfL) showing promise for monitoring neurodegeneration and predicting cognitive outcomes. Here, we update and complement previous findings from the Dominantly Inherited Alzheimer Network Observational Study by using matched cross-sectional and longitudinal cerebrospinal fluid (CSF) and plasma samples from 567 individuals, allowing timely comparative analyses of CSF and blood trajectories across the entire disease spectrum. CSF and plasma trajectories were similar at presymptomatic stages, discriminating mutation carriers from non-carrier controls 10-20 years before the estimated onset of clinical symptoms, depending on the statistical model used. However, after symptom onset the rate of change in CSF NfL continued to increase steadily, whereas the rate of change in plasma NfL leveled off. Both plasma and CSF NfL changes were associated with grey-matter atrophy, but not with Aβ-PET changes, supporting a temporal decoupling of Aβ deposition and neurodegeneration. These observations support NfL in both CSF and blood as an early marker of neurodegeneration but suggest that NfL measured in the CSF may be better suited for monitoring clinical trial outcomes in symptomatic AD patients.

Statistical Data-Driven Modelling and Forecasting: An Application to COVID-19 Pandemic

Statistical Data-Driven Modelling and Forecasting: An Application to COVID-19 Pandemic

Inference for big data assisted by small area methods: an application on sustainable development goals sensitivity of enterprises in Italy

Abstract In this study, we proposed a new method for estimating the sensitivity of enterprises in Italy to the United Nation’s sustainable development goals at the provincial level using web-scraping data (a nonprobability sample) because this value is not surveyed by the Italian National Institute of Statistics. The proposed method used a probability sample to reduce the selection bias of estimates obtained from the nonprobability sample in the context of small area estimation and integrated nonprobability and probability samples using a double robust estimator that combined (i) propensity weighting to improve the representativeness of the nonprobability sample and (ii) a statistical model to predict the units that were not in the nonprobability sample. A bootstrap procedure for estimating variance was also proposed. To validate the proposed method, a Monte Carlo simulation was performed. Results showed that the proposed method allowed the correction of bias from the nonprobability sample while maintaining a good level of estimate reliability.

Response Surface Methodology Approach for the Prediction and Optimization of the Mechanical Properties of Sustainable Laterized Concrete Incorporating Eco-Friendly Calcium Carbide Waste

This study investigated the combined effects of calcium carbide waste (CCW) and lateritic soil (LS) on sustainable concrete’s fresh and mechanical properties as a construction material for infrastructure development. The study will explore the possibility of using easily accessible materials, such as lateritic soils and calcium carbide waste. Therefore, laterite soil was used to replace some portions of fine aggregate at 0% to 40% (interval of 10%) by weight, while CCW substituted the cement content at 0%, 5%, 10%, 15%, and 20% by weight. A response surface methodology/central composite design (RSM/CCD) tool was applied to design and develop statistical models for predicting and optimizing the properties of the sustainable concrete. The LS and CCW were input variables, and compressive strength and splitting tensile properties are response variables. The results indicated that the combined effects of CCW and LS improve workability by 18.2% compared to the control mixture. Regarding the mechanical properties, the synergic effects of CCW as a cementitious material and LS as a fine aggregate have improved the concrete’s compressive and splitting tensile strengths. The contribution of LS is more pronounced than that of CCW. The established models have successfully predicted the mechanical behavior and fresh properties of sustainable concrete utilizing LS and CCW as the independent variables with high accuracy. The optimized responses can be achieved with 15% CCW and 10% lateritic soil as a substitute for fine aggregate weight. These optimization outcomes produced the most robust possible results, with a desirability of 81.3%.

On the distribution of ocean wave crest heights in varying wave conditions

AbstractThis paper addresses the statistical distribution of wave crest heights in ocean environments. This is much needed in several engineering applications including risk and reliability assessment of marine and coastal structures and is an important input for design of ocean structures. However, even though crest height distributions have received a lot of attention within both academia and the industry, current state-of-the-art models have notable shortcomings and do not describe the distribution of crest heights well in all sea states. In particular, commonly used models do not accurately describe the upper tail in certain unstable wave conditions. This will be addressed in this paper, which investigates in what sea-state conditions, established crest height distributions describe the data well and under what conditions, these models fail to fit well to the data. A large dataset of crest heights observed in different sea states is analyzed, and various goodness-of-fit tests are applied to determine when the different statistical models are appropriate. Then rejection rates of the various statistical models will be linked to selected sea-state parameters to identify wave conditions where alternative statistical models are needed. Results from this study indicate that the well-known Forristall distribution, which is known to capture second-order effects well, fails to perform well in some sea states. Different variants of the Tayfun–Fedele distribution are also analyzed, and results suggest that this represent a slight improvement. Somewhat surprisingly, sea-state parameters such as significant wave height ($$H_S$$ H S ), significant wave steepness ($$S_1$$ S 1 ), and the Benjamin–Feir index (BFI) do not seem to be very influential on the probability of rejecting these distributions, even though empirical skewness and kurtosis play a significant role. The implications of this for describing the distribution of crest heights well in certain sea-state conditions are discussed.

Quantifying the Cooling Effect of Urban Greening Driven by Ecological Restoration Projects in China.

Urban greening (UG) affects local climate by altering surface energy balance, while long-term UG cooling potential, patterns, and contribution to curbing urban warming remain unclear. Here, we designed an novel statistical model to evaluate the cooling potential of UG (CPUG) and created the first CPUG map for China. By exploring the trends in observed and simulated urban surface temperatures (UST), we quantified the CPUG of 0.20 K over the past two decades, which slowed down the warming trend by 14.17% in Chinese cities. We found that the CPUG varied significantly between the urban core and sprawl areas. Specifically, the CPUG in the urban core was approximately 1.01 K, and it contributed to curbing urban warming by 56.08%, which was more than 7.2 times higher than in the sprawl areas, where the CPUG was only 0.14 K and contributed to curbing urban warming by 9.93%. We further revealed that urbanization and major ecological restoration projects are the key factors influencing CPUG, emphasizing the need for anthropogenic vegetation management to curb urban warming. The proposed model in this study provides a powerful tool for quantitatively assessing the impact of long-term UG trends on urban warming. The results of the study are an important reference for building climate-adaptive cities.

Understanding the effect of experimental variables on the recovery of 231Pa from siliceous cake using factorial design

AbstractFour-factor (oxalic acid concentration, contact time, temperature, ultra-sound), two-level experimental design was applied during the recovery of 231Pa from siliceous cake—a solid radioactive-waste, produced in monazite processing. Oxalic acid (0.5 to 1.1 M) had the highest positive (F = 1018.6) and ultrasound had the least (F = 33.0) impact on recovery. All inter-factor interactions were significant, as Fcalculated < Fcritical and P < 0.05. Enthalpy of protactinium-oxalate complexation was found to be − 24.8 kJ mol−1. The regression coefficient (0.9853), randomness and magnitude (< ± 2) of fitted residuals explicated the suitability of present statistical model for predictive analysis.

The triple-helix model as foundation of innovative entrepreneurial ecosystems

Purpose Recommend ways to increase the entrepreneurial performance score for city-centered entrepreneurial ecosystems (EES) and the possible role within the triplex helix concept. The former applies to lower-ranked EESs. For the latter, the authors hypothesize that the standard triple helix model of counting on some existing, well known multi-party interest in bringing together businesses and other stakeholders (especially universities and government) is insufficient. Including more players and alternative cooperation models may truly enable a long lasting productive interplay of cooperative and competitive forces leading to genuinely innovative (non-marginal, non-opportunistic) businesses and also to more regional well-being. This study aims at leveraging the city-based view on entrepreneurial ecosystems, to enable new ways of boosting the functionality of triple-helix minded programs, especially so for emerging economies and their cross-border regions. Design/methodology/approach Methodology is dual: (1) paying tribute to the history of entrepreneurial behavior and reviewing the diverse sources of support (institutional and otherwise), eventually leading to today’s concentrations of successful innovative business, and (2) analyzing modern data on performance of city-centered EESs. Putting emphasis on path-dependence (meaning that history matters) the authors analyze data distributions of measures concerning city-centered EESs and also nation-wide entrepreneurial aggregates, searching signs of long-tail statistic, an indicator of path dependence. Furthermore the authors statistically analyze if and to what extend, key university performance indices translate into successful EESs. From these results, recommendation follow. Findings Historical developments and cultural backgrounds of different countries lead to different ways of support for new ideas and approaches. Entrepreneurship success is path-dependent and there are entrepreneurial approaches more easy – but others way more difficult – to imitate (e.g. “Hidden Champions”). Statistical analysis of entrepreneurial performance data confirms power law decay, which confirms the action of path-depend processes. Statistical models show that university performance is just weakly influencing entrepreneurial success. This leads to proposing a new, polycentric approach for organizing robust cooperation between the stakeholders, potentially capable of lifting low-score EESs into a more productive regime. Research limitations/implications Research limitation primarily concern data availability, as potentially useful information is not publicly available. The findings lead to further questions, and to questioning too optimistic expectations about cooperation readiness of the implied triple helix stakeholders. As pointed out repeatedly, other important players are non-university research organizations with more focused goals (national labs, etc.), which should be explicitly accounted for. Another directions is distinguishing between support for (a) short-term high-tech, and for (b) longer term, deep-tech entrepreneurs. The latter may require new evaluation techniques and specially tailored private-public partnerships. Finally, polycentric and polycentric cross-border cooperation requires more research into networked organizations. Practical implications Triple-helix based, cooperative agreements should rethink the effective functionality of EESs. City-centered, EESs tend to be most expressive in their projects but also subject to a stronger long tail-effect. This implies “success-breads-success” for the front-runners, enjoying very low ranking mobility. Lower-positioned EESs may profit from the mobility within this score region. However, the still lower positioned ones face strong downward rank mobility, implying that special effort (support) is needed. The authors’ proposal of polycentric cooperation, especially the cross-border variant, would credibly enable such support by bringing in both, more players and more levels of play, helping robust networking. Social implications This research has potentially profound social implications. Acknowledging the path-dependence of intellectual capital formation and emergence of entrepreneurial spirit in the modern sense helps to understand which techno-entrepreneurial endeavors would have increased chance of success of being brought up in the respective EES. It is also useful to identify the oversold (over-hyped) entrepreneurial projects. Concerning the cross-border polycentric organizations harboring EESs one may have to concede that national borders are sub-optimal, at least with regard to lifting up the entrepreneurial reputation of border-adjacent regions. Implicitly, opportunistic and marginal entrepreneurship (e.g. in Eastern Europe) would reduce. Originality/value This research claims originality for combining a qualitative-historical approach with a statistical modeling approach, which can explain the dynamic nature of the involved entrepreneurial processes (long-tails). While the historical approach is deemed to be necessary for understanding the reasons for the large differences in entrepreneurial success across nations and regions, the statistical modeling is helping to identify lower-score intervals within the entrepreneurial score hierarchy which are more easy, or, alternatively, very hard to escape. This in turn leads to adapted recommendations and mobilizing special efforts for creating new, potentially robust, cooperative arrangements beyond the triple-helix concept.

Assessment of lake Markakol’s physical and chemical condition with consideration of eutrofication

The study was conducted in 2023 and comprised a comparative assessment of the state of Lake Markakol’s aquatic ecosystems in surface and bottom water strata. The analysis of bottom water temperatures showed correlation between this parameter and dissolved oxygen concentrations, indicating that pollution caused by organic impurities leads to lake eutrophication, in turn, pro-pelling the extinction of aquatic life. Although the main water physicochemical parameters of Markakol Lake correspond to the oligotrophic type, the shifts in dissolved oxygen and phos-phate content, as well as growth of aquatic vegetation indicate its transition to the mesotrophic type. Higher phosphate content in water is a consequence of pollution disturbing the biological balance, as well as stimulating the reservoir’s eutrophication and increased biological productivity, i.e. algal bloom. In addition, phosphate ions serve an informative indicator of Hazard Class 3 (organoleptically hazardous) anthropogenic pollution. Based on the correlation factor (r), two statistical models were considered for the target lake: 1) Water Pollution: nutri-ent concentrations depending on surface and bottom water temperatures (Model 1); and 2) changes in Water Pollution Indices (biogenic, heavy metals, mean) because of air temperature growth due to global warming of 0.25°C/decade (Model 2).

Transferability and comparability of sewer deterioration models – a case study on Norwegian sewer data

ABSTRACT The adoption of emerging machine learning (ML) techniques in sewer deterioration modelling remains limited. This is primarily due to a lack of comparisons with popular models to evaluate if they perform superiorly and the extensive datasets required for ML training, which many small- to medium-sized municipalities do not possess. This study compares an emerging ML technique, random survival forest (RSF), with two established models: support vector machine (SVM) and GompitZ. Additionally, it examines the transferability of RSF models to different municipalities. The results indicate that both RSF and GompitZ have unique strengths and weaknesses, suggesting parallel use with statistical models. RSF’s performance was comparable to, or better than, SVM. Furthermore, globally trained RSF models, which use aggregated datasets of multiple municipalities, perform similarly to locally trained models, which are trained on representative municipal data. However, globally trained models are promising for municipalities lacking data or expertise to develop their own models.

Optimising bearing degradation prediction: a bayesian vector autoregressive-assisted DeepAR model approach

Abstract Accurate prediction of the remaining useful life of bearings is crucial for the maintenance of rotating machinery. Current research often involves extracting multidimensional features from vibration signals to construct health degradation indicator, and health indicator is directly used for degradation prediction. However, constructed health indicator exhibit complex nonlinearity and fail to utilize the correlations with the original multidimensional features. To address this limitation, we propose an integrated prediction framework, known as the bayesian vector autoregression-assisted DeepAR method. The proposed two step prediction framework combines the advantages of covariance prediction and temporal correlation prediction, and realizes the full utilization of multidimensional features correlation information. Additionally, the proposed approach utilizes the intrinsic properties of statistical models to constrain improve the prediction results of deep learning methods, achieving nonlinear modeling of the degradation indicator while maintaining the stability of the prediction results. This process of prediction, re-estimation and adaptive adjustment achieves a balance in model performance. Finally, experimental studies using the PRONOSTIA and XJTU-SY datasets validate the effectiveness of the proposed method in improving the prediction accuracy of bearing health degradation indicators and enhancing the stability of multistep prediction in deep model.

Genetic connectivity of wolverines in western North America.

Wolverine distribution contracted along the southern periphery of its range in North America during the 19th and 20th centuries due primarily to human influences. This history, along with low densities, sensitivity to climate change, and concerns about connectivity among fragmented habitats spurred the recent US federal listing of threatened status and special concern status in Canada. To help inform large scale landscape connectivity, we collected 882 genetic samples genotyped at 19 microsatellite loci. We employed multiple statistical models to assess the landscape factors (terrain complexity, human disturbance, forest configuration, and climate) associated with wolverine genetic connectivity across 2.2million km2of southwestern Canada and the northwestern contiguous United States. Genetic similarity (positive spatial autocorrelation) of wolverines was detected up to 555km and a high-to-low gradient of genetic diversity occurred from north-to-south. Landscape genetics analyses confirmed that wolverine genetic connectivity has been negatively influenced by human disturbance at broad scales and positively influenced by forest cover and snow persistence at fine- and broad-scales, respectively. This information applied across large landscapes can be used to guide management actions with the goal of maintaining or restoring population connectivity.

Clinical measures in chronic neuropathic pain are related to the Kennedy and endocannabinoid pathways.

Chronic neuropathic pain (CNP) is a debilitating condition, often refractory to currently available drugs. Understanding biochemical alterations in peripheral tissues such as blood will be useful for understanding underlying pathophysiological processes relating to CNP. We collected blood from two independent cohorts of CNP and pain-free controls (CNP n = 129/Controls n = 127) in the UK and Ireland to investigate the relationship between CNP-associated molecular/biochemical alterations and a range of clinical and pain metric parameters. Multiple statistical comparisons were conducted on the data, with selected variables included in one or more of the intended inferential analyses (six models). Gene expression analysis showed that choline phosphotransferase (CHPT1) was increased (p < .001) in the CNP group compared to controls. The levels of phosphatidylcholine, a metabolite of CHPT1 in the Kennedy Pathway, were significantly (p = .008) decreased in the plasma of patients with CNP. Given the relationship between the Kennedy pathway and endocannabinoids, plasma endocannabinoids and related N-acylethanolamines were quantified in clinical samples by HPLC-Tandem Mass Spectrometry. Plasma levels of the endocannabinoid 2-arachidonoylglycerol were higher in CNP samples compared to controls, and in the statistical models applied, 2-arachidonoylglycerol significantly increased the odds of CNP (p < .001). The expression of genes related to the synthesis and catabolism of endocannabinoids also corroborated the increased plasma 2-arachidonoylglycerol levels in patients with CNP. Endocannabinoid levels, expression of genes related to endocannabinoid metabolism, age, sex, depression and anxiety state together were strong predictors of CNP. The observed molecular changes indicate that lipid metabolism is altered in CNP and thus may represent a viable target for novel analgesics or biomarker development.

Addressing statistical challenges in the analysis of proteomics data with extremely small sample size: a simulation study.

One of the most promising approaches for early and more precise disease prediction and diagnosis is through the inclusion of proteomics data augmented with clinical data. Clinical proteomics data is often characterized by its high dimensionality and extremely limited sample size, posing a significant challenge when employing machine learning techniques for extracting only the most relevant information. Although there is a wide array of statistical techniques and numerous analysis pipelines employed in proteomics data analysis, it is unclear which of these methods produce the most efficient, reproducible, and clinically meaningful results. In this study, we compared 9 unique analysis schemes comprised of different machine learning and dimensionality reduction methods for the analysis of simulated proteomics data consisting of 1317 proteins measured in 26 subjects (i.e., 13 controls and 13 cases). In scenarios where the sample size is extremely small (i.e., n < 30), all schemes resulted in an exceptionally high level of performance metrics, indicating potential overfitting. While performance metrics did not exhibit significant differences across schemes, the set of proteins selected to be discriminatory between groups demonstrated a substantial level of heterogeneity. However, despite heterogeneity in the selected proteins, their biological pathways and genetic diseases exhibited similarities. A sensitivity analysis conducted using varying sample sizes indicated that the stability of a set of selected biomarkers improves with larger sample sizes within a scheme. When the aim of the study is to identify a statistical model that best distinguishes between cohort groups using proteomics data and to uncover the biological pathways and disorders common among the selected proteins, the majority of widely used analysis pipelines perform similarly. However, if the main objective is to pinpoint a set of selected proteins that wield significant influence in discriminating cohort groups and utilize them for subsequent investigations, meticulous consideration is necessary when opting for statistical models, due to the possibility of heterogeneity in the sets of selected proteins.

Diagnostic power of serum creatinine/cystatin C ratio for identifying low MRI-muscle volume and low grip strength: Data from 9,731 to 149,707 UK Biobank older adults.

Biomarkers for sarcopenia are lacking. We examined the diagnostic power of serum creatinine to cystatin C (Cr:Cyc) ratio for identifying low MRI-muscle volume and low grip strength in a large observational study of UK Biobank older adults. Serum creatinine and cystatin C were measured via immunoassays (Beckman Coulter AU5800 and Siemens Advia 1800, respectively) and grip strength by hydraulic hand dynamometer at baseline visit (2008-2010). MRI-thigh fat-free muscle volume (FFMV) and DXA-derived appendicular lean mass were measured at imaging visit (2014-2018). Extreme outliers were removed, and covariates (demographic, lifestyle, and clinical factors, as well as time elapsed between baseline-imaging visit) were adjusted for in statistical models. 12,873 older adults (mean age: 63.5 ± 2.7 years, 44.2% women) were included for FFMV and ALM/BMI; 149,707 older adults (mean age: 64.0 ± 2.9 years, 50.5% women) for grip strength. Despite significant associations (p<0.05), in fully-adjusted models, Cr:Cyc showed poor to acceptable diagnostic power for identifying low FFMV when using cutpoints of 20th percentile (AUC: 0.577 men; 0.622 women) and T scores of -2 (AUC: 0.596 men; 0.659 women) and -2.5 (AUC: 0.609 men; 0.722 women). In fully-adjusted model, Cr:Cyc showed poor diagnostic power (AUCs: <0.70) for identifying low ALM/BMI or low grip strength irrespective of the cutpoint used. Cr:Cyc may not be a suitable biomarker for identifying low muscle volume or low strength in older adults. This finding, drawn from a large sample size and the use of advanced medical imaging, marks an important contribution to the sarcopenia field.

On Leveraging Machine Learning in Sport Science in the Hypothetico-deductive Framework.

Supervised machine learning (ML) offers an exciting suite of algorithms that could benefit research in sport science. In principle, supervised ML approaches were designed for pure prediction, as opposed to explanation, leading to a rise in powerful, but opaque, algorithms. Recently, two subdomains of ML-explainable ML, which allows us to "peek into the black box," and interpretable ML, which encourages using algorithms that are inherently interpretable-have grown in popularity. The increased transparency of these powerful ML algorithms may provide considerable support for the hypothetico-deductive framework, in which hypotheses are generated from prior beliefs and theory, and are assessed against data collected specifically to test that hypothesis. However, this paper shows why ML algorithms are fundamentally different from statistical methods, even when using explainable or interpretable approaches. Translating potential insights from supervised ML algorithms, while in many cases seemingly straightforward, can have unanticipated challenges. While supervised ML cannot be used to replace statistical methods, we propose ways in which the sport sciences community can take advantage of supervised ML in the hypothetico-deductive framework. In this manuscript we argue that supervised machine learning can and should augment our exploratory investigations in sport science, but that leveraging potential insights from supervised ML algorithms should be undertaken with caution. We justify our position through a careful examination of supervised machine learning, and provide a useful analogy to help elucidate our findings. Three case studies are provided to demonstrate how supervised machine learning can be integrated into exploratory analysis. Supervised machine learning should be integrated into the scientific workflow with requisite caution. The approaches described in this paper provide ways to safely leverage the strengths of machine learning-like the flexibility ML algorithms can provide for fitting complex patterns-while avoiding potential pitfalls-at best, like wasted effort and money, and at worst, like misguided clinical recommendations-that may arise when trying to integrate findings from ML algorithms into domain knowledge. KEY POINTS: Some supervised machine learning algorithms and statistical models are used to solve the same problem, y = f(x) + ε, but differ fundamentally in motivation and approach. The hypothetico-deductive framework-in which hypotheses are generated from prior beliefs and theory, and are assessed against data collected specifically to test that hypothesis-is one of the core frameworks comprising the scientific method. In the hypothetico-deductive framework, supervised machine learning can be used in an exploratory capacity. However, it cannot replace the use of statistical methods, even as explainable and interpretable machine learning methods become increasingly popular. Improper use of supervised machine learning in the hypothetico-deductive framework is tantamount to p-value hacking in statistical methods.

DOES SHAPE SYMMETRY EXIST IN THE LUNATE BONE? TOWARDS DEVELOPMENT OF A PATIENT-SPECIFIC LUNATE IMPLANT

IntroductionKienböck's disease is generally defined as the collapse of the lunate bone, and this may lead to early wrist osteoarthritis. Replacing the collapsed lunate with an implant has regained renewed interest with the advancing technology of additive manufacturing, enabling the design of patient-specific implants. The aims of this project are (1) to determine how accurate it is to use the contralateral lunate shape as a template for patient-specific lunate implants, and (2) to study the effects of shape variations wrist kinematics using 4D-computed tomography (CT) scanning.MethodsA 3D statistical shape model (SSM) of the lunate was built based on bilateral CT scans of 54 individuals. Using SMM, shape variations of the lunate were identified and the intra- and inter-subject shape variations were compared by performing an intraclass correlation analysis. A radiolucent motor-controlled wrist-holder was designed to guide flexion/extension and radial/ulnar deviation of ex vivo wrist specimens under 4D-CT scanning. In this pilot, three shape mode variations were tested per specimen in two specimens were. After post-processing each CT, the scapholunate angle (SLA) and capitolunate angle (CLA) were measured.ResultsThe shape of the lunate was not symmetrical, defined as exceeding the intra-subject variation in five different shape modes. The FE tests show a generalized increase in scapholunate and capitolunate angle when using lunate implants, and comparing variation of shape modes showed that shape mode 3 has a significant effect on the measured angles (p&lt;0.05).DiscussionThe design of patient-specific lunate implants may prove to be challenging using a ‘mirror’-design as it will lead to a degree of shape asymmetry. The pilot study, to determine the effects of those shape variations on wrist kinematics suggest that the degree of shape variation observed indeed may alter the wrist kinematics, although this needs to be further investigated in study using more specimens.

Delineation of Groundwater Potential Using the Bivariate Statistical Models and Hybridized Multi-Criteria Decision-Making Models

Identifying groundwater potential zones in a basin and developing a sustainable management plan is becoming more important, especially where surface water is scarce. The main aim of the study is to prepare the groundwater potential maps (GWPMs) considering the bivariate statistical models of frequency ratio (FR), weight of evidence (WoE), and the multi-criteria decision-making (MCDM) model of Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) hybridized with FR and WoE. Two distance measures, Euclidean and Manhattan, were used in TOPSIS to evaluate their effect on GWPMs. The research focused on the Burdur Lake catchment located in the southwest of Türkiye. In total, 74 wells with high yields were chosen randomly for the analysis, 52 (70%) for training, and 22 (30%) for testing processes. Sixteen groundwater conditioning factors were selected. The area under the receiver operating characteristic (AUROC) and true skill statistics (TSS) were utilized to examine the goodness-of-fit and prediction accuracy of approaches. The TOPSIS-WoE-Manhattan model and the FR and WoE models gave the best AUROC values of 0.915 and 0.944 for the training and testing processes, respectively. The best TSS values of 0.827 and 0.864 were obtained by the TOPSIS-FR-Euclidean and WoE models for the training and testing processes, respectively.

Structure-mutagenicity relationships on quinoline and indole analogues in the Ames test.

Although the in silico predictive ability of the Ames test results has recently made remarkable progress, there are still some chemical classes for which the predictive ability is not yet sufficient due to a lack of Ames test data. These classes include simple heterocyclic compounds. This study aimed to investigate the mutagenicity and structure-mutagenicity relationships for some heterocycles in the Ames test. In the present study, we selected 12 quinoline analogues containing one or two nitrogen atoms in the naphthalene ring and 12 indole analogues containing one to three nitrogen atoms in the indole ring, without any side moiety. The Ames test was performed with five standard bacterial strains (TA100, TA1535, TA98, TA1537, and WP2uvrA) using the pre-incubation method with and without rat liver S9. Five quinoline and two indole analogues were mutagenic. Among the five quinoline analogues, four were mutagenic in the presence of S9 mix with TA100, whereas cinnoline was mutagenic in the absence of S9 mix with TA1537. Among the two indole analogues, indazole was mutagenic in the presence and absence of S9 mix with WP2uvrA and 4-azaindole was mutagenic in the absence of S9 mix with TA1537. The mechanisms underlying the induction of mutagenesis appear to differ between quinoline and indole analogues. In addition, we performed in silico analysis of the mutagenicity of all these analogues using DEREK Nexus 6.1.1 (Lhasa Limited) and GT_EXPERT from CASE Ultra 1.8.0.5 (MultiCASE Inc.) as knowledge-based models and GT1_BMUT from CASE Ultra 1.8.0.5 (MultiCASE Inc.) as a statistical-based model. The knowledge-based model showed low sensitivity for both the quinoline and indole analogues (DEREK Nexus and GT_EXPERT: 20% for quinolines and 0% for indoles). Conversely, the statistical model showed high sensitivity (100% for both quinolines and indoles) and low specificity (43% for quinolines and 10% for indoles). Based on the Ames test results, we proposed structural alerts noting that quinoline analogues were mutagenic when they had nitrogens in any of the positions 2, 5, 7, or 8 in addition to 1, and indole analogues were mutagenic when they had nitrogens at positions 2 or 4 in addition to 1.

Modeling the dynamics of addiction relapse via the double-well potential system.

Substance use relapse is difficult to define, and previous work has used one-size-fits-all ad hoc definitions. Researchers have called for a dynamic and personalized understanding of relapse as a concept and model, necessitating novel statistical tools. We aimed to develop and validate a novel statistical model of latent relapse processes: the double-well potential model (DWPM). This model describes posttreatment substance use in terms of a dynamical system with stable equilibria of abstinence and relapse, person-specific dominant equilibria (tilt), the ease of changing between equilibria (steepness), and an overall relapse risk (RR). Using timeline follow-back data from N = 139 adults with a substance use disorder transitioning back to the community after residential treatment, we examined individual differences and the criterion-related validity of DWPM parameters to determine the clinical utility of the double-well model. While nonuse was the predominant stable state across participants, we found significant between-subjects variability steepness and RR. These individual differences were predictable via demographics, baseline psychopathology, treatment history, and treatment condition. Steepness and RR also predicted long-term outcomes, including life satisfaction and criminal behavior, above and beyond traditional metrics of relapse (proportion of days used and time to first use). Thus, the DWPM is a strong theoretical and statistical representation of the underlying relapse processes. Moreover, the parameters show criterion-related validity and may be useful in precision medicine. (PsycInfo Database Record (c) 2024 APA, all rights reserved).