Variation Distance Research Articles

Deep phenotypic profiling of neuroactive drugs in larval zebrafish

AbstractBehavioral larval zebrafish screens leverage a high-throughput small molecule discovery format to find neuroactive molecules relevant to mammalian physiology. We screen a library of 650 central nervous system active compounds in high replicate to train deep metric learning models on zebrafish behavioral profiles. The machine learning initially exploited subtle artifacts in the phenotypic screen, necessitating a complete experimental re-run with rigorous physical well-wise randomization. These large matched phenotypic screening datasets (initial and well-randomized) provide a unique opportunity to quantify and understand shortcut learning in a full-scale, real-world drug discovery dataset. The final deep metric learning model substantially outperforms correlation distance–the canonical way of computing distances between profiles–and generalizes to an orthogonal dataset of diverse drug-like compounds. We validate predictions by prospective in vitro radio-ligand binding assays against human protein targets, achieving a hit rate of 58% despite crossing species and chemical scaffold boundaries. These neuroactive compounds exhibit diverse chemical scaffolds, demonstrating that zebrafish phenotypic screens combined with metric learning achieve robust scaffold hopping capabilities.

Identifying time patterns of highland and lowland air temperature trends in Italy and the UK across monthly and annual scales

Abstract. This paper presents a statistical analysis of air temperature data from 32 stations in Italy and the UK up to 2000 m above sea level from 2002 to 2021. The data came from both highland and lowland areas in order to evaluate the differences due to both location and elevation. The analysis focused on detecting trends at annual and monthly timescales, employing ordinary least-squares (OLS), robust S-estimator regression, and Mann–Kendall (MK) and Sen's slope methods. Hierarchical clustering (HCA) using dynamic time warping (DTW) was then applied to the monthly data to analyze the intra-annual pattern similarity of trends within and across the groups. Two different regions of Europe were chosen because of the different climate and temperature trends – namely, the northern UK (smaller trends) and the northwest Italian Alps (larger trends). The main novelty of the work is to show that stations with similar locations and altitudes have similar monthly slopes by quantifying them using DTW and clustering. These results reveal the nonrandomness of different trends throughout the year and between different parts of Europe, with a modest influence of altitude in wintertime. The findings revealed that group average trends were close to the National Oceanic and Atmospheric Administration (NOAA) values for the areas in Italy and the UK, confirming the validity of analyzing a small number of stations. More interestingly, intra-annual patterns were detected commonly at the stations of each of the groups and are clearly different between them. Confirming the different climates, most highland and lowland stations in Italy exhibit statistically significant positive trends, while in the UK, both highland and lowland stations show statistically nonsignificant negative trends. Hierarchical clustering in combination with DTW showed consistent similarity between monthly patterns of means and trends within the group of stations and inconsistent similarity between patterns across groups. The use of the 12 distance correlation matrices (dcor) (one for each month) also contributes to what is the main result of the paper, which is to clearly show the different temporal patterns in relation to location and (in some months) altitude. The anomalous behaviors detected at 3 of the 32 stations, namely Valpelline, Fossano, and Aonoch Mòr, can be attributed, respectively, to the facts that Valpelline is the lowest-elevation station in its group; Fossano is the southernmost of the Italian stations, with some sublittoral influence; and Aonoch Mòr has a large number of missing values. In conclusion, these results improve our understanding of temperature spatio-temporal dynamics in two very different regions of Europe and emphasize the importance of consistent analysis of data to assess the ongoing effects of climate change. The intra-annual time patterns of temperature trends could also be compared with climate model results.

A data representation method using distance correlation

A data representation method using distance correlation

SNOOKER: a dataset generator for helpdesk services

AbstractThe availability of suitable datasets and data generators is crucial for developing intelligent systems, especially in helpdesk services. However, the lack of publicly accessible data generators focused on helpdesk operations, where incidents are often closed without detailing the treatment procedures, poses challenges to implementing intelligent systems such as recommender systems. To address this issue, a dataset generator can be employed to simulate helpdesk incidents. This paper introduces SNOOKER (dataSet geNeratOr fOr helpdesK sERvices), a customizable dataset generator designed to create and treat helpdesk tickets, including domain-specific incidents (e.g., cybersecurity) by orchestrating simulated actions and multiple IT teams. SNOOKER’s output is compared against a real anonymized dataset from S21Sec Cyber Solutions by Thales. The datasets are evaluated using Kolmogorov-Smirnov, Kullback-Leibler Divergence, and Hellinger distance tests, with results indicating similar distributions. For example, the first metric returned a low K-S value and a p-value exceeding 5%, while the second and third measures presented 0.003 and 0.03, respectively. Furthermore, experiments with different team configurations revealed that ticket scheduling highly depends on each team’s operators’ numbers and work shifts, increasing with unbalanced shifts and fewer operators.

Correlation functions of the six-vertex IRF model and its quantum spin chain

We consider the interaction-round-a-face version of the isotropic six-vertex model. The associated spin chain is made of two coupled Heisenberg spin chains with different boundary twists. The phase diagram of the model and the long distance correlations were studied in [Nucl. Phys. B, 995 (2023) 116333]. Here, we compute the short-distance correlation functions of the model in the ground state for finite system sizes via non-linear integral equations and in the thermodynamic limit. This was possible since the model satisfies the face version of the discrete quantum Knizhnik-Zamolodchikov (qKZ) equation. A suitable ansatz for the density matrix is proposed in the form of a direct sum of two Heisenberg density matrices, which allows us to obtain the discrete functional equation for the two-site function ω(λ1,λ2). Thanks to the known results on the factorization of correlation functions of the Heisenberg chain, we are able to compute the density matrix of the IRF model for up to four sites and its associated spin chain for up to three sites.

Automated Posterior Scleral Topography Assessment for Enhanced Staphyloma Visualization and Quantification With Improved Maculopathy Correlation.

To quantitatively characterize the posterior morphology of high myopia eyes with posterior staphyloma. Surface points of the eyeball were automatically extracted from magnetic resonance imaging scans using deep learning. Subsequently, the topography of posterior staphylomas was constructed to facilitate accurate visualization and quantification of their location and severity. In the three-dimensional Cartesian coordinate system established with surface points, measurements of distances (D) from each point to the hypothetical pre-elongation eye center within the eyeball and local curvatures (C) at each point on the posterior sclera were computed. Using this data, specific parameters were formulated. The concordance of these parameters with traditional staphyloma classification methods and their association with myopic traction maculopathy (MTM) grades based on the ATN classifications were investigated. The study included 102 eyes from 52 participants. The measured parameters, particularly the variance of distance (Dvar) and the maximum value of the curvature and distance product (C · Dmax), demonstrated efficacy in differentiating various types of posterior staphyloma and exhibited strong correlations with the grades of MTM. The automated generation of the posterior scleral topography facilitated visualization and quantification of staphyloma location and severity. Simple geometric parameters can quantify staphyloma shape and correlate well with retinal complications. Future works on expanding these measures to more imaging modalities could improve their clinical use and deepen insights into the link between posterior staphyloma and related retinal diseases. This work has the potential to be translated into clinical practice, allowing for the accurate assessment of staphyloma severity and ultimately improving disease management.

Smoothness and Lévy concentration function inequalities for distributions of random diagonal sums

We present new explicit upper bounds for the smoothness of the distribution of the random diagonal sum S n = ∑ j = 1 n X j , π ( j ) S_n=\sum _{j=1}^nX_{j,\pi (j)} of a random n × n n\times n matrix X = ( X j , r ) X=(X_{j,r}) , where X j , r X_{j,r} are independent integer valued random variables, and π \pi denotes a uniformly distributed random permutation on { 1 , … , n } \{1,\dots ,n\} independent of X X . As a measure of smoothness, we consider the total variation distance between the distributions of S n S_n and 1 + S n 1+S_n . Our approach uses new auxiliary inequalities for a generalized normalized matrix hafnian and for inverse moments of non-negative random variables, which could be of independent interest. This approach is also used to prove upper bounds of the Lévy concentration function of S n S_n in the case of independent real valued random variables X j , r X_{j,r} .

Non-uniform Bounds and Edgeworth Expansions in Self-normalized Limit Theorems

We study Edgeworth expansions in limit theorems for self-normalized sums. Non-uniform bounds for expansions in the central limit theorem are established while imposing only minimal moment conditions. Within this result, we address the case of non-integer moments leading to a reduced remainder. Furthermore, we provide non-uniform bounds for expansions in local limit theorems. The enhanced tail accuracy of our non-uniform bounds allows for deriving an Edgeworth-type expansion in the entropic central limit theorem as well as a central limit theorem in total variation distance for self-normalized sums.

Skeletal and respiratory muscle weakness contributes to decreased exercise capacity in hypertrophic cardiomyopathy

Abstract Background and Aims Although decreased exercise capacity (due to dyspnea or angina-like symptoms) constitutes the fundamental characteristics of hypertrophic cardiomyopathy (HCM), the pathophysiology of these symptoms remains only partly understood. We investigated clinical and functional determinants of decreased exercise capacity in a broad range of HCM patients irrespective of primary disease aetiology (either genetic disorder or e.g. infiltrative phenocopies in the course of cardiac amyloidosis), overt heart failure (HF) and the presence of haemodynamically significant left ventricular outflow tract obstruction (LVOTO). Further, we deterimined the contribution of skeletal and respiratory muscle weakness to decresed exercise capacity. Methods We analyzed clinical data and functional assessments (skeletal and respiratory muscle strength, physical fitness) in relation to symptomatology and sub-maximal exercise capacity in 82 clinically stable patients with HCM (females: 32 %; age: 62±16 years; left ventricular ejection fraction [LVEF] 59±7%; intraventricular septum wall thickness [IVS] 19±4 mm; median plasma N-terminal pro-B-type natriuretic peptide [NT-proBNP]: 818 pg/mL [lower and upper quartile: 208-1917]; New York Heart Association [NYHA] class 0/I/II/III: 38/18/33/11 % [0 indicates no HF]; significant LVOTO: 29%). Results HCM patients with more severe symptomatology (NYHA class II-III vs 0-I) were older, had more frequently significant LVOTO and greater comorbidity burden, and had higher NT-proBNP and worse estimated renal function (all p≤0.03). Gender distribution, percentage of beta-blocker therapy and implanted device, body mass index, major echocardiography parameters (including LVEF, left ventricular end-diastolic diameter and IVS), high-sensitive (hs) cardiac troponin type I and hs-C-reactive protein were comparable in these 2 groups of subjects (all p>0.05). In multivariable linear regression models more advanced age, higher circulating natriuretic peptides (NT-proBNP) and previous coronary revascularization were independent predictors of more advanced NYHA class (standardized β-coefficient=0.30, p<0.01; β=0.27, p<0.01; and β=0.22, p<0.05; respectively). Similarly, more advanced age, female sex and higher NT-proBNP were independent correlates of shorter distance in 6-minute walking test (6MWT) (β=-0.46, p<0.001; β=-0.26, p<0.001; and β=-0.36, p<0.001; respectively). All analyzed muscle parameters (including respiratory muscle strength parameters) and objective measures of physical fitness (Functional Fitness Test for older adults) remained independently associated with 6MWT distance (all p<0.001) when adjusted for aforementioned clinical and laboratory correlates of 6MWT. Conclusions Muscle weakness and low physical fitness are related to decreased exercise capacity in HCM and may be potential therapeutic targets to alleviate disease symptoms also in non-obstructive HCM phenotype.

Insights into the Thermal Expansion of Amorphous Polymers.

We investigated the thermal expansion of amorphous polystyrene (PS) and poly(methyl methacrylate) (PMMA) homopolymers using the temperature dependence of spatial electron-density correlations. The strong and broad X-ray interference maxima arising from inter- and intramolecular correlation distances were distinct, maintaining their shape during the heating of the samples to 250 °C. Three maxima characteristic of electron density correlations between the backbones, substituents along the chain, and repeat-units were observed. A remarkable temperature dependence was identified in the largest correlation distances arising from the intermolecular interactions. Based on the temperature dependence of the correlation distances, the coefficients of molecular expansion were very close to the coefficients of thermal expansion. This study provides a simple yet accurate way to correlate macroscopic volume expansions with the molecular expansion obtained from wide-angle X-ray scattering (WAXS) data.

Multiple moving object classification and tracking using DenCNN classifier

A Multiple moving object detection, tracking, and counting algorithm is mainly designed exclusively suitable for congested areas. The counting system can alleviate the betrayal performance in the crowded areas. Most of the existing methods developed for tracking and counting face serious challenges in detection due to high densities of the target. This condition urged the researchers to update the existing systems. The present methodology was designed to address such issues. In the present methodology, the contrast was initially enhanced between the objects and their backgrounds using a Double Plateau Histogram Equalization (DPHE). Then, the motion was estimated for the contrast-enhanced image to identify the moment of the object using the modified Adaptive Distance Covariance Rood Pattern Search (ADCRPS) algorithm. After that, the morphological operation was deployed to sharpen the images by removing all the unwanted things. Then, the features were extracted and important features were selected using the modified Chaotic Tent Shuffled Shepherd Optimization (CTSSO) Algorithm. With the selected features object, detection was done using the proposed Scaled Non-Monotonic Cauchy Dense Convolutional Neural Network (SNMC-DenCNN). The detected object was then tracked with the aid of Channel and Spatial Reliability Tracker (CSRT). Finally, the objects were counted by intersection over union (IOU) by explicitly computing the association between detected and tracked objects. Also, the experimental results showed the effectiveness and efficiency of the proposed system with enhanced accuracy.

CRITID: enhancing CRITIC with advanced independence testing for robust multi-criteria decision-making

In multi-criteria decision-making and model evaluation, determining the weight of criteria is crucial. With the rapid development of information technology and the advent of the big data era, the need for complex problem analysis and decision-making has intensified. Traditional CRiteria Importance Through Intercriteria Correlation (CRITIC) methods rely on Pearson correlation, which may not adequately address nonlinearity in some scenarios. This study aims to refine the CRITIC method to better accommodate nonlinear relationships and enhance its robustness. We have developed a novel method named CRiteria Importance Through Intercriteria Dependence (CRITID), which leverages cutting-edge independence testing methods such as distance correlation among others. This approach enhances the assessment of intercriteria relationships. Upon application across diverse data distributions, the CRITID method has demonstrated enhanced rationality and robustness relative to the traditional CRITIC method. These improvements significantly benefit multi-criteria decision-making and model evaluation, providing a more accurate and dependable framework for analyzing complex datasets.

Hydrochemical Assessment of Groundwater in Ludhiana and Amritsar Districts of Punjab and Identification of Fluoride Hotspots using GIS

High fluoride concentrations in soil, water, or air can pose serious environmental and health risks to plants, and animals. Along with other hydrochemical parameters, this study investigates fluoride concentrations in the groundwater in the Ludhiana and Amritsar districts of Punjab, India. A total of 222 water samples were uniformly collected at approximately five-kilometer intervals for hydrochemical analyses. Statistical methods such as inverse distance weighting (IDW) and correlation matrices were used to assess the fluoride distribution and its relationships with other parameters. According to WHO guidelines, most fluoride concentrations were below 0.6 ppm in Ludhiana (84.30%) and Amritsar (77.23%). Fluoride levels that were within the permissible range (0.6–1.5 ppm) were found in 15.70% of Ludhiana’s samples and 21.78% of Amritsar’s samples; only 1% of Amritsar’s samples exceeded the permissible limit (>1.5 ppm). The water quality index (WQI) analysis indicated that0.83% of the groundwater samples from the Ludhiana district and 4.95% from the Amritsar district were unfit for consumption. This study demonstrates the importance of standardized sample collection and the use of GIS technology for comprehensive hydrochemical assessments, raising awareness and reducing health risks.

Tangent space functional reconfigurations in individuals at risk for alcohol use disorder

Abstract Human brain function dynamically adjusts to ever-changing stimuli from the external environment. Studies characterizing brain functional reconfiguration are nevertheless scarce. Here we present a principled mathematical framework to quantify brain functional reconfiguration when engaging and disengaging from a stop signal task (SST). We apply tangent space projection (a Riemannian geometry mapping technique) to transform functional connectomes (FCs) of 54 participants and quantify functional reconfiguration using the correlation distance of the resulting tangent-FCs. Our goal was to compare functional reconfigurations in individuals at risk for alcohol use disorder (AUD). We hypothesized that functional reconfigurations when transitioning to/from a task would be influenced by family history of alcohol use disorder (FHA) and other AUD risk factors. Multilinear regression models showed that engaging and disengaging functional reconfiguration were associated with FHA and recent drinking. When engaging in the SST after a rest condition, functional reconfiguration was negatively associated with recent drinking, while functional reconfiguration when disengaging from the SST was negatively associated with FHA. In both models, several other factors contributed to the functional reconfiguration. This study demonstrates that tangent-FCs can characterize task-induced functional reconfiguration, and that it is related to AUD risk.

Circadian rhythms of macrophages are altered by the acidic tumor microenvironment

Tumor-associated macrophages (TAMs) are prime therapeutic targets due to their pro-tumorigenic functions, but varying efficacy of macrophage-targeting therapies highlights our incomplete understanding of how macrophages are regulated within the tumor microenvironment (TME). The circadian clock is a key regulator of macrophage function, but how circadian rhythms of macrophages are influenced by the TME remains unknown. Here, we show that conditions associated with the TME such as polarizing stimuli, acidic pH, and lactate can alter circadian rhythms in macrophages. While cyclic AMP (cAMP) has been reported to play a role in macrophage response to acidic pH, our results indicate pH-driven changes in circadian rhythms are not mediated solely by cAMP signaling. Remarkably, circadian disorder of TAMs was revealed by clock correlation distance analysis. Our data suggest that heterogeneity in circadian rhythms within the TAM population level may underlie this circadian disorder. Finally, we report that circadian regulation of macrophages suppresses tumor growth in a murine model of pancreatic cancer. Our work demonstrates a novel mechanism by which the TME influences macrophage biology through modulation of circadian rhythms.

Analysis of Diurnal Air Temperature Trends and Pattern Similarities in Highland and Lowland Stations of Italy and UK

ABSTRACTIn this study, an analysis of hourly air temperatures in four groups of 32 stations from the UK highland (5 stations), UK lowland (4 stations), Italian highland (11 stations), and Italian lowland (12 stations) at different altitudes was carried out over the period from 2002 to 2021. The study aimed to examine the trends of each hour of the day during this period, over different averaging time windows (10‐day, 30‐day, and 60‐day). The trends were computed using the Mann–Kendall trend test and Sen's slope estimator. The similarity of trends within and across the groups of stations was assessed using the hierarchical clustering with dynamic time warping technique. An additional analysis was conducted to show the correlation of trends among the group of stations using the correlation distance matrix. Hierarchical clustering and distance correlation analysis show trend similarities and correlations, also indicating dissimilarities among different groups. Using 30‐day averages, significant warming trends in specific months at the Italian stations are evident, especially in February, July, August, and December. The UK highland stations did not show statistically significant trends, but clear pattern similarities were found within the groups, especially in certain months. The ultimate goal of this article is to provide insights into temperature dynamics and climate change characteristics on regional and diurnal scales.

Biweight midcorrelation-based CRITIC method: a case study for electrification of BIPV in Iran

Criteria weights are essential in various multi-criteria decision-making (MCDM) approaches. The amount of weight indicates the criterion's importance, and the performance of MCDM techniques depends on the correct estimation of criteria weights and criteria amount in the decision matrix. The distance correlation-based CRiteria Importance Through Inter-criteria Correlation (CRITIC) technique is a modified version of the CRITIC method. Since Pearson's correlation is sensitive to noisy and outlier data, this paper proposes using the biweight midcorrelation as a robust alternative to Pearson's correlation. The weights obtained from the proposed biweight midcorrelation method make using the full range of MCDM techniques possible. This study examined the performance of the proposed method in comparison with eight criteria weighting techniques on the electric supply for a Building-Integrated PhotoVoltaic (BIPV) problem. Results from three different tests (i.e. distance correlation, Spearman rank-order correlation, and symmetric mean absolute percentage error (sMAPE)) indicated that the proposed method could produce more valid criteria weights and ranks than other methods. The value of the sMAPE criterion for the proposed method is 0.1096. Sensitivity analysis also revealed that the proposed method provides more robust criteria weights and ranks with a larger decision matrix.

Stability and failure probability analysis of super-large irregularly shaped deep excavation in coastal area considering spatial variability in soil properties

ABSTRACT The objective of this paper is to investigate the effects of spatial variability in coastal silty soil properties on the stability and failure probability of deep excavation. A super-large irregularly shaped deep excavation in the coastal area in Wenzhou, China, is considered. A numerical model is established based on the random field theory, finite difference method and Monte-Carlo simulation method. Focusing on the friction angle and cohesion, the effects of horizontal and vertical correlation distances and variation coefficients on excavation stability are systematically studied. Meanwhile, the reliability evaluation approach is also applied to systematically examine the impact of the correlation distance on the surface settlement and horizontal displacement of pile. The results show that the greater correlation distance leads to a larger dispersion degree of the ground settlement curve and the displacement curve of the diaphragm wall around. The influence of the horizontal correlation distance on land settlement is more significant. The horizontal and vertical displacements of the pile top are less affected by the correlation distance. Compared with the deterministic analysis, the random analysis results considering the soil spatial variability produce larger displacement.

Electric field effects during disruptions

Tokamak disruptions are associated with breaking magnetic surfaces, which makes magnetic field lines chaotic in large regions of the plasma. The enforcement of quasi-neutrality in a region of chaotic field lines requires an electric potential that has both short and long correlation distances across the magnetic field lines. The short correlation distances produce a Bohm-like diffusion coefficient ∼Te/eB and the long correlation distances aT produce a large scale flow ∼Te/eBaT. This cross-field diffusion and flow are important for sweeping impurities into the core of a disrupting tokamak. The analysis separates the electric field in a plasma into the sum of a divergence-free, E→B, and a curl-free, E→q, part, a Helmholtz decomposition. The divergence-free part of E→ determines the evolution of the magnetic field. The curl-free part enforces quasi-neutrality, E→q=−∇→Φq. Magnetic helicity evolution gives the required boundary condition for a unique Helmholtz decomposition and an unfortunate constraint on steady-state tokamak maintenance.

Characterization of Serum Biochemical Changes in Response to Alcohol Consumption Using FT‐Raman Spectroscopy

ABSTRACTAlcohol is one of the most widely used stimulants globally, with increasing consumption rates annually. Prolonged alcohol consumption leads to significant physiological and neurological changes, potentially resulting in various diseases, which could be detect in serum. Therefore, FT‐Raman spectroscopy was used to measure chemical changes in the serum of heavy drinkers, and these results were compared with the serum composition of a control group of non‐drinkers. Obtained results showed a significant increase in lipids and a decrease in the amide/lipid ratio after alcohol administration. Additionally, higher amide III/amide II and lower amide II/amide I ratios were observed in the serum of alcohol‐addicted patients. These shifts of 892, 966, 1286, 1459 and 2940 cm−1 peaks indicate alterations in the protein‐lipid balance due to alcohol consumption. Principal component analysis (PCA) was employed to further analyze the spectral data, with the first three principal components accounting for 95.36% of total data variability. A Bayesian‐optimized k‐nearest neighbor (BO‐KNN) model was applied for classification. The optimal hyperparameters—two neighbors, correlation distance metric, and squared inverse distance weight—were determined after seven iterations, resulting in a remarkably low classification error of 0.0384. The BO‐KNN model achieved training and test accuracies of 95.92% and 90.48%, respectively. Receiver operating characteristic (ROC) analysis showed an area under the curve (AUC) of 1.00 for the training set and 0.90 for the test set, demonstrating the model's high precision and robustness. In conclusion, FT‐Raman spectroscopy effectively revealed significant chemical changes in the serum of alcohol‐addicted individuals, providing valuable insights into the biochemical impact of alcohol consumption.