Additional Statistical Methods Research Articles

Empirical analysis of the cyberattacks of the Russian–Ukrainian war

This research study aims to empirically analyze the cyberattacks that occurred in the context of the Russian–Ukrainian conflict between 2022 and 2023, with a specific focus on the impact of these attacks on civilian infrastructure and institutions. The data collection for this study is based on publicly available sources from the Cyber- Peace Institute, taking into account various types of incidents such as malware, distributed denial of service (DDoS) attacks, spam, information operations, and website defacements. The study employs a network theory approach to examine the structure and dynamics of incidents and campaigns, while additional statistical methods and trend analysis are used to assess sector-specific and geographic patterns, as well as changes in attack frequency and severity. The research aims to contribute to the existing literature on cyber warfare and to provide valuable insights into the cyber threats faced by civilian infrastructure and institutions during times of conflict.

Conducting Multivariate One- and Two-Sample Statistical tests using SPSS Syntax

The availability of multivariate one-sample and two-sample testing procedures is often restricted or merged with other statistical tests in many statistical applications. SPSS software, known for its user-friendly Graphical User Interface (GUI) for statistical analysis, also provides a specialised programming language called SPSS syntax. This syntax allows users to execute statistical procedures by writing commands, providing an alternative to the GUI interface. The purpose of this paper is to create new dialogs written in the SPSS syntax language using custom dialog builder for extensions. These dialogs are designed to test multivariate one- and two-sample data when ∑ is unknown by applying Hotelling’s T2 test statistic, especially in situations where the GUI lacks these particular functionalities. In the case of two-sample data, both independent and paired multivariate testing methods are employed. Examples are supplied to demonstrate the key analyses performed using Hotelling's T2 tests. The syntax written for the analyses was performed using SPSS software v.27. The adoption of the developed dialogs is recommended for researchers requiring analysis of multivariate one- and two-sample data with limited or no programming experience, with potential future incorporation of the dialogs into forthcoming SPSS versions by IBM. Extending the scope of statistical analyses supported by SPSS dialogs through future research that encourages their use and supports wider acceptance of additional statistical methods not included in standard versions of SPSS.

Intelligent chemical profiling of 73 edible flowers by liquid chromatography-high resolution mass spectrometry combined with HRMS database and their authentication based on large-scale fingerprints

A commercial high-resolution MS database “TCM-PCDL” was innovatively introduced to automatically identify multi-components in 73 edible flowers rapidly and accurately by liquid chromatography-high resolution mass spectrometry, which can be time-consuming and labor-intensive in traditional manual method. The database encompasses over 2565 natural products with various energy levels. Unknown compounds can be identified through direct matching and scoring MS2 spectra with database. A total of 870 compounds were identified from 73 flowers, with polyphenols constituting up to 75%. Focusing on polyphenols, a high performance liquid chromatography (HPLC) method was developed to generate fingerprints from 510 batches, establishing an “HPLC database” that enabled accurate authentication using similarity scores and rankings. This method demonstrated an accuracy rate of 100% when applied to 30 unknown samples. For flowers prone to confusion, additional statistical analysis methods could be employed as aids in authentication. This study provides valuable insights for large-scale sample chemical profiling and authentication.

Seven psychiatric traits and the risk of increased carotid intima-media thickness: a Mendelian randomization study.

Numerous observational studies have suggested an association between psychiatric traits and carotid intima-media thickness (cIMT). However, whether these associations have a causal relationship remains unknown, largely due to issues of reverse causality and potential confounders. This study aims to elucidate the potential causal role of psychiatric traits in the risk of arterial injury as measured by cIMT. We utilized instrumental variables for attention deficit/hyperactivity disorder (ADHD, n = 226,534), bipolar disorder (n = 353,899), major depressive disorder (n = 142,646), post-traumatic stress disorder (n = 174,494), obsessive-compulsive disorder (n = 9,725), autism spectrum disorder (n = 173,773), and anxiety disease (n = 17,310), derived from the largest corresponding genome-wide association studies (GWAS). Summary statistics for cIMT associations were obtained from a meta-analysis combining GWAS data from the Cohorts for Heart and Aging Research in Genomic Epidemiology consortia (n = 71,128) and the UK Biobank study (n = 45,185). The inverse-variance weighted method served as the primary analytical tool, supplemented by additional statistical methods in the secondary analyses to corroborate the findings. Adjustments were made according to the Bonferroni correction threshold. The Mendelian randomization analyses indicated a suggestive causal link between genetically predicted ADHD and cIMT (beta = 0.05; 95% confidence interval, 0.01-0.09; p = 0.018). Sensitivity analyses largely concurred with this finding. However, no significant associations were found between other psychiatric traits and cIMT. This study provides insights into the risk effect of ADHD on cIMT, suggesting that arteriopathy and potential associated complications should be considered during the treatment and monitoring of patients with ADHD.

Importance of missingness in baseline variables: A case study of the All of Us Research Program.

The All of Us Research Program collects data from multiple information sources, including health surveys, to build a national longitudinal research repository that researchers can use to advance precision medicine. Missing survey responses pose challenges to study conclusions. We describe missingness in All of Us baseline surveys. We extracted survey responses between May 31, 2017, to September 30, 2020. Missing percentages for groups historically underrepresented in biomedical research were compared to represented groups. Associations of missing percentages with age, health literacy score, and survey completion date were evaluated. We used negative binomial regression to evaluate participant characteristics on the number of missed questions out of the total eligible questions for each participant. The dataset analyzed contained data for 334,183 participants who submitted at least one baseline survey. Almost all (97.0%) of the participants completed all baseline surveys, and only 541 (0.2%) participants skipped all questions in at least one of the baseline surveys. The median skip rate was 5.0% of the questions, with an interquartile range (IQR) of 2.5% to 7.9%. Historically underrepresented groups were associated with higher missingness (incidence rate ratio (IRR) [95% CI]: 1.26 [1.25, 1.27] for Black/African American compared to White). Missing percentages were similar by survey completion date, participant age, and health literacy score. Skipping specific questions were associated with higher missingness (IRRs [95% CI]: 1.39 [1.38, 1.40] for skipping income, 1.92 [1.89, 1.95] for skipping education, 2.19 [2.09-2.30] for skipping sexual and gender questions). Surveys in the All of Us Research Program will form an essential component of the data researchers can use to perform their analyses. Missingness was low in All of Us baseline surveys, but group differences exist. Additional statistical methods and careful analysis of surveys could help mitigate challenges to the validity of conclusions.

Are the results of immunochemical screening of urine for synthetic cannabinoids affected by other common drugs? – Pilot statistical study on samples from detention prisons

Determine whether the outcome of immunochemical screening for synthetic cannabinoids is affected by the presence of drugs frequently abused by prisoners. Current surveillance of drug and drug abuse in prisons does not routinely use synthetic cannabinoid (SC) screening, so their possible occurrence may go undetected. Since the thresholds for the detection of SC must be low (ng/mL and less) due to their high efficiency, theoretically other substances present in the urine at orders of magnitude higher concentrations may influence the results of the SC screeqning. Urine samples of 450 prisoners from detention prisons in Prague were tested with immunochemical tests for the presence of buprenorphine, benzodiazepines, fentanyl, methamphetamine-amphetamine-ecstasy, morphine, cannabinoids, and tramadol. The results were in binary format, 1 or 0 (Dataset 1). These samples were further tested for the presence of SC [K2 SC–1 (JWH-210); K2-II SC–2 (UR-144); K2-III SC–3 (AB-PINACA); all Thermo Scientific] on the Indiko Plus (Thermo Scientific) instrument. The format of their results are numeric values (Dataset 2). The statistical data evaluation and analysis were done with Statistica software. The dataset 1 was analyzed for distribution of zero, single, double, triple etc. occurrence of positive and negative screening results. SC-1, SC-2, and SC-3 values of dataset 2 were analyzed for the possible correlation, and the principal component analysis (PCA) was applied to evaluate the data structure, where the SC-1 and SC-2 data were found more intercorrelated in contrast to SC-3 data which do not correlate with the previous SC-1 and SC-2 values. Furthermore, the possibilities of dependencies between the two datasets 1 and 2 were studied. First, the multiple linear regression analysis (MLR) was used, where the dependent variables were the values SC-1, SC-2 and SC-3 and the binary values of dataset 1 were used as independent variables. A statistically significant (but close to critical value) relationship was found between SC-1, SC-2 and SC-3 and positive results for the test from dataset 1. No dependence was found between the occurrence of other substances and the SC test values. As an additional statistical method for determining possible relationships, the logistic regression analysis (LogReg) was used. Binary values corresponding to the presence or absence (1 or 0) of the test substance (dataset 1) were used as a dependent variables and dataset values 2 (SC-1, SC-2, SC-3) were used as independent ones. As with MLR, a statistically significant dependence (also close to the critical value) was found between the presence or absence of methamphetamine-amphetamine-ecstasy (dataset 1) and the SC values of dataset 2. According to the PCA, a correlation was found between the values of the SC-1 and SC-2 test results. Although correlations were found among the methamphetamine-amphetamine-ecstasy incidence and the SC-1, SC-2 and SC-3 test result values, the statistical significance parameters for both the MLR and the LogReg analyses were very close to their critical values. In addition, the results of the analyses were rather sensitive to the choice of the regression dataset. Based on these findings, it is not possible to unequivocally answer the question whether the presence of commonly abused substances affects the results of immunochemical urine SC screening. To make a clear decision, it is necessary to statistically evaluate a larger set of data.

Linking fire-induced evapotranspiration shifts to streamflow magnitude and timing in the western United States

The impact of wildfire on water availability is a critical issue in the western United States. Because actual evapotranspiration (ETa) constitutes the largest loss in the terrestrial water budget, it has been suggested that fire-induced ETa reduction is a primary driver of elevated post-fire discharge. Ten gaged watersheds with burns exceeding 5% of their total contributing drainage area were selected from California, Oregon, Montana, Utah, New Mexico, and Colorado. Continuous daily stream gage data were compiled, and 30-meter ETa estimates were calculated with the Operational Simplified Surface Energy Balance (SSEBop) model. Fire-induced ETa shifts were quantified with statistical tests that compared pre and post-fire monthly ETa in burned and unburned pixels; the dampening effect of scale was also evaluated by repeating tests on all pixels from the entire basin. As streamflow data are point measurements that aggregate a large spatial area, additional statistical methods were required to isolate the effect of fire from climate on baseflow and runoff. Key findings include a) significant fire-induced ETa reductions were only distinguishable in basin-scale monthly datasets when at least 73% of the basin burned, b) the effect of wildfire disturbance on streamflow magnitude was seasonably variable, c) streamflow was modified in basins with as little as 6% burned drainage area; however, shifts only persisted beyond the fifth post-fire year where more than three-quarters of the basin was fire-impacted, and d) surplus water from ETa reduction was sufficient to account for boosted fire-induced streamflow. Where fire-induced streamflow increases were not significantly correlated with ETa anomaly, other fire-impacted landscape processes may have contributed to modified runoff generation and routing. Where fire reduced ETa but streamflow shifts were not detected, compensatory ETa pathways may have consumed the excess water before it reached the gage. Findings suggest that water providers with small source-water collection areas have higher relative risk for fire-induced hydromodification than providers with larger or more diversified supply portfolios. Results also illustrate the tendency of overarching climate signals to mask or artificially boost the apparent effect of landscape disturbance on streamflow at the basin outlet.

University course choice and the use of heuristics and biases as decision making mechanisms: a study in Brazil

The choice of a higher education course is one of the most important decisions individuals make, as it usually directs the choice of career or profession. Therefore, it is a complex decision, and it is thus possible that young individuals rely on heuristics and biases to facilitate the decision-making process. Considering that, we explored whether and how heuristics and biases affected college students’ judgments and decisions regarding courses. We developed a questionnaire and applied it to a sample of students in Brazil, and we analyzed the collected data using CATPCA and additional statistical methods. We found that students suffered from sunk-cost bias, in addition to the anchoring and representativeness heuristics. Additionally, we found no evidence of a framing effect. We conclude that heuristics and biases were relevant factors in students’ judgments and decisions, but they did not dominate them, operating, instead, in conjunction with traditional aspects of analysis and choice.

Monitoring of Lithium Contents in Lithium Ores and Concentrate-Assessment Using X-ray Diffraction (XRD)

Lithium plays an increasing role in battery applications, but is also used in ceramics and other chemical applications. Therefore, a higher demand can be expected for the coming years. Lithium occurs in nature mainly in different mineralizations but also in large salt lakes in dry areas. As lithium cannot normally be analyzed using XRF-techniques (XRF = X-ray Fluorescence), the element must be analyzed by time consuming wet chemical treatment techniques. This paper concentrates on XRD techniques for the quantitative analysis of lithium minerals and the resulting recalculation using additional statistical methods of the lithium contents. Many lithium containing ores and concentrates are rather simple in mineralogical composition and are often based on binary mineral assemblages. Using these compositions in binary and ternary mixtures of lithium minerals, such as spodumene, amblygonite, lepidolite, zinnwaldite, petalite and triphylite, a quantification of mineral content can be made. The recalculation of lithium content from quantitative mineralogical analysis leads to a fast and reliable lithium determination in the ores and concentrates. The techniques used for the characterization were quantitative mineralogy by the Rietveld method for determining the quantitative mineral compositions and statistical calculations using additional methods such as partial least square regression (PLSR) and cluster analysis methods to predict additional parameters, like quality, of the samples. The statistical calculations and calibration techniques makes it especially possible to quantify reliable and fast. Samples and concentrates from different lithium deposits and occurrences around the world were used for these investigations. Using the proposed XRD method, detection limits of less than 1% of mineral and, therefore down to 0.1% lithium oxide, can be reached. Case studies from a hard rock lithium deposit will demonstrate the value of mineralogical monitoring during mining and the different processing steps. Additional, more complex considerations for the analysis of lithium samples from salt lake brines are included and will be discussed.

Performance Analysis of MAU-9 Electronic-Nose MOS Sensor Array Components and ANN Classification Methods for Discrimination of Herb and Fruit Essential Oils

The recent development of MAU-9 electronic sensory methods, based on artificial olfaction detection of volatile emissions using an experimental metal oxide semiconductor (MOS)-type electronic-nose (e-nose) device, have provided novel means for the effective discovery of adulterated and counterfeit essential oil-based plant products sold in worldwide commercial markets. These new methods have the potential of facilitating enforcement of regulatory quality assurance (QA) for authentication of plant product genuineness and quality through rapid evaluation by volatile (aroma) emissions. The MAU-9 e-nose system was further evaluated using performance-analysis methods to determine ways for improving on overall system operation and effectiveness in discriminating and classifying volatile essential oils derived from fruit and herbal edible plants. Individual MOS-sensor components in the e-nose sensor array were performance tested for their effectiveness in contributing to discriminations of volatile organic compounds (VOCs) analyzed in headspace from purified essential oils using artificial neural network (ANN) classification. Two additional statistical data-analysis methods, including principal regression (PR) and partial least squares (PLS), were also compared. All statistical methods tested effectively classified essential oils with high accuracy. Aroma classification with PLS method using 2 optimal MOS sensors yielded much higher accuracy than using all nine sensors. The accuracy of 2-group and 6-group classifications of essentials oils by ANN was 100% and 98.9%, respectively.

Health and Housing Energy Expenditures: A Two-Part Model Approach

Interest in the interaction between energy and health within the built environment has been increasing in recent years, in the context of sustainable development. However, in order to promote health and wellbeing across all ages it is necessary to have a better understanding of the association between health and energy at household level. This study contributes to this debate by addressing the case of Portugal using data from the Household Budget Survey (HBS) microdata database. A two-part model is applied to estimate health expenditures based on energy-related expenditures, as well as socioeconomic variables. Additional statistical methods are used to enhance the perception of relevant predictors for health expenditures. Our findings suggest that given the high significance and coefficient value, energy expenditure is a relevant explanatory variable for health expenditures. This result is further validated by a dominance analysis ranking. Moreover, the results show that health gains and medical cost reductions can be a key factor to consider on the assessment of the economic viability of energy efficiency projects in buildings. This is particularly relevant for the older and low-income segments of the population.

Advancements and critical steps for statistical analyses in blood pressure response to resistance training in hypertensive older women: a methodological approach.

Repeated measures analysis of covariance and three-way analysis of variance with repeated measures are common statistical methods. For a valid interpretation of blood pressure (BP) response to exercise, a variety of additional statistical methods must be implemented. Four additional statistical methods are presented: technical error of measurement (SEM), smallest real difference (SRD), magnitude-based inference and mixed effect modeling technique (MEM). The aim of this perspective article is to demonstrate how to apply already known statistical analyses regarding BP responsiveness in order to improve interpretation and achieve higher reliability for future studies in exercise science. A total of 27 hypertensive older women (aged 68.37 ± 5.55 years) participated in the present study. A whole-body resistance training (RT) program was performed on two nonconsecutive days per week for 10 weeks. BP was monitored during the 10-week RT intervention and after 15 weeks of detraining. First, individuals were classified as high and low responders, then statistical methods to analyze data included the use of SEM, SRD, magnitude-based inference and MEM. When magnitude-based inference was used to classify responsiveness, most participants displayed a trivial response. Decrements in SBP between 1 and 10 mmHg were not clinically meaningful but fell within the measurement error of the SBP measurements. Baseline SBP and time of training predicted post-SBP response. Changes over time and declines in SBP might not be a SRD and fell in the SEM. Moreover, SBP responsiveness was the result of inappropriate control of covariates such as period of training.

Characterization and differentiation of aluminum powders used in improvised explosive devices - Part 1: Proof of concept of the utility of particle micromorphometry.

Aluminum (Al) powders are commonly used in improvised explosive devices as metallic fuels, a component of explosive mixtures. These powders can be obtained readily from industrial-scale and consumer products, and produced using unsophisticated "kitchen chemistry" techniques. This research demonstrates the potential of automated particle micromorphometry for comparisons between known source and questioned Al powders recovered from IEDs, as well as for insight into the method of Al powder manufacture. Al powder samples were obtained from legitimate manufacturers, and 56 samples were produced "in-house" from Al-containing spray paints and ball-milled Al foils. Transmitted light microscope images of Al powder particles were acquired using an automated stage with automated z-focus; 17 size and shape parameters were measured for all particles. Approximately 37,000-2,500,000 particles/sample were analyzed using an open-source statistical package with customized code. Dimensionality reduction was required for processing the large datasets: eight of the 17 measured variables were selected based on inspection of the correlation matrix. Data from four subsamples from each of the 56 samples produced using "in-house" methods were analyzed using ANOVA to assess the within- and between-sample variation. High within-sample variation was noted; however, ANOVA and post-hoc Tukey's honestly significant difference (HSD) tests demonstrated that the between-sample variation was substantially larger than the within-sample variation. Each sample could be differentiated from all other samples in the test set. Future experiments will focus on ways to reduce the within-sample variation, and additional statistical and microanalytical methods to classify sources and confidently constrain the method of Al powder manufacture.

Outlier Detection in Growth Data: Beyond Biologically Implausible Values

Abstract Objectives Eliminating anthropometry measurement error and employing outlier and biological implausible values (BIV) detection methods adapted to longitudinal measurements is important for the study of growth. This work aimed to review and assess the accuracy of the available BIV and outlier detection methods and propose a growth trajectory outlier detection method. Methods We included 2354 infants from the Applied Research Group for Kids (TARGet Kids! ) cohort-based in Toronto (ON, Canada) that recruits healthy children from birth to 5 years of age. We considered infants with at least 8 length and weight measurements available between the 1st and the 24th month of age. Weight-for-length z-scores (wflz) were calculated using the WHO growth standards. Outlier measurements were randomly introduced in 5% of the wflz measurements using a normal distribution (μ = 0, σ = 1). We employed 4 outlier detection methods; an empirical detection method for BIV using the cut-offs derived from the WHO Child Growth Standards, a clustering method, a method based on cluster prototypes for individual outlier measurements and a method based on cluster prototypes for entire growth trajectories. Each method was applied individually and evaluated using the sensitivity and specificity indexes based on the manually introduced outliers. We also calculated the Kappa statistic to evaluate the agreement of each method against the manual outliers. Results After excluding premature (<37 weeks), low birth weight (<1500 g) neonates and children with missing length and weight measurements, we analyzed 393 children with a total of 3144 measurements. Sensitivity and specificity for the four methods ranged between 4.4%–55.0% and 83.7% −99.7%, respectively, with kappa being non-significant (P > 0.05) only for the empirical. The clustering detection method reported a higher finding rate, while the empirical method found most of the BIV, but few of the rest of the outliers. Conclusions BIV account for a small portion of the possible outliers in growth datasets. We show that additional statistical or model-based methods are required for a more comprehensive outlier detection process, which has implications for growth analysis and nutritional assessment. Funding Sources Joannah and Brian Lawson Center for Child Nutrition, Connaught Fund, Onassis Foundation.

Groundwater Level Fluctuation Analysis in a Semi-Urban Area Using Statistical Methods and Data Mining Techniques—A Case Study in Wrocław, Poland

Long-term groundwater level analysis, which is usually based on traditionally defined hydrological years is essential in an era of global warming and other climatic and environmental changes, especially in urban areas. A complex interplay of multiple factors influencing the groundwater level makes the investigation of their interdependencies a challenge. Based on multiple data sets and a long time series available as well as specific geological and hydrological conditions, a semi-urban district of Wrocław/Poland was selected as a case study for investigating these dependencies. This paper presents an interdisciplinary approach to the analysis of groundwater level fluctuations by combining mathematics, signal processing, hydrogeology, and meteorology. Applying well-known methods from disciplines other than hydrogeology, the authors investigated seasonal behavior and similarity of groundwater level fluctuations during 15 hydrological years. Based on segmentation and agglomerative clustering (AHP), five classes of groundwater levels fluctuations for predefined hydrologic years and the corresponding seasons were identified and compared to the classification scheme by Pleczyński. Additionally, the relationship between precipitation and groundwater level was investigated using Pearson, Kendall and Spearman correlations. This led to the identification of “typical” and “untypical” seasons for the correlation between the cumulative precipitation sum and groundwater levels. The results presented here will be used for further investigations of groundwater level fluctuations using additional factors and statistical methods. These aim to identify periods that describe similarities better than the commonly used hydrological year.

Analyzing Trend and Variability of Rainfall in The Tafna Basin (Northwestern Algeria)

Northwest Algeria has experienced fluctuations in rainfall between the two decades 1940s and 1990s from positive to negative anomalies, which reflected a significant decline in rainfall during the mid-1970s. Therefore, further analyzing rainfall in this region is required for improving the strategies on water resource management. In this study, we complement previous studies by dealing with sub basins that were not previously addressed in Tafna basin (our study area located in Northwest Algeria), and by including additional statistical methods (Kruskal–Wallis test, Jonckheere-Terpstra test, and the Friedman test) that were not earlier reported on the large scale (Northwest Algeria). In order to analyse the homogeneity, trends, and stationarity in rainfall time series for nine rainfall stations over the period 1979–2011, we have used several statistical tests. The results showed an increasing trend for annual rainfall after the break detected in 2007 for Djbel Chouachi, Ouled Mimoun, Sidi Benkhala stations using Hubert, Pettitt, and Buishand tests. The Lee and Heghinian test has detected a break at the same year in 2007 for all stations except Sebdou, Beni Bahdel, and Hennaya stations, which have a break date in 1980. We have confirmed this increasing trend for rainfall with other trend detection methods such as Mann Kendall and Sen’s method that highlighted an upward trend for all the stations in the autumn season, which is mainly due to an increase in rainfall in September and October. On a monthly scale, the date of rupture is different from one station to another because the time series are not homogeneous. In addition, we have applied three tests enabling further results: (i) the Jonckheere-Terpstra test has detected an upward trend for two stations (Khemis and Hennaya), (ii) Friedman test has indicated the difference between the mean rank again with Khemis and Hennaya stations and the Merbeh station, (iii) according to the Kruskal-Wallis test, there have been no variance detected between all the rainfall stations. The increasing trend in rainfall may lead to a rise in stream flow and enhance potential floods risks in low-lying regions of the study area.

MiCA: An extended tool for microarray gene expression analysis

Gene expression microarrays capture a complete image of all the transcriptional activity in a biological sample. Microarrays produce a large amount of data, which becomes a challenge when it comes to exploring and interpreting using modern computational and statistical tools. We propose the Microarray Analysis (MiCA) tool that outperforms other similar tools both in terms of ease of use and statistical features requiring minimal input to conduct an analysis.MiCA is an integrated, interactive, and streamlined desktop software for the analysis of microarray gene expression data. MiCA consists of a complete microarray analysis pipeline including but not limited to fetching data directly from GEO, normalization, interactive quality control, batch-effect correction, regression analysis, surrogate variable analysis and functional annotation methods such as GSVA using known existing R packages. We compare the features offered by MiCA and other similar tools while performing differential expression analysis using previously published datasets.MiCA offers additional statistical and visualization methods to conduct a microarray data analysis compared to other available microarray analysis tools. MiCA minimizes the need for technical knowledge by providing a very intuitive and versatile interface that integrates all necessary tasks and features required for basic microarray data analysis. We analyzed multiple published datasets and showed that the features offered by MiCA not only simplify the analysis pipeline but also provide additional interpretation to the data.

Risk assessment model for predicting ventricular tachycardia or ventricular fibrillation in ST-segment elevation myocardial infarction patients who received primary percutaneous coronary intervention.

Ventricular tachycardia/ventricular fibrillation (VT/VF) is a kind of malignant arrhythmia in ST-segment elevation myocardial infarction (STEMI) patients who received primary percutaneous coronary intervention (PPCI). However, there are no risk assessment tools to anticipate the occurrence of VT/VF.This study is to build a risk assessment model to predict the possibility of VT/VF onset in STEMI patients undergoing PPCI.A retrospective study was conducted to analyze the patients who underwent PPCI from January 2006 to May 2015. Subjects were divided into VT/VF group and no VT/VF group based on whether VT/VF had occurred or not. In addition, the VT/VF group was further separated into early-onset group (from the time that symptoms began to before the end of PPCI) and late-onset group (after the end of PPCI) based on the timing of when VT/VF happened. Multivariate regression analysis was carried out to distinguish the independent risk factors of VT/VF and an additional statistical method was executed to build the risk assessment model.A total of 607 patients were enrolled in this study. Of these patients, 67 cases (11%) experienced VT/VF. In addition, 91% (61) of patients experienced VT/VF within 48 h from the time that the symptoms emerged. Independent risk factors include: age, diabetes mellitus, heart rate, ST-segment maximum elevation, ST-segment total elevation, serum potassium, left ventricular ejection fraction (LVEF), culprit artery was right coronary artery, left main (LM) stenosis, Killip class > I class, and pre-procedure thrombolysis in myocardial infarction (TIMI) flow zero grade. Risk score model and risk rank model have been established to evaluate the possibility of VT/VF. Class I: ≤ 4 points; Class II: > 4 points, ≤ 5.5 points; Class III: > 5.5 points, < 6.5 points; and Class IV ≥ 6.5 points. The higher the class, the higher the risk.The incidence of VT/VF in STEMI patients undergoing PPCI is 11% and it occurs more frequently from the time that symptoms begin to before the end of PPCI, which, in most cases, occurs within 48 h of the event. Our risk assessment model could predict the possible occurrence of VT/VF.

Adaptive Design in Surveys and Clinical Trials: Similarities, Differences and Opportunities for Cross-fertilization

SummaryAdaptive designs involve preplanned rules for modifying an on-going study based on accruing data. We compare the goals and methods of adaptation for trials and surveys, identify similarities and differences, and make recommendations for what types of adaptive approaches from one domain have high potential to be useful in the other. For example, clinical trials could benefit from recently developed survey methods for monitoring which groups have low response rates and intervening to fix this. Clinical trials may also benefit from more formal identification of the target population, and from using paradata (contextual information collected before or during the collection of actual outcomes) to predict participant compliance and retention and then to intervene to improve these. Surveys could benefit from stopping rules based on information monitoring, applying techniques from sequential multiple-assignment randomized trial designs to improve response rates, prespecifying a formal adaptation protocol and including a data monitoring committee. We conclude with a discussion of the additional information, infrastructure and statistical analysis methods that are needed when conducting adaptive designs, as well as benefits and risks of adaptation.

Large Study of Black Adults Points to Strategy to Slow Dementia

Back to table of contents Previous article Next article Clinical and Research NewsFull AccessLarge Study of Black Adults Points to Strategy to Slow DementiaNick ZagorskiNick ZagorskiSearch for more papers by this authorPublished Online:19 Oct 2018https://doi.org/10.1176/appi.pn.2018.10b4AbstractA clinical study in older black adults found that using behavioral activation to reinforce healthy living could slow cognitive decline over a two-year period.Encouraging older black adults with mild cognitive impairment (MCI) to increase their cognitive, physical, and/or social activities may help to slow cognitive decline, according to a study posted on September 10 in JAMA Neurology.iStock/monkeybusinessimagesThis study is not the first to suggest that staying mentally, physically, and socially active can help slow down cognitive decline; however, previous population studies in the United States have not included significant numbers of black individuals, so it was not known whether the cognitive benefits of staying active generalized to blacks.This new study, led by investigators at Sidney Kimmel Medical College of Thomas Jefferson University in Philadelphia, enrolled 221 black adults aged 65 and older with MCI. The participants were randomly assigned to receive either behavioral activation or supported therapy over a two-year period. Behavioral activation uses strategies like setting goals and making action plans to reinforce a healthy lifestyle, while supportive therapy involves structured, empathic discussions with people about the experience of aging and memory loss but does not include any behavioral reinforcement strategies.Participants in both groups received five in-home, hour-long treatment sessions over the first four months, followed by six in-home, hour-long follow-up sessions over the next 20 months. The sessions were administered by trained community health workers.The participants were given cognitive tests and asked about their cognitive, physical, and social activities every six months. There were no significant differences in cognitive performance between the groups at six months, but in the subsequent follow-ups, the adults receiving behavioral activation began to perform better than those in the supportive therapy group.The primary measurement was the number of participants whose scores on the Hopkins Verbal Learning Test (which tasks people to learn a set of words and then remember them after 20 to 25 minutes) declined by at least six points from baseline. Only 1 percent of the behavioral activation group had a significant decline after 24 months, compared with 9 percent in the supportive therapy group.Lead study investigator Barry W. Rovner, M.D., a professor of psychiatry and neurology at Thomas Jefferson University, told Psychiatric News that using a preset cutoff on the Hopkins test could be seen as arbitrary; people might have dropped 4 or 5 points on their cognitive test scores and not be considered as showing a decline. To account for this, the investigators used additional statistical methods to compare cognitive decline.“We found that if you looked at overall test performance over the two years, the participants in the behavioral activation group were half as likely to show any decline on the cognitive test at any given time point,” Rovner said.In discussing why behavioral activation showed such significant results, Rovner noted that social determinants of health such as environment or income play a significant role in the dementia risk of blacks.“Starting from conception, a time during which black mothers have higher rates of stress, poor nutrition, and less access to quality medical care, blacks face tremendous health disparities that break down their cognitive reserve and double their risk of dementia,” he said. “This study showed that if we intervene in that socioeconomic context and enrich these adults’ environments in a culturally sensitive way, we can overcome that adversity.”Translating these controlled study findings to a real-world setting is challenging, Rovner admitted. Study participants received continual behavioral reinforcement from trained community workers, and many older black adults do not have access to such personnel in their communities. But that’s where family members can step in, he said. “That’s the way we can scale this up to the community level.”Rovner said that the behavioral strategies employed do not even have to be very detailed if that proves difficult for family members. He noted that while behavioral activation is superior, the 9 percent decline in the supportive therapy group was encouraging. Epidemiological studies have suggested that up to 20 percent of black adults with MCI show significant cognitive decline over two years. That indicates that even a little bit of support and social interaction can benefit this population, he said.This study was supported by a grant from the National Institute on Aging. ■“Preventing Cognitive Decline in Black Individuals With Mild Cognitive Impairment: A Randomized Clinical Trial” can be accessed here. ISSUES NewArchived