Robust Statistical Approach Research Articles

Identifying fabricated networks within authorship-for-sale enterprises

It is estimated that 2% of all journal submissions across all disciplines originate from paper mills, both creating significant risk that the body of research that we rely on to progress becomes corrupted, and placing undue burden on the submission process to reject these articles. By understanding how the business of paper mills—the technological approaches that they adopt, as well as the social structures that they require to operate—the research community can be empowered to develop strategies that make it harder, or ideally impossible for them to operate. Most of the contemporary work in paper-mill detection has focused on identifying the signals that have been left behind inside the text or structure of fabricated papers that result from the technological approaches that paper mills employ. As technologies employed by paper mills advance, these signals will become harder to detect. Fabricated papers do not just need text, images, and data however, they also require a fabricated or partially fabricated network of authors. Most ‘authors’ on a fabricated paper have not been associated with the research, but rather are added through a transaction. This lack of deeper connection means that there is a low likelihood that co-authors on fabricated papers will ever appear together on the same paper more than once. This paper constructs a model that encodes some of the key characteristics of this activity in an ‘authorship-for-sale’ network with the aim to create a robust method to detect this type of activity. A characteristic network fingerprint arises from this model that provides a robust statistical approach to the detection of paper-mill networks. The model suggested in this paper detects networks that have a statistically significant overlap with other approaches that principally rely on textual analysis for the detection of fraudulent papers. Researchers connected to networks identified using the methodology outlined in this paper are shown to be connected with 37% of papers identified through the tortured-phrase and clay-feet methods deployed in the Problematic Paper Screener website. Finally, methods to limit the expansion and propagation of these networks is discussed both in technological and social terms.

Environmental Assessment of Calcium Sulfoaluminate Cement: A Monte Carlo Simulation in an Industrial Symbiosis Framework

Calcium sulfoaluminate (CSA) cement is recognized as an environmentally friendly alternative to Portland cement (PC) due to its lower carbon footprint and energy requirements. However, traditional CSA cement production faces significant obstacles, including the high cost and regionally constrained availability of bauxite, a key raw material. Utilizing alternative materials in the production process offers a viable approach to address these limitations. This study evaluated the environmental performance of three laboratory-synthesized CSA cements using alternative raw materials sourced through an industrial symbiosis framework. A comparative assessment with PC was conducted, focusing on energy consumption and CO2 emissions as key environmental performance indicators. The environmental impact of the CSA cements was analyzed using Monte Carlo simulations, a robust statistical approach based on data for the constituent raw materials. This method provides a practical alternative to a full life cycle assessment (LCA) when comprehensive data are not available. The results demonstrate that the CSA cements have significantly lower environmental impacts compared to PC, achieving energy savings of 13–16% and CO2 emission reductions of 35–48%. These results emphasize the potential of industrial symbiosis to enable more sustainable CSA cement production while addressing raw material constraints. In addition, this approach highlights the wider applicability of industrial symbiosis frameworks in the construction industry, contributing to a zero-waste future and supporting global climate goals.

Gender-Specific Hormonal Profiles: Unveiling the Complex Link to Stroke Risk

Stroke affects women more frequently, suggesting a possible link to sex hormones, yet the evidence for this remains inconclusive. Here, we present a pioneering exploration of the nuanced interplay between genetically predicted sex hormone levels, with a particular focus on testosterone and SHBG, to elucidate their causal relationship with stroke risk. Utilizing Mendelian randomization, we aimed to determine whether SHBG levels causally affect stroke occurrence, with a focus on sex-specific effects and the interplay with established stroke risk factors. We leveraged genetic variants from comprehensive genome-wide association studies (GWAS), which are known to robustly predict levels of total and bioavailable testosterone, SHBG, and cholesterol. Employing a robust statistical approach, we meticulously analyzed published GWAS summary statistics, applying inverse variance weighting and conducting comprehensive sensitivity analyses with state-of-the-art methods such as MR-Egger, weighted median, and MR-PRESSO. Multivariate analyses further addressed potential confounders, including pleiotropic effects and selection bias. Strikingly, our results demonstrate that elevated SHBG levels are robustly associated with a markedly reduced stroke risk in women. In contrast, lower SHBG levels were associated with an increased risk of small-vessel ischemic stroke, potentially due to elevated cholesterol. These novel insights not only highlight the intricate interplay among SHBG, cholesterol, and immune system components in the pathogenesis of stroke but also illuminate avenues for future research aimed at unraveling the underlying biological mechanisms.

A robust statistical approach for finding informative spatially associated pathways.

Spatial transcriptomics offers deep insights into cellular functional localization and communication by mapping gene expression to spatial locations. Traditional approaches that focus on selecting spatially variable genes often overlook the complexity of biological pathways and the interactions among genes. Here, we introduce a novel framework that shifts the focus towards directly identifying functional pathways associated with spatial variability by adapting the Brownian distance covariance test in an innovative manner to explore the heterogeneity of biological functions over space. Unlike most other methods, this statistical testing approach is free of gene selection and parameter selection and allows nonlinear and complex dependencies. It allows for a deeper understanding of how cells coordinate their activities across different spatial domains through biological pathways. By analyzing real human and mouse datasets, the method found significant pathways that were associated with spatial variation, as well as different pathway patterns among inner- and edge-cancer regions. This innovative framework offers a new perspective on analyzing spatial transcriptomic data, contributing to our understanding of tissue architecture and disease pathology. The implementation is publicly available at https://github.com/tianlq-prog/STpathway.

Behaviour of effective heat transfer rate in radiating micropolar nanofluid over an expanding sheet with slip effects

The enhancement in the heat transfer rate is one of the vital aspects nowadays for the main objective of various industries to get better quality as well as the longevity of the product. Not only in industries but also in various areas such as in drug delivery, peristaltic pumping process, etc. it is found that this will be benefited by the utility of the various nanofluids that gives rise to enhanced thermal conductivity. Therefore, the present investigation leads to analyse the performance of effective heat transfer rate considering the flow of radiating micropolar nanofluid through a permeable expanding sheet embedding within a permeable medium. Precisely, the consideration of slip boundary conditions of both velocity and thermal profile enhances the flow phenomena. The main focus of the study is the consideration of Gherasim model viscosity and Hamilton-Crosser model which energies the thermal properties. Shooting based “Runge-Kutta fourth-order” technique is useful for the solution of governing equation followed by the transformation of these equations into dimensional form to the non-dimensional for with the help of suitable similarity rules. Further, a robust statistical approach i.e. “response surface methodology” is adopted to get optimized heat transfer rate for various factors in two different conditions such as injection and suction. However, the validation is presented through hypothetical test comparing the variances. Moreover, the important outcomes are: Nanoparticle concentration encourages the flow properties along with the heat transport phenomena. The thermal energy due to the enhanced thermal radiation accelerates the heat transmission rate for both the case of suction/injection.

Assessment of the Link between CO2 $emissions and Socio-Economic Indicators

Abstract This article examines the intricate relationships between carbon dioxide (CO2) emissions and key socio-economic indicators across various sectors. Utilizing different statistic methods, we scrutinize the interdependencies between air emissions and indicators such as Gross Domestic Product (GDP), education, life expectancy, pesticide use, recycling rates, agricultural production, net income, and tourism. Our analysis reveals a complex panorama where economic growth, indicated by GDP, agricultural output, and tourism, exhibits a strong positive correlation with CO2 emissions, suggesting that economic activity is a significant driver of environmental impact. Intriguingly, life expectancy also correlates positively with emissions, potentially reflecting the dual facets of industrial development, which, while boosting living standards and healthcare, concurrently increases environmental burdens. Conversely, educational attainment shows a negligible correlation, hinting at the potential for decoupling intellectual capital from carbon output. Through a robust statistical approach, this study underscores the multifaceted linkages between emissions and socio-economic health, highlighting areas for policy intervention and sustainable development strategies to balance economic advancement with environmental stewardship.

Post-glaciation depositional changes in Wijdefjorden, northern Svalbard, using grain-size end-member modelling

Grain-size end-member (EM) modelling is a robust statistical approach for identifying and quantifying dominant grain-size distributions. This approach provides a novel perspective for understanding the impact of interactions between depositional processes in complex sedimentary environments. This study examines grain-size distributions of six glacimarine sediment cores collected along an NS transect from the continental shelf to the Wijdefjorden system in northern Svalbard. In addition, we integrate grain-size EMs with lithologic and acoustic facies, allowing us to identify three distinct groups of EMs (EM1–3), each closely associated with specific depositional processes: turbid meltwater discharge (EM1), sediment winnowing by bottom currents (EM2), and the deposition of ice-rafted debris in glacimarine conditions and subglacial till (EM3). An analysis of the three EM groups reveals that the glacial retreat during the last deglaciation and the Atlantic Water inflow significantly impacted depositional changes within the Wijdefjorden system. In contrast, a decrease in the Atlantic Water inflow during the late Holocene corresponds to glacial re-advance, resulting in shifts in the depositional environment. This study demonstrates the utility of EM modelling in deciphering complex grain-size distributions and reconstructing different climate-driven depositional processes in glacimarine sediments in Svalbard fjords. This integrated approach enhances our understanding of the intricate interplay among climate change, glacier dynamics, and oceanic forcing in polar fjord environments.

Localized colorectal cancer database integrating 4 randomized controlled trials; (JCOG2310A)

Although phase III randomized controlled trials (RCTs) represent the most robust statistical approach for answering clinical questions, they require massive expenditures in terms of time, labor, and funding. Ancillary and supplementary analyses using RCTs are sometimes conducted as alternative approaches to answering clinical questions, but the available integrated databases of RCTs are limited. In this background, the Colorectal Cancer Study Group (CCSG) of the Japan Clinical Oncology Group (JCOG) established a database of ancillary studies integrating four phase III RCTs (JCOG0212, JCOG0404, JCOG0910 and JCOG1006) conducted by the CCSG to investigate specific clinicopathological factors in pStage II/III colorectal cancer (JCOG2310A). This database will be updated by adding another clinical trial data and accelerating several analyses that are clinically relevant in the management of localized colorectal cancer. This study describes the details of this database and planned and ongoing analyses as an initiative of JCOG cOlorectal Young investigators (JOY).

Beyond change scores: Employing an improved statistical approach to analyze the impact of entry fitness on physical performance during British Army basic training in men and women.

The aim was to use a robust statistical approach to examine whether physical fitness at entry influences performance changes between men and women undertaking British Army basic training (BT). Performance of 2 km run, seated medicine ball throw (MBT) and isometric mid-thigh pull (MTP) were assessed at entry and completion of Standard Entry (SE), Junior Entry-Short (JE-Short), and Junior Entry-Long (JE-Long) training for 2350 (272 women) recruits. Performance change was analyzed with entry performance as a covariate (ANCOVA), with an additional interaction term allowing different slopes for courses and genders (p < 0.05). Overall, BT courses saw average improvements in 2 km run performance (SE: -6.8% [-0.62 min], JE-Short: -4.6% [-0.43 min], JE-Long: -7.7% [-0.70 min]; all p < 0.001) and MBT (1.0-8.8% [0.04-0.34 m]; all p < 0.05) and MTP (4.5-26.9% [6.5-28.8 kg]; all p < 0.001). Regression models indicate an expected form of "regression to the mean" whereby test performance change was negatively associated with entry fitness in each course (those with low baseline fitness exhibit larger training improvements; all interaction effects: p < 0.001, > 0.006), particularly for women. However, when matched for entry fitness, men displayed considerable improvements in all tests, relative to women. Training courses were effective in developing recruit physical fitness, whereby the level of improvement is, in large part, dependent on entry fitness. Factors including age, physical maturity, course length, and physical training, could also contribute to the variability in training response between genders and should be considered when analyzing and/or developing physical fitness in these cohorts for future success of military job-task performance.

Exploring the link between particulate matter pollution and acute respiratory infection risk in children using generalized estimating equations analysis: a robust statistical approach

BackgroundIndia is facing a burdensome public health challenge due to air pollution, with a particularly high burden of acute respiratory infections (ARI) among children. To address this issue, our study aims to evaluate the association between exposure to fine particulate matter (PM2.5) and ARI incidence in young children in India.Materials and methodsOur study used PM2.5 data provided by the Atmospheric Composition Analysis Group at Washington University to assess the association between PM2.5 exposure and ARI incidence in 223,375 children sampled from the 2019–2021 Demographic Health Survey in India. We employed the generalized estimating equation and reported odds ratios and 95% confidence intervals for a 10 µg/m3 increase in PM2.5 and quartiles of PM2.5 exposure.ResultsEach 10 µg/m3 increase in PM2.5 levels was associated with an increased odds of ARI (OR: 1.23, 95% CI: 1.19–1.27). A change from the first quartile of PM2.5 (2.5–34.4 µg/m3) to the second quartile (34.5–51.5 µg/m3) of PM2.5 was associated with a two-fold change (OR: 2.06, 95% CI: 1.60–2.66) in the odds of developing ARI. Similarly, comparing the first quartile to the fourth quartile of PM2.5 exposure (78.3–128.9 µg/m3) resulted in an over four-fold increase in the odds of ARI (OR: 4.45, 95% CI: 3.37–5.87).ConclusionMitigation efforts must be continued implementing higher restrictions in India and to bring new interventions to ensure safe levels of air for reducing the burden of disease and mortality associated with air pollution in India.

Sensitivity analysis on optimizing heat transfer rate in hybrid nanofluid flow over a permeable surface for the power law heat flux model: Response surface methodology with ANOVA test

&lt;abstract&gt; &lt;p&gt;Joule dissipation has an important role in the conversion of mechanical energy to heat within a fluid due to the internal friction and viscosity. Moreover, Darcy friction is a measure of the resistance to flow in a porous medium. In response to the efficient heat transfer performance, a robust statistical approach was established to optimize the heat transfer rate in a two-dimensional flow of a nanofluid over a permeable surface embedded with a porous matrix. The electrically conducive fluid affected the flow phenomena to include a carbon nanotube nanoparticle in the conventional liquid water for the enhanced heat transfer properties; additionally, the power-law heat flux model was considered. Appropriate transformation rules were adopted to obtain a non-dimensional system that brought a developed model equipped with several factors. The traditional numerical technique (i.e., shooting based Runge-Kutta) was proposed to handle the coupled nonlinear system. Furthermore, the statistical response surface methodology (RSM) was adopted to obtain an efficient optimized model for the heat transportation rate of the considered factors. An analysis of variance (ANOVA) was utilized to validate the result of the regression analysis. However, it was evident that the nanoparticle concentrations were useful to augment the fluid velocity and the temperature distributions; the statistical approach adopted for the heat transfer rate displayed an optimized effect as compared to a conventional effect.&lt;/p&gt; &lt;/abstract&gt;

Association between dietary consumption patterns and the development of adolescent overnutrition in eastern Ethiopia: new perspectives.

Overnutrition among adolescents is becoming a major public health concern, with all the adverse consequences associated with unhealthy eating behaviors. Hence, clear evidence linking dietary consumption with the risk of overnutrition is crucial for targeted dietary recommendations using a robust statistical approach. This study assessed the link between dietary consumption patterns and the risks of overnutrition among adolescents in Ethiopia. A community-based survey was conducted on a random sample of 510 adolescents selected using a stratified random sampling via proportional allocation. Dietary consumption was captured using a validated and contextualized 80-item food frequency questionnaire over the past month through a face-to-face interview. Weight and height were measured under a standard procedure. Body Mass Index for Age Z-score (BAZ) was calculated, and BAZ above +1 was considered overnutrition. The frequency measures were standardized into daily equivalents, and dietary patterns were derived using exploratory factor analysis after checking for assumptions. A bivariable and multivariable binary logistic regression model was fitted with an odds ratio and 95% confidence intervals. A total of 510 participants were enrolled. Four major dietary patterns ("cereals, energy, and discretionary calory," "fat, oil, and milk groups," "proteins and vegetables," and "fruits"), explaining 66.6% of the total variation, were identified. The overall prevalence of overnutrition was 29.0% (27-31%), where 22.5 and 6.5% were overweight and obese, respectively. Physical inactivity (AOR = 6.27; 95% CI: 2.75-14.3), maternal literacy (AOR = 111.3; 95% CI: 50.0-247.8), habit of snacking (AOR = 1.80; 95% CI: 0.69-4.67), skipping meals (AOR = 2.05; 955 CI: 0.84-5.04), cereals and discretionary food dietary pattern (AOR = 2.28; 95 CI: 0.94-5.55), and protein-rich and vegetable dietary pattern (AOR = 2.30; 95% CI: 0.97-5.46) were important factors associated with odds of overnutrition. Overnutrition is a public health concern affecting one-third of adolescents, and it is closely linked with dietary consumption patterns, eating behaviors, wealth status, literacy, and level of physical activity. Therefore, public health interventions targeting unhealthy eating and lifestyles are urgently needed to curb the increasing burden of overnutrition among adolescents and its future complications.

Novel fluorometric protocol for assessing myeloperoxidase activity

Neutrophil myeloperoxidase (MPO) is an essential enzyme for the innate immune system. Measuring MPO activity is vital for understanding neutrophil characteristics and functions in various diseases. MPO activity can be measured using several methods, including spectrophotometric and fluorometric protocols. This paper introduces a fluorometric method for specifically quantifying MPO activity based on the H2O2-dependent oxidation of thiamine. We optimized this new method using the robust statistical approach response surface methodology (RSM) and Box Benken Design (BBD). We extensively examined the effects of several experimental parameters using the RSM methodology and determined the best conditions for accurate and sensitive MPO activity measurement. The optimal conditions were determined using the analysis of variance (ANOVA) for second-order polynomial equations. The resulting F-value (4.86) indicated that the model was significant. However, the lack-of-fitness F-value (1.79) suggested it did not differ significantly from the corresponding p-value. The greatest MPO activity (30 ± 2 U L−1) was obtained under optimum conditions, which were 1000 µM of H2O2, 10 min incubation time, and 1000 µM of thiamine. Our results suggest that this advanced fluorometric method has significant accuracy, sensitivity, and linearity up to 60 IU. The new and standard colorimetric methods also showed a good correlation. These results indicate that the new fluorometric method can be dependable and efficient for assessing MPO activity. The new method is characterized by excellent accuracy, sensitivity, and linearity, making it a valuable protocol for researchers and clinicians interested in assessing MPO activity.

Optimization and sensitivity analysis on axisymmetric motile microorganism flow of viscoelastic nanofluid over a spinning circular disk with a central composite model

A robust statistical analysis is presented for the axisymmetric motile microorganism flow over a spinning disk. The bio-convective flow of non-Newtonian nanofluid over a circular stretching rotating disk is considered. As a novelty, the influence of thermal radiation and binary chemical reaction and activation energy are incorporated to enhance the study. The requisite transformation rules are adopted to get a suitable non-dimensional model of the problem under consideration. Further, traditional numerical technique with the help of shooting-based Runge-Kutta fourth-order is employed subject to the suitable surface conditions in the proposed model. The characteristics of these parameters affecting the flow phenomena are depicted through graphs. According to the physical interpretation of the results, the non-Newtonian parameter, in conjunction with the stretching parameter, does have a significant role in elevating the radial velocity profile. A faster chemical reaction rate causes a decrease in fluid concentration, and faster energy transmission is caused by thermal radiation. The higher degree of thermal radiation and the greater Biot number are associated with a considerable increase in the rate of heat transfer and also boost the temperature of the nanofluid. The rate of heat transmission is extremely sensitive to the Biot number. A robust statistical approach namely Response Surface Methodology based on a face-centered Central Composite Design model is prescribed for the optimized heat transfer properties using various factors. The regression analysis is conducted through analysis of variance and the sensitive analysis of these factors is also exhibited. The current numerical analysis has relevance in the bioengineering of organisms movement, gas turbines, processing of food, enzyme bio-metals, and oil recovery.

Downscaling ERA5 wind speed data: a machine learning approach considering topographic influences

Energy system modeling and analysis can provide comprehensive guidelines to integrate renewable energy sources into the energy system. Modeling renewable energy potential, such as wind energy, typically involves the use of wind speed time series in the modeling process. One of the most widely utilized datasets in this regard is ERA5, which provides global meteorological information. Despite its broad coverage, the coarse spatial resolution of ERA5 data presents challenges in examining local-scale effects on energy systems, such as battery storage for small-scale wind farms or community energy systems. In this study, we introduce a robust statistical downscaling approach that utilizes a machine learning approach to improve the resolution of ERA5 wind speed data from around 31 km × 31 km to 1 km × 1 km. To ensure optimal results, a comprehensive preprocessing step is performed to classify regions into three classes based on the quality of ERA5 wind speed estimates. Subsequently, a regression method is applied to each class to downscale the ERA5 wind speed time series by considering the relationship between ERA5 data, observations from weather stations, and topographic metrics. Our results indicate that this approach significantly improves the performance of ERA5 wind speed data in complex terrain. To ensure the effectiveness and robustness of our approach, we also perform thorough evaluations by comparing our results with the reference dataset COSMO-REA6 and validating with independent datasets.

Robust estimates of solute diffusivity in polymers for predicting patient exposure to medical device leachables

AbstractMedical devices often include polymeric components, which contain additives or contaminants that may leach into patients and pose a health risk. Previously, we proposed a mass transport model that conservatively estimates the leaching kinetics and only requires the solute's diffusion coefficient in the polymer, , to be specified. Because determining experimentally is time‐consuming, we also parameterized empirical models to estimate worst‐case values using only the solute molecular weight, . These models were based on a modest database and were limited to 19 polymers and larger solutes ( g/mol). Here, we assemble a much larger database, which enables us to construct more accurate models using a robust statistical approach, expanding the coverage to 50 device‐relevant polymers and smaller solutes ( g/mol). Then, we demonstrate several applications of these bounds, including modeling the release kinetics. Finally, we observe an interesting phenomenon, a discontinuous drop in of up to 25,000× for solutes with g/mol in glassy polymers. Using molecular simulations and cheminformatics tools, we propose a novel definition of the effective diameter of free volume channels in polymers, and we show that solutes larger than this channel size diffuse much more slowly.

The impact of csr practice on employee engagement and intention to stay in the hotel industry: An ordered logit model approach

This study examines the impact of CSR practices on employee work engagement and intention to stay in the hotel industry during the COVID-19 pandemic. It draws on Social Exchange Theory and Stakeholder Theory to analyze the effects of CSR on employee outcomes. The empirical analysis utilizes an ordered logit model and Likert scale data collected from hotel workers. The findings contribute to understanding the role of CSR in attracting and retaining talented employees and building sustainable tourism economies. By employing the ordered logit model, this study provides a robust statistical approach to uncover the relationships between variables. The findings have implications for hotel industry practitioners and policymakers in promoting CSR practices and improving employee engagement and retention.

Robust Statistical Approach for Determination of Graphite Nitridation Using Bayesian Model Comparison

A better estimation of surface reaction efficiency of semiconductor-grade graphite with atomic nitrogen, as well as the calibration error are calculated using Bayesian updating based on experimental data. Compared with a conventional deterministic model, the stochastic model approach is a powerful tool in the sense that the model is capable of taking into account underlying error correlations among the data quantities. In this paper, we investigate four different stochastic models (called “stochastic system model classes” herein) corresponding to different descriptions of modeling and measurement error structures, given one deterministic physical model. These stochastic system model classes differ in the covariance matrix structure that is used in the uncertainty model to represent uncertainties associated with the physical model and experimental measurements. For each model class, Bayesian inference is used to estimate the posterior probabilities of the physical model parameters as well as of the stochastic model parameters. Model comparison and selection are then applied based on two measures including Bayesian evidence and Bayesian information criterion, as well as the deviance information criterion. Both measures suggest the stochastic model class, which considers that a correlation between errors in two data quantities among different data points is the most plausible. With the stochastic model class, the range of uncertainty in surface reaction efficiency is estimated to be about two orders of magnitude at .

Collective dynamics analysis based on the multiplex network method to unravel the backbone of fluctuations in groundwater level data

Collective behavior of groundwater level (GWL) in the Tabriz plain, northwest Iran, was modeled in the framework of complex networks. The GWL is considerably influenced by the lack of recharge due to drought in recent years and the increasing water demand of the industrial city of Tabriz. In this order, convergent cross-mapping (CCM) method was utilized to infer the causal relations by adopting a robust statistical approach based on hypothesis testing. Reconstructing structure is a challenge in network-based approaches to capture the dynamical variability of the measurements.This study aims to construct functional connectivity for a better understanding of informational flows among the wells. Multiplex network (MUX) was used as a set of coupled networks through interconnected layers to structure the information obtained from collective dynamics and time-reversal. The proposed procedure was evaluated by performing a systematic analysis of random network (RN) as a well-known model to consider the topological characteristics. The influence of connectivity reconstruction on network topology was also investigated by the generated experiments for different numbers of the dynamical units and lengths of the simulations at each unit. The MUX was constructed from the GWL for monthly observations over 15 years (2001–2017). Afterwards, the time-series was divided into three categories to infer the network connections in different time periods for various sets of the monitoring wells.The analysis indicates that unphysical connections are reduced by increasing the number of the coupling units as a desirable feature for detecting the underlying connectivity. The MUX is employed with the proposed statistical approach to quantitatively unravel the backbone of fluctuations in the GWL whose indirect causal relations form the skeleton. The MUX dynamics efficiently determines the underlying processes and identifies spatio-temporal patterns of the GWL fluctuations in two layers of the constructed networks.

Protein family neighborhood analyzer-ProFaNA.

Functionally related genes are well known to be often grouped in close vicinity in the genomes, particularly in prokaryotes. Notwithstanding the diverse evolutionary mechanisms leading to this phenomenon, it can be used to predict functions of uncharacterized genes. Here, we provide a simple but robust statistical approach that leverages the vast amounts of genomic data available today. Considering a protein domain as a functional unit, one can explore other functional units (domains) that significantly often occur within the genomic neighborhoods of the queried domain. This analysis can be performed across different taxonomic levels. Provisions can also be made to correct for the uneven sampling of the taxonomic space by genomic sequencing projects that often focus on large numbers of very closely related strains, e.g., pathogenic ones. To this end, an optional procedure for averaging occurrences within subtaxa is available. Several examples show this approach can provide useful functional predictions for uncharacterized gene families, and how to combine this information with other approaches. The method is made available as a web server at http://bioinfo.sggw.edu.pl/neighborhood_analysis.