Two-sample Test Research Articles

Novel cryotherapy technique for pulpotomy in mature permanent teeth with symptomatic irreversible pulpitis- a randomized controlled trial.

To assess the effect of cryotherapy on haemostasis, post-operative pain, and the outcome of full pulpotomy performed in mature permanent teeth with symptomatic irreversible pulpitis. The study included sixty mature permanent mandibular molar teeth with symptomatic irreversible pulpitis and no periapical rarefaction. After coronal pulp tissue amputation, teeth were randomly allocated to one of two groups (n = 30 each). In group I (conventional pulpotomy), a sterile cotton pellet moistened with 2.5% NaOCl was used for haemostasis. In group II (cryotherapy), the pulp chamber was continuously lavaged with 2.50C normal saline solution for haemostasis using an indigenous portable cryotherapy irrigation unit. Following haemostasis, the pulp was capped with mineral trioxide aggregate and the tooth was restored with resin composite. The time taken to achieve haemostasis was recorded. Preoperative and 24, 48 and 72h postoperative pain was measured using the Numerical Rating Scale. The pulpotomy outcome was assessed at the 12-month follow-up. Data were analyzed using Fischer's exact test, two-sample t-test, two-sample Wilcoxon rank-sum test, Friedman Test, and Wilcoxon Signed Rank Test. The cryotherapy group achieved haemostasis in less time (p < 0.05). There was a significant pain reduction at 24 and 48h in the cryotherapy group when compared with the conventional pulpotomy group (P < 0.005). The overall success rate of pulpotomy after 12 months was 88% (n = 22) in both study groups(p < 0.05). Cryotherapy application reduces postoperative pain and has no adverse effect on the outcome of pulpotomy in permanent teeth with symptomatic irreversible pulpitis. The cryotherapy can be incorporated in pulpotomy protocol as an adjunct to minimize post-operative pain.

An Efficient Spoofing Attack Detection Using Deep Learning Based Physical Layer Security Technique

Spoofing attack detection plays a crucial role in the defence field, involving critical and highly secured data processing. The accurate attack detection mechanism prevents unauthorised access to sensitive information, thereby protecting National security. Physical Layer Security (PLS) is a promising emerging technique that uses the wireless channel’s randomness to secure the communication network. The spoofing attack is one of the severe threats to the wireless network, where the attacker imitates the legitimate user to launch an attack against the network. This paper investigates the channel characteristics-based physical layer technique to detect spoofing attacks. For static radio environments, the two-sample independent hypothesis testing is used to identify the spoofing attack, showing an improvement in detection accuracy of 97 %. The attack detection problem is considered a Reinforcement Learning (RL) based classification problem for a challenging dynamic radio environment. It is simulated using the actor-critic-based Deep Reinforcement Learning (DRL) technique with the help of the Reformed Deep Deterministic Policy Gradient (Re-DDPG) algorithm. The simulated results show that the proposed method performs better than the existing strategies and achieves a Receiver Operating Characteristics (ROC) value of 0.96. The detection accuracy of the proposed method can reach up to 98 %, with precision and recall of about 98 % and 99 %, respectively.

Testing distributional equality for functional random variables

In this article, we present a nonparametric method for the general two-sample problem involving functional random variables modeled as elements of a separable Hilbert space H. First, we present a general recipe based on linear projections to construct a measure of dissimilarity between two probability distributions on H. In particular, we consider a measure based on the energy statistic and present some of its nice theoretical properties. A plug-in estimator of this measure is used as the test statistic to construct a general two-sample test. Large sample distribution of this statistic is derived both under null and alternative hypotheses. However, since the quantiles of the limiting null distribution are analytically intractable, the test is calibrated using the permutation method. We prove the large sample consistency of the resulting permutation test under fairly general assumptions. We also study the efficiency of the proposed test by establishing a new local asymptotic normality result for functional random variables. Using that result, we derive the asymptotic distribution of the permuted test statistic and the asymptotic power of the permutation test under local contiguous alternatives. This establishes that the permutation test is statistically efficient in the Pitman sense. Extensive simulation studies are carried out and a real data set is analyzed to compare the performance of our proposed test with some state-of-the-art methods.

114 Burn Specific VTE Prophylaxis: Higher Enoxaparin Dose and Anti-Xa Adjustments Is Safe and Potentially Beneficial

Abstract Introduction Burn and trauma patients are at high risk of venous thromboembolism (VTE), including deep vein thrombosis (DVT) and pulmonary embolus (PE). VTE prophylaxis guidelines for trauma patients recommends a higher standard dose of enoxaparin and anti-Xa level adjustments in select patients; however, no guidelines specific to burn patients exist. We hypothesize that higher standard doses of enoxaparin and anti-Xa level adjustments will be safe and effective at reducing VTE complications in burn patients. Methods This retrospective review included patients admitted to a regional burn center six months prior to and after the initiation of a VTE prophylaxis quality improvement protocol. The protocol doubled the standard dose of enoxaparin, 40 mg daily to 40 mg twice a day, for all burn patients who were &amp;gt; 50 kg and had creatinine clearance &amp;gt; 60mg/dl. Peak anti-Xa level monitoring was indicated if a high-risk factor (TBSA ≥20%, inhalation injury, burn shock, BMI ≥40) was present. Patients on therapeutic anticoagulation or anti-platelet therapy, had a history of chronic kidney disease, decompensated cirrhosis, coagulation disorder, heparin induced thrombocytopenia (HIT), or were admitted for polytrauma were excluded. Efficacy was evaluated by rates of VTE within 31 days of discharge. Safety was evaluated by bleeding complications, including estimated blood loss (EBL), number of transfusions and gastrointestinal (GI) bleeding events. Differences between groups were compared using a two-sample t-test or Wilcoxon rank-sum test. A p-value of &amp;lt; 0.05 signified statistical significance. Results The pre-protocol group included 217 patients and the post-protocol group included 207 patients. There were no significant differences in age (45 vs 44 years), BMI (27 vs 26.4 kg/m2), TBSA (5.9 vs 5.3%), IHI (4.6 vs 7%), number of procedures (3 vs 3), mortality (4.1 vs 3%), or ICU length of stay (1.9 vs 1.6 days) between groups. No significant difference in EBL (50 vs 25 cc), number of transfusions (3.3 vs 2.4 units), or GI bleeding events (1 vs 1) was noted between groups, and no patient developed thrombocytopenia or HIT. There were 5 patients (2.3%) with a VTE within 31 days of discharge in the pre-protocol group compared to 1 patient (0.4%) in the post-protocol group (p=0.19). There was not enough power to determine statistically significant VTE prevention. Conclusions A lack of VTE prophylaxis recommendations for burn patients leads to variability in protocols among burn centers. These protocols are infrequently studied. This protocol, which doubled the standard dose of enoxaparin and adjusted based on anti-Xa levels for high-risk patients, was safe. Efficacy and applicability to other centers needs further elucidation. Applicability of Research to Practice Enoxaparin 40mg twice a day with anti-Xa level adjustments is safe and a promising strategy to reduce the rates of VTE in the burn population without increasing bleeding complications.

Assessing portfolio diversification via two-sample graph kernel inference. A case study on the influence of ESG screening.

In this work we seek to enhance the frameworks practitioners in asset management and wealth management may adopt to asses how different screening rules may influence the diversification benefits of portfolios. The problem arises naturally in the area of Environmental, Social, and Governance (ESG) based investing practices as practitioners often need to select subsets of the total available assets based on some ESG screening rule. Once a screening rule is identified, one constructs a dynamic portfolio which is usually compared with another dynamic portfolio to check if it satisfies or outperforms the risk and return profile set by the company. Our study proposes a novel method that tackles the problem of comparing diversification benefits of portfolios constructed under different screening rules. Each screening rule produces a sequence of graphs, where the nodes are assets and edges are partial correlations. To compare the diversification benefits of screening rules, we propose to compare the obtained graph sequences. The method proposed is based on a machine learning hypothesis testing framework called the kernel two-sample test whose objective is to determine whether the graphs come from the same distribution. If they come from the same distribution, then the risk and return profiles should be the same. The fact that the sample data points are graphs means that one needs to use graph testing frameworks. The problem is natural for kernel two-sample testing as one can use so-called graph kernels to work with samples of graphs. The null hypothesis of the two-sample graph kernel test is that the graph sequences were generated from the same distribution, while the alternative is that the distributions are different. A failure to reject the null hypothesis would indicate that ESG screening does not affect diversification while rejection would indicate that ESG screening does have an effect. The article describes the graph kernel two-sample testing framework, and further provides a brief overview of different graph kernels. We then demonstrate the power of the graph two-sample testing framework under different realistic scenarios. Finally, the proposed methodology is applied to data within the SnP500 to demonstrate the workflow one can use in asset management to test for structural differences in diversification of portfolios under different ESG screening rules.

A uniform kernel trick for high and infinite-dimensional two-sample problems

We use a suitable version of the so-called ”kernel trick” to devise two-sample tests, especially focussed on high-dimensional and functional data. Our proposal entails a simplification of the practical problem of selecting an appropriate kernel function. Specifically, we apply a uniform variant of the kernel trick which involves the supremum within a class of kernel-based distances. We obtain the asymptotic distribution of the test statistic under the null and alternative hypotheses. The proofs rely on empirical processes theory, combined with the delta method and Hadamard directional differentiability techniques, and functional Karhunen–Loève-type expansions of the underlying processes. This methodology has some advantages over other standard approaches in the literature. We also give some experimental insight into the performance of our proposal compared to other kernel-based approaches (the original proposal by Borgwardt et al. (2006) and some variants based on splitting methods) as well as tests based on energy distances (Rizzo and Székely, 2017).

A novel two-sample test within the space of symmetric positive definite matrix distributions and its application in finance

A novel two-sample test within the space of symmetric positive definite matrix distributions and its application in finance

117 Applying the Competency Index for Clinical Research Professionals (CICRP) for Educational Program Evaluation

OBJECTIVES/GOALS: To demonstrate the value of the Competency Index for Clinical Research Professional (CICRP) as a tool in program evaluation using a pre- and post- design to evaluate student perceived self-efficacy in clinical trial competencies at program entry and at program completion. METHODS/STUDY POPULATION: Using a separate-sample pre-post study design, we administered the CICRP questionnaire to students in the entry and exit courses of the Master of Clinical Research (MCR) Program during the 2021-2022 academic year, using QualtricsTM (Provo, Utah) survey instrument for use on desktop or mobile device. We included the 20 CICRP competency items asking students to rate their self-efficacy in performing each item using a Likert Scale (from 0-10) (0=not at all confident; 10= extremely confident). Links to the survey were included in the courses for the foundational entry course and for the final culminating project course. RESULTS/ANTICIPATED RESULTS: Overall, 54 students took the CICRP during the entry course and 56 during the exit. Cronbach’s alpha for each assessment ranged from 0.93 to 0.98. Both the Welch’s two-sample t-test and Wilcoxon rank-sum test show very significant differences between the group of students entering the program and leaving the program (p value &lt; 0.001). A significant increase in mean CICRP total score is seen at each experience level between program entry and program exit (p&lt;0.001) A linear regression, adjusting for available covariates, individuals taking the exit course have a mean CICRP total score 92.690 (p value &lt; 0.001) higher than individuals taking the entry course. DISCUSSION/SIGNIFICANCE: Competency indices have been used to measure self-efficacy in translational research scientists, thus the use of CICRP to measure self-efficacy can be useful in assessing whether our competency-based program is meeting the JTF Competency needs of students.

On the power of popular two-sample tests applied to precipitation and discharge series

On the power of popular two-sample tests applied to precipitation and discharge series

Two-sample test for stochastic block models via the largest singular value

The stochastic block model is widely used for detecting community structures in network data. However, the research interest in much of the literature focuses on the study of one sample of stochastic block models. Detecting the difference between the two community structures is a less studied issue for stochastic block models. In this article, we propose a novel test statistic based on the largest singular value of a residual matrix obtained by subtracting the geometric mean of two estimated block mean effects from the sum of two observed adjacency matrices. We prove that the null distribution of the proposed test statistic converges in distribution to the Tracy-Widom distribution with index 1, and we show the difference between the two samples for stochastic block models can be tested via the proposed method. We show that the proposed test is asymptotically powerful against alternative models. Further, we extend the proposed method to the degree-corrected stochastic block model. Both simulation studies and real-world data examples indicate that the proposed method works well.

Abstract 982: Variation of cell-free DNA concentrations in liquid biopsies

Abstract Introduction: Liquid biopsies are promising noninvasive tools for cancer detection. Variation in cell-free DNA concentrations (cfDNA-conc) has been observed between individuals with and without cancer. We assessed cfDNA-conc in 2287 treatment-naive individuals including those without cancer, with benign or high-risk conditions, or with one of eight cancer types. Methods: Plasma (0.3 to 9.8 mL) was separated from whole blood collected in Streck or EDTA tubes from individuals with bile duct, breast, colorectal, gastric, ovarian, liver, lung, pancreatic, or metastatic cancer (n=23, 54, 28, 26, 264, 75, 180, 31, 22, respectively) as well as benign or high-risk conditions (n=550, 133, respectively) such as cirrhosis or hepatitis B or C virus infection (HBV/HCV), and without cancer (n=901). cfDNA was extracted using the Qiagen QIAamp Circulating Nucleic Acid Kit. Quality and quantity were assessed using the High Sensitivity DNA assay on the Agilent Bioanalyzer. Total amount (ng) of cfDNA from all nucleosomal peaks was evaluated per volume (mL) of plasma to assess significance across cohorts using unpaired two-sample Wilcoxon tests. Results: We observed that in individuals without cancer, cfDNA-conc increased with age (Pearson R2=0.59; 18-40yrs vs 41-55yrs, vs 56-65yrs, vs 66-85yrs; p=2.31E-09, 1.50E-12, 8.31E-17, respectively). No difference was observed between cfDNA-conc from EDTA and Streck plasma from the same individual (n=9, p=0.359, Wilcoxon signed-rank test two-tailed). Individuals with benign adnexal masses or cirrhosis had similar cfDNA-conc compared to those without cancer while individuals with benign lung lesions or HBV/HCV had significantly different cfDNA-conc (p=0.70, 0.35, 6.96E-04, 1.88E-09, respectively). These observations were not affected by age. Individuals with cancer had significantly higher cfDNA-conc compared to those without cancer (bile duct p=2.08E-14, breast p=4.44E-16, colorectal p=6.04E-10, gastric p=5.93E-04, ovarian p=9.99E-16, liver p=1.22E-15, lung p=0, pancreatic p=8.57E-03, metastatic p=1.18E-06). Ovarian cancer exhibited higher cfDNA-conc than benign adnexal masses which was more pronounced in later stage disease (I/II p=3.83E-06, III/IV p=8.18E-08). Compared to cirrhosis or HBV/HCV we observed a stepwise increase in cfDNA-conc in liver cancer with progressing stage (0/A p= 6.50E-04, B p= 5.69E-10, C p= 1.47E-08). Higher cfDNA-conc were also observed with higher stage of lung cancer compared to benign lung lesions (I/II p=4.99E-03, III/IV p=3.89E-06). Using cfDNA-conc (ng/mL) as a single feature, we distinguished individuals with and without cancer with an AUC of 0.72 (95% CI=0.70-0.75). Conclusions: In a cohort of 2287 individuals, we observed cfDNA-conc increases with age and cancer stage that varied across cancer types. As a classifier, cfDNA-conc can be predictive of cancer status, yet further work is needed to understand the contribution of other comorbidities. Citation Format: Sarah Short, Akshaya V. Annapragada, Jamie E. Medina, Zachariah H. Foda, Dimitrios Mathios, Carolyn Hruban, Elaine J. Chiao, Kavya Boyapati, Adrianna Bartolomucci, Keerti Boyapati, Vilmos Adleff, Robert B. Scharpf, Victor E. Velculescu, Jillian Phallen. Variation of cell-free DNA concentrations in liquid biopsies [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 982.

GTST: A Python Package for Graph Two-Sample Testing

GTST: A Python Package for Graph Two-Sample Testing

ESVD-DE: cohort-wide differential expression in single-cell RNA-seq data using exponential-family embeddings

BackgroundSingle-cell RNA-sequencing (scRNA) datasets are becoming increasingly popular in clinical and cohort studies, but there is a lack of methods to investigate differentially expressed (DE) genes among such datasets with numerous individuals. While numerous methods exist to find DE genes for scRNA data from limited individuals, differential-expression testing for large cohorts of case and control individuals using scRNA data poses unique challenges due to substantial effects of human variation, i.e., individual-level confounding covariates that are difficult to account for in the presence of sparsely-observed genes.ResultsWe develop the eSVD-DE, a matrix factorization that pools information across genes and removes confounding covariate effects, followed by a novel two-sample test in mean expression between case and control individuals. In general, differential testing after dimension reduction yields an inflation of Type-1 errors. However, we overcome this by testing for differences between the case and control individuals’ posterior mean distributions via a hierarchical model. In previously published datasets of various biological systems, eSVD-DE has more accuracy and power compared to other DE methods typically repurposed for analyzing cohort-wide differential expression.ConclusionseSVD-DE proposes a novel and powerful way to test for DE genes among cohorts after performing a dimension reduction. Accurate identification of differential expression on the individual level, instead of the cell level, is important for linking scRNA-seq studies to our understanding of the human population.

A two-sample tree-based test for hierarchically organized genomic signals

Abstract This article addresses a common type of data encountered in genomic studies, where a signal along a linear chromosome exhibits a hierarchical organization. We propose a novel framework to assess the significance of dissimilarities between two sets of genomic matrices obtained from distinct biological conditions. Our approach relies on a data representation based on trees. It utilizes tree distances and an aggregation procedure for tests performed at the level of leaf pairs. Numerical experiments demonstrate its statistical validity and its superior accuracy and power compared to alternatives. The method’s effectiveness is illustrated using real-world data from GWAS and Hi-C data.

Firearm ownership and storage among US college students: results from the healthy minds study, 2021-2022.

The purpose of this study is to address a significant gap in knowledge on firearm ownership rates and storage characteristics in a national sample of college students. We used 2021-2022 survey data from the Healthy Minds Study, which included approximately 88,500 students at over 100 US colleges and universities. We conducted analyses using descriptive statistics and two-sample proportion tests. About 4% of respondents reported having a firearm on or around campus. Among firearm owners, 68.8% reported storing firearms at their permanent address within an hour's drive from campus, and 43.1% reported storing their firearms unloaded and locked. Firearm ownership rates were significantly higher for respondents who were positive for specific risk factors (i.e., in a relationship, suicide ideation, recent binge drinking, and having been physically assaulted) versus those who were negative. These findings support the need for targeted messaging and firearm safety training for college students to reduce firearm-related risks.

Two-sample test of stochastic block models via the maximum sampling entry-wise deviation

Two-sample test of stochastic block models via the maximum sampling entry-wise deviation

Dipsticks and point-of-care Microscopy to reduce antibiotic use in women with an uncomplicated Urinary Tract Infection (MicUTI): protocol of a randomised controlled pilot trial in primary care

IntroductionUncomplicated urinary tract infections (uUTIs) in women are common infections encountered in primary care. Evidence suggests that rapid point-of-care tests (POCTs) to detect bacteria and erythrocytes in urine at presentation...

A Blockwise Bootstrap-Based Two-Sample Test for High-Dimensional Time Series.

We propose a two-sample testing procedure for high-dimensional time series. To obtain the asymptotic distribution of our ℓ∞-type test statistic under the null hypothesis, we establish high-dimensional central limit theorems (HCLTs) for an α-mixing sequence. Specifically, we derive two HCLTs for the maximum of a sum of high-dimensional α-mixing random vectors under the assumptions of bounded finite moments and exponential tails, respectively. The proposed HCLT for α-mixing sequence under bounded finite moments assumption is novel, and in comparison with existing results, we improve the convergence rate of the HCLT under the exponential tails assumption. To compute the critical value, we employ the blockwise bootstrap method. Importantly, our approach does not require the independence of the two samples, making it applicable for detecting change points in high-dimensional time series. Numerical results emphasize the effectiveness and advantages of our method.

Perceived Participation to Kindergartens, Teachers, and Parents of Physical Education in Xi’an, Shaanxi Province, China

Background and Aim: This study addresses the scarcity of empirical evidence on children's participation in Physical Education (PE) and the perspectives of teachers and parents within the Chinese context. The study aims to determine the level of student engagement in PE and identify factors influencing their participation. It also seeks insights into how teachers and parents view PE's importance in children's well-being. Materials and Methods: Conducted in Xi'an, Shaanxi Province, China, the study involved 201 kindergartens, 200 parents, and 65 teachers. Analytical methods included tables, frequencies, percentages, mean proportion analysis, standard deviation proportion analysis, confidence interval analysis, one-sample z-score test for proportion, two-sample z-score test for proportion, and correlation analysis. The research also led to the development of a guide for creating a kindergarten's physical literacy model. Results: Kindergartens exhibit a moderate perception of PE, with males generally having a higher perception than females. Teachers view PE positively, considering it moderately beneficial for physical and mental development. However, barriers to teaching PE include limited event space, security concerns, venue and equipment issues, weather-related inconveniences, and parents' lack of sports proficiency. Parents have a favorable view of PE, acknowledging its positive impact. Barriers for parents include safety concerns, extracurricular workloads, limited interest in after-school sports, venue limitations, and academic pressures. Kindergartens express enthusiasm for sports like basketball and football, but challenges may reduce physical activity and lead to frustration. Conclusion: The perception of physical education in kindergartens varies among different stakeholders, with gender differences and positive attitudes from teachers and parents. However, numerous barriers, including space limitations and safety concerns, need to be addressed to ensure a more effective and enjoyable PE experience for children in this educational setting.

On Gait Consistency Quantification Through ARX Residual Modeling and Kernel Two-Sample Testing.

The quantification of the way an individual walks is key to the understanding of diseases affecting the neuromuscular system. More specifically, to improve diagnostics and treatment plans, there is a continuous interest in quantifying gait consistency, allowing clinicians to distinguish natural variability of the gait patterns from disease progression or treatment effects. To this end, the current article presents a novel objective method for assessing the consistency of an individual's gait, consisting of two major components. Firstly, inertial sensor accelerometer data from both shanks and the lower back is used to fit an AutoRegressive with eXogenous input model. The model residuals are then used as a key feature for gait consistency monitoring. Secondly, the non-parametric maximum mean discrepancy hypothesis test is introduced to measure differences in the distributions of the residuals as a measure of gait consistency. As a paradigmatic case, gait consistency was evaluated both in a single walking test and between tests at different time points in healthy individuals and those affected by multiple sclerosis (MS). It was found that MS patients experienced difficulties maintaining a consistent gait, even when the retest was performed one-hour apart and all external factors were controlled. When the retest was performed one-week apart, both healthy and MS individuals displayed inconsistent gait patterns. Gait consistency has been successfully quantified for both healthy and MS individuals. This newly proposed approach revealed the detrimental effects of varying assessment conditions on gait pattern consistency, indicating potential masking effects at follow-up assessments.