Impact of Limosilactobacillus fermentum probiotic treatment on gut microbiota composition in sahiwal calves with rotavirus diarrhea: A 16S metagenomic analysis study".

BACKGROUND: Diarrhea poses a major threat to bovine calves leading to mortality and economic losses. Among the causes of calf diarrhea, bovine rotavirus is a major etiological agent and may result in dysbiosis of gut microbiota. The current study was designed to investigate the effect of probiotic Limosilactobacillus fermentum (Accession No.OR504458) on the microbial composition of rotavirus-infected calves using 16S metagenomic analysis technique. Screening of rotavirus infection in calves below one month of age was done through clinical signs and Reverse Transcriptase PCR. The healthy calves (n = 10) were taken as control while the infected calves (n = 10) before treatment was designated as diarrheal group were treated with Probiotic for 5 days. All the calves were screened for the presence of rotavirus infection on each day and fecal scoring was done to assess the fecal consistency. Infected calves after treatment were designated as recovered group. Fecal samples from healthy, recovered and diarrheal (infected calves before sampling) were processed for DNA extraction while four samples from each group were processed for 16S metagenomic analysis using Illumina sequencing technique and analyzed via QIIME 2. RESULTS: The results show that Firmicutes were more abundant in the healthy and recovered group than in the diarrheal group. At the same time Proteobacteria was higher in abundance in the diarrheal group. Order Oscillospirales dominated healthy and recovered calves and Enterobacterials dominated the diarrheal group. Alpha diversity indices show that diversity indices based on richness were higher in the healthy group and lower in the diarrheal group while a mixed pattern of clustering between diarrheal and recovered groups samples in PCA plots based on beta diversity indices was observed. CONCLUSION: It is concluded that probiotic Limosilactobacillus Fermentum N-30 ameliorate the dysbiosis caused by rotavirus diarrhea and may be used to prevent diarrhea in pre-weaned calves after further exploration.

Estimating Transcriptome Diversity and Specialization in Capsicum annuum L.

Chili pepper fruits of the genus Capsicum represent excellent experimental models to study the growth, development, and ripening processes in a non-climacteric species at the physiological, biochemical, and molecular levels. Fruit growth, development, and ripening involve a complex, harmonious, and finely controlled regulation of gene expression. The purpose of this study was to estimate the changes in transcriptome diversity and specialization, as well as gene specificities during fruit development in this crop, and to illustrate the advantages of estimating these parameters. To achieve these aims, we programmed and made publicly available an R package. In this study, we applied these methods to a set of 179 RNA-Seq libraries from a factorial experiment that includes 12 different genotypes at various stages of fruit development. We found that the diversity of the transcriptome decreases linearly from the flower to the mature fruit, while its specialization follows a complex and non-linear behavior during this process. Additionally, by defining sets of genes with different degrees of specialization and applying Gene Ontology enrichment analysis, we identified processes, functions, and components that play a central role in particular fruit development stages. In conclusion, the estimation of diversity, specialization, and specificity summarizes the global properties of the transcriptomes, providing insights that are difficult to achieve by other means.

An innovative method based on Gaussian cloud distribution and sample information richness for eutrophication assessment of Yangtze's lakes and reservoirs under uncertainty.

The precise assessment of a water body's eutrophication status is essential for making informed decisions in water environment management. However, conventional approaches frequently fail to consider the randomness, fuzziness, and inherent hidden information of water quality indicators. These would result in an unreliable assessment. An enhanced method was proposed for the eutrophication assessment under uncertainty in this study. The multi-dimension gaussian cloud distribution was introduced to capture the randomness and fuzziness. The Shannon entropy based on various sample size and trophic levels was proposed to maximize valuable information hidden in the datasets. Twenty-seven significant lakes and reservoirs located in the Yangtze River Basin were selected to demonstrate the proposed method. The sensitivity and consistency were used to evaluate the accuracy of the proposed method. Results indicate that the proposed method has the capability to effectively assess the eutrophication status of lakes and reservoirs under uncertainty and that it has a better sensitivity since it can identify more than 33-50% trophic levels compared to the traditional methods. Further scenario experiments analysis revealed that the sample information richness, i.e., sample size and the number of trophic levels is of great significance to the accuracy/robustness of the method. Moreover, a sample size of 60 can offer the most favorable balance between accuracy/robustness and the monitoring expenses. These findings are crucial to optimizing the eutrophication assessment.

Quasi-Equilibrium States and Phase Transitions in Biological Evolution.

We developed a macroscopic description of the evolutionary dynamics by following the temporal dynamics of the total Shannon entropy of sequences, denoted by S, and the average Hamming distance between them, denoted by H. We argue that a biological system can persist in the so-called quasi-equilibrium state for an extended period, characterized by strong correlations between S and H, before undergoing a phase transition to another quasi-equilibrium state. To demonstrate the results, we conducted a statistical analysis of SARS-CoV-2 data from the United Kingdom during the period between March 2020 and December 2023. From a purely theoretical perspective, this allowed us to systematically study various types of phase transitions described by a discontinuous change in the thermodynamic parameters. From a more-practical point of view, the analysis can be used, for example, as an early warning system for pandemics.

(Non)Resonance Bonds in Molecular Dynamics Simulations: A Case Study concerning C60 Fullerenes.

In the case of certain chemical compounds, especially organic ones, electrons can be delocalized between different atoms within the molecule. These resulting bonds, known as resonance bonds, pose a challenge not only in theoretical descriptions of the studied system but also present difficulties in simulating such systems using molecular dynamics methods. In computer simulations of such systems, it is often common practice to use fractional bonds as an averaged value across equivalent structures, known as a resonance hybrid. This paper presents the results of the analysis of five forms of C60 fullerene polymorphs: one with all bonds being resonance, three with all bonds being integer (singles and doubles in different configurations), one with the majority of bonds being integer (singles and doubles), and ten bonds (within two opposite pentagons) valued at one and a half. The analysis involved the Shannon entropy value for bond length distributions and the eigenfrequency of intrinsic vibrations (first vibrational mode), reflecting the stiffness of the entire structure. The maps of the electrostatic potential distribution around the investigated structures are presented and the dipole moment was estimated. Introducing asymmetry in bond redistribution by incorporating mixed bonds (integer and partial), in contrast to variants with equivalent bonds, resulted in a significant change in the examined observables.

Choice of Metric Divergence in Genome Sequence Comparison.

The paper introduces a novel probability descriptor for genome sequence comparison, employing a generalized form of Jensen-Shannon divergence. This divergence metric stems from a one-parameter family, comprising fractions up to a maximum value of half. Utilizing this metric as a distance measure, a distance matrix is computed for the new probability descriptor, shaping Phylogenetic trees via the neighbor-joining method. Initial exploration involves setting the parameter at half for various species. Assessing the impact of parameter variation, trees drawn at different parameter values (half, one-fourth, one-eighth). However, measurement scales decrease with parameter value increments, with higher similarity accuracy corresponding to lower scale values. Ultimately, the highest accuracy aligns with the maximum parameter value of half. Comparative analyses against previous methods, evaluating via Symmetric Distance (SD) values and rationalized perception, consistently favor the present approach's results. Notably, outcomes at the maximum parameter value exhibit the most accuracy, validating the method's efficacy against earlier approaches.

COVID-19 detection in lung CT slices using Brownian-butterfly-algorithm optimized lightweight deep features.

Several deep-learning assisted disease assessment schemes (DAS) have been proposed to enhance accurate detection of COVID-19, a critical medical emergency, through the analysis of clinical data. Lung imaging, particularly from CT scans, plays a pivotal role in identifying and assessing the severity of COVID-19 infections. Existing automated methods leveraging deep learning contribute significantly to reducing the diagnostic burden associated with this process. This research aims in developing a simple DAS for COVID-19 detection using the pre-trained lightweight deep learning methods (LDMs) applied to lung CT slices. The use of LDMs contributes to a less complex yet highly accurate detection system. The key stages of the developed DAS include image collection and initial processing using Shannon's thresholding, deep-feature mining supported by LDMs, feature optimization utilizing the Brownian Butterfly Algorithm (BBA), and binary classification through three-fold cross-validation. The performance evaluation of the proposed scheme involves assessing individual, fused, and ensemble features. The investigation reveals that the developed DAS achieves a detection accuracy of 93.80% with individual features, 96% accuracy with fused features, and an impressive 99.10% accuracy with ensemble features. These outcomes affirm the effectiveness of the proposed scheme in significantly enhancing COVID-19 detection accuracy in the chosen lung CT database.

Mathematical constraints on a family of biodiversity measures via connections with Rényi entropy.

The Hill numbers are statistics for biodiversity measurement in ecological studies, closely related to the Rényi and Shannon entropies from information theory. Recent developments in the mathematics of diversity in the setting of population genetics have produced mathematical constraints that characterize how standard measures depend on the highest-frequency class in a discrete probability distribution. Here, we apply these constraints to diversity statistics in ecology, focusing on the Hill numbers and the Rényi and Shannon entropies. The mathematical bounds can shift perspectives on the diversities of communities, in that when upper and lower bounds on Hill numbers are evaluated in a classic butterfly example, Hill numbers that are initially larger in one community switch positions-so that associated normalized Hill numbers are instead smaller than those of the other community. The new bounds hence add to the tools available for interpreting a commonly used family of statistics for ecological data.

Arterial Pulse Wave Velocity Signal Reconstruction Using Low Sampling Rates.

Pulse Wave Velocity (PWV) analysis is valuable for assessing arterial stiffness and cardiovascular health and potentially for estimating blood pressure cufflessly. However, conventional PWV analysis from two transducers spaced closely poses challenges in data management, battery life, and developing the device for continuous real-time applications together along an artery, which typically need data to be recorded at high sampling rates. Specifically, although a pulse signal consists of low-frequency components when used for applications such as determining heart rate, the pulse transit time for transducers near each other along an artery takes place in the millisecond range, typically needing a high sampling rate. To overcome this issue, in this study, we present a novel approach that leverages the Nyquist-Shannon sampling theorem and reconstruction techniques for signals produced by bioimpedance transducers closely spaced along a radial artery. Specifically, we recorded bioimpedance artery pulse signals at a low sampling rate, reducing the data size and subsequently algorithmically reconstructing these signals at a higher sampling rate. We were able to retain vital transit time information and achieved enhanced precision that is comparable to the traditional high-rate sampling method. Our research demonstrates the viability of the algorithmic method for enabling PWV analysis from low-sampling-rate data, overcoming the constraints of conventional approaches. This technique has the potential to contribute to the development of cardiovascular health monitoring and diagnosis using closely spaced wearable devices for real-time and low-resource PWV assessment, enhancing patient care and cardiovascular disease management.

Directed Network Comparison Using Motifs.

Analyzing and characterizing the differences between networks is a fundamental and challenging problem in network science. Most previous network comparison methods that rely on topological properties have been restricted to measuring differences between two undirected networks. However, many networks, such as biological networks, social networks, and transportation networks, exhibit inherent directionality and higher-order attributes that should not be ignored when comparing networks. Therefore, we propose a motif-based directed network comparison method that captures local, global, and higher-order differences between two directed networks. Specifically, we first construct a motif distribution vector for each node, which captures the information of a node's involvement in different directed motifs. Then, the dissimilarity between two directed networks is defined on the basis of a matrix, which is composed of the motif distribution vector of every node and the Jensen-Shannon divergence. The performance of our method is evaluated via the comparison of six real directed networks with their null models, as well as their perturbed networks based on edge perturbation. Our method is superior to the state-of-the-art baselines and is robust with different parameter settings.

Exploring the fidelity of satellite precipitation products in capturing flood risks: A novel framework incorporating hazard and vulnerability dimensions over a sensitive coastal multi-hazard catchment.

Complexities involved in flood risks over global coastal multi-hazard catchments are a severe concern for vulnerable communities, infrastructure, and the environment. Data scarcity in these regions often hinders our holistic understanding of flood risks, especially when socio-economic and physical vulnerabilities are involved. The extent to which Satellite Precipitation Products (SPPs), which are looked upon as alternatives to ground-based observations, can influence flood risk dynamics remains unexplored. In an attempt to answer the most riveted questions in flood management literature, this study, for the first time, explores the suitability of two competent SPPs, i.e., CHIRPS v2.0 and PERSIANN-CDR, in multi-hazard flood risk mapping. The proposed framework is demonstrated over the sensitive flood-prone deltaic stretches of the Lower Mahanadi River Basin (India). A computationally efficient MIKE+ 1D2D hydrodynamic model is developed to account for the wave propagation of concurrent flood drivers and generate high-resolution flood hazard maps for three disastrous historical flood events (July 2019, September 2020, and August 2022). To understand the hidden characteristics of vulnerability, a comprehensive set of 24 physical and socio-economic indicators is considered in a Shannon-entropy and TOPSIS framework. The variations in flood risk from both SPPs at the finest administrative scale are represented using the novel concept of Bivariate Choropleth, which portrays the marginal and compound contributions of hazard and vulnerability. A superlative performance of CHIRPS v2.0 over PERSIANN-CDR was observed in capturing hydro-climatological behaviors. CHIRPS v2.0-derived flood hazards were found analogous to the SAR-derived maps for all the three events. >70 % of villages display large disparities in flood risk, thereby affirming the role of appropriate SPPs towards efficient flood management. The observations from the study add vital information to the existing flood management policies, especially over resource-constrained regions in low and middle-income nations.

Shannon entropy of performance metrics to choose the best novel hybrid algorithm to predict groundwater level (case study: Tabriz plain, Iran).

Predicting groundwater level (GWL) fluctuations, which act as a reserve water reservoir, particularly in arid and semi-arid climates, is vital in water resources management and planning. Within the scope of current research, a novel hybrid algorithm is proposed for estimating GWL values in the Tabriz plain of Iran by combining the artificial neural network (ANN) algorithm with newly developed nature-inspired Coot and Honey Badger metaheuristic optimization algorithms. Various combinations of meteorological data such as temperature, evaporation, and precipitation, previous GWL values, and the month and year values of the data were used to evaluate the algorithm's success. Furthermore, the Shannon entropy of model performance was assessed according to 44 different statistical indicators, classified into two classes: accuracy and error. Hence, based on the high value of Shannon entropy, the best statistical indicator was selected. The results of the best model and the best scenario were analyzed. Results indicated that value of Shannon entropy is higher for the accuracy class than error class. Also, for accuracy and error class, respectively, Akaike information criterion (AIC) and residual sum of squares (RSS) indexes with the highest entropy value which is equal to 12.72 and 7.3 are the best indicators of both classes, and Legate-McCabe efficiency (LME) and normalized root mean square error-mean (NRMSE-Mean) indexes with the lowest entropy value which is equal to 3.7 and - 8.3 are the worst indicators of both classes. According to the evaluation best indicator results in the testing phase, the AIC indicator value for HBA-ANN, COOT-ANN, and the standalone ANN models is equal to - 344, - 332.8, and - 175.8, respectively. Furthermore, it was revealed that the proposed metaheuristic algorithms significantly improve the performance of the standalone ANN model and offer satisfactory GWL prediction results. Finally, it was concluded that the Honey Badger optimization algorithm showed superior results than the Coot optimization algorithm in GWL prediction.

Optimal entropic properties of SARS-CoV-2 RNA sequences.

The reaction of the scientific community against the COVID-19 pandemic has generated a huge (approx. 106 entries) dataset of genome sequences collected worldwide and spanning a relatively short time window. These unprecedented conditions together with the certain identification of the reference viral genome sequence allow for an original statistical study of mutations in the virus genome. In this paper, we compute the Shannon entropy of every sequence in the dataset as well as the relative entropy and the mutual information between the reference sequence and the mutated ones. These functions, originally developed in information theory, measure the information content of a sequence and allows us to study the random character of mutation mechanism in terms of its entropy and information gain or loss. We show that this approach allows us to set in new format known features of the SARS-CoV-2 mutation mechanism like the CT bias, but also to discover new optimal entropic properties of the mutation process in the sense that the virus mutation mechanism track closely theoretically computable lower bounds for the entropy decrease and the information transfer.

The influence of resolution on the predictive power of spatial heterogeneity measures as biomarkers of liver fibrosis.

Spatial heterogeneity of cells in liver biopsies can be used as biomarker for disease severity of patients. This heterogeneity can be quantified by non-parametric statistics of point pattern data, which make use of an aggregation of the point locations. The method and scale of aggregation are usually chosen ad hoc, despite values of the aforementioned statistics being heavily dependent on them. Moreover, in the context of measuring heterogeneity, increasing spatial resolution will not endlessly provide more accuracy. The question then becomes how changes in resolution influence heterogeneity indicators, and subsequently how they influence their predictive abilities. In this paper, cell level data of liver biopsy tissue taken from chronic Hepatitis B patients is used to analyze this issue. Firstly, Morisita-Horn indices, Shannon indices and Getis-Ord statistics were evaluated as heterogeneity indicators of different types of cells, using multiple resolutions. Secondly, the effect of resolution on the predictive performance of the indices in an ordinal regression model was investigated, as well as their importance in the model. A simulation study was subsequently performed to validate the aforementioned methods. In general, for specific heterogeneity indicators, a downward trend in predictive performance could be observed. While for local measures of heterogeneity a smaller grid-size is outperforming, global measures have a better performance with medium-sized grids. In addition, the use of both local and global measures of heterogeneity is recommended to improve the predictive performance.

Effects of a prebiotic formulation on the composition of the faecal microbiota of people with functional constipation.

PURPOSE: Prebiotics are defined as substances which selectively promote beneficial gut microbes leading to a health benefit for the host. Limited trials have been carried out investigating their effect on the microbiota composition of individuals afflicted by functional constipation with equivocal outcomes. In a 21-day randomised, controlled clinical trial involving 61 adults with functional constipation, a prebiotic formulation with partially hydrolysed guar gum and acacia gum as its main ingredients, significantly increased complete spontaneous bowel motions in the treatment group. This follow-up exploratory analysis investigated whether the prebiotic was associated with changes to the composition, richness, and diversity of the faecal microbiota. METHODS: Participants provided a faecal specimen at baseline and on day 21 of the intervention period. Whole genome metagenomic shotgun sequencing comprehensively assessed taxonomic and functional composition of the microbiota. RESULTS: Linear mixed effects regression models adjusted for potential confounders showed a significant reduction in species richness of 28.15 species (95% CI - 49.86, - 6.43) and Shannon diversity of 0.29 units (95% CI - 0.56, - 0.02) over the trial period in the prebiotic group. These changes were not observed in the control group, and functional composition was unchanged in both groups. CONCLUSION: In adults with functional constipation, the intake of a prebiotic formulation was associated with a decline of species richness and Shannon diversity. Further research regarding the associations between prebiotics and the composition and function of the gut microbiota is warranted.

Quantitative assessment of pneumothorax by using Shannon entropy of lung ultrasound M-mode image and diaphragmatic excursion based on automated measurement.

Background: Lung ultrasound (LUS) and diaphragm ultrasound (DUS) are the appropriate modalities for conservative observation to those patients who are with stable pneumothorax, as well as for the timely detection of life-threatening pneumothorax at any location, due to they are portable, real-time, relatively cost effective, and most important, without radiation exposure. The absence of lung sliding on LUS M-mode images and the abnormality of diaphragmatic excursion (DE) on DUS M-mode images are the most common and novel diagnostic criteria for pneumothorax, respectively. However, visual inspection of M-mode images remains subjective and quantitative analysis of LUS and DUS M-mode images are required. Methods: Shannon entropy of LUS M-mode image (ShanEnLM) and DE based on the automated measurement (DEAM) are adapted to the objective pneumothorax diagnoses and the severity quantifications in this study. Mild, moderate, and severe pneumothoraces were induced in 24 male New Zealand rabbits through insufflation of room air (5, 10 and 15, and 25 and 40 mL/kg, respectively) into their pleural cavities. In vivo intercostal LUS and subcostal DUS M-mode images were acquired using a point-of-care system for estimating ShanEnLM and DEAM. Results: ShanEnLM and DEAM as functions of air insufflation volumes exhibited U-shaped curves and were exponentially decreasing, respectively. Either ShanEnLM or DEAM had areas under the receiver operating characteristic curves [95% confidence interval (CI)] of 1.0000 (95% CI: 1.0000-1.0000), 0.9833 (95% CI: 0.9214-1.0000), and 0.9407 (95% CI: 0.8511-1.0000) for differentiating between normal and mild pneumothorax, mild and moderate pneumothoraces, and moderate and severe pneumothoraces, respectively. Conclusions: Our findings imply that the combination of ShanEnLM and DEAM give the promising potential for pneumothorax quantitative diagnosis.

Entropy Estimators for Markovian Sequences: A Comparative Analysis.

Entropy estimation is a fundamental problem in information theory that has applications in various fields, including physics, biology, and computer science. Estimating the entropy of discrete sequences can be challenging due to limited data and the lack of unbiased estimators. Most existing entropy estimators are designed for sequences of independent events and their performances vary depending on the system being studied and the available data size. In this work, we compare different entropy estimators and their performance when applied to Markovian sequences. Specifically, we analyze both binary Markovian sequences and Markovian systems in the undersampled regime. We calculate the bias, standard deviation, and mean squared error for some of the most widely employed estimators. We discuss the limitations of entropy estimation as a function of the transition probabilities of the Markov processes and the sample size. Overall, this paper provides a comprehensive comparison of entropy estimators and their performance in estimating entropy for systems with memory, which can be useful for researchers and practitioners in various fields.

An Information Theoretic Condition for Perfect Reconstruction.

A new information theoretic condition is presented for reconstructing a discrete random variable X based on the knowledge of a set of discrete functions of X. The reconstruction condition is derived from Shannon's 1953 lattice theory with two entropic metrics of Shannon and Rajski. Because such a theoretical material is relatively unknown and appears quite dispersed in different references, we first provide a synthetic description (with complete proofs) of its concepts, such as total, common, and complementary information. The definitions and properties of the two entropic metrics are also fully detailed and shown to be compatible with the lattice structure. A new geometric interpretation of such a lattice structure is then investigated, which leads to a necessary (and sometimes sufficient) condition for reconstructing the discrete random variable X given a set {X1,…,Xn} of elements in the lattice generated by X. Intuitively, the components X1,…,Xn of the original source of information X should not be globally "too far away" from X in the entropic distance in order that X is reconstructable. In other words, these components should not overall have too low of a dependence on X; otherwise, reconstruction is impossible. These geometric considerations constitute a starting point for a possible novel "perfect reconstruction theory", which needs to be further investigated and improved along these lines. Finally, this condition is illustrated in five specific examples of perfect reconstruction problems: the reconstruction of a symmetric random variable from the knowledge of its sign and absolute value, the reconstruction of a word from a set of linear combinations, the reconstruction of an integer from its prime signature (fundamental theorem of arithmetic) and from its remainders modulo a set of coprime integers (Chinese remainder theorem), and the reconstruction of the sorting permutation of a list from a minimal set of pairwise comparisons.

Functional network properties in schizophrenia and bipolar disorder assessed with high-density electroencephalography.

BACKGROUND: The study of the cortical functional network properties in schizophrenia (SZ) may benefit from the use of graph theory parameters applied to high-density electroencephalography (EEG). Connectivity Strength (CS) assesses global synchrony of the network, and Shannon Graph Complexity (SGC) summarizes the network distribution of link weights and allows distinguishing between primary and secondary pathways. Their joint use may help in understanding the underpinnings of the functional network hyperactivation and task-related hypomodulation previously described in psychoses. METHODS: We used 64-sensor EEG recordings during a P300 oddball task in 128 SZ patients (96 chronic, CR, and 32 first episodes, FE), as well as 46 bipolar disorder (BD) patients, and 92 healthy controls (HC). Pre-stimulus and modulation (task-response minus pre-stimulus windows values) of CS and SGC were assessed in the theta band (4-8 Hz) and the broadband (4-70 Hz). RESULTS: Compared to HC, SZ patients (CR and FE) showed significantly higher pre-stimulus CS values in the broadband, and both SZ and BD patients showed lower theta-band CS modulation. SGC modulation values, both theta-band and broadband, were also abnormally reduced in CR patients. Statistically significant relationships were found in the theta band between SGC modulation and both CS pre-stimulus and modulation values in patients. CS altered measures in patients were additionally related to their cognitive outcome and negative symptoms. A primary role of antipsychotics in these results was ruled out. CONCLUSIONS: Our results linking SGC and CS alterations in psychotic patients supported a hyperactive and hypomodulatory network mainly involving connections in secondary pathways.

Comparison of electrogram characteristics in persistent atrial fibrillation.

INTRODUCTION: Multiple analysis techniques evaluate electrograms during atrial fibrillation (AF), but none have been established to guide catheter ablation. This study compares electrogram properties recorded from multiple right (RA) and left atrial (LA) sites. METHODS: Multisite LA/RA mapping (281 ± 176/239 ± 166 sites/patient) was performed in 42 patients (30 males, age 63 ± 9 years) undergoing first (n = 32) or redo-AF ablation (n = 10). All electrogram recordings were visually reviewed and artifactual signals were excluded leaving a total of 21 846 for analysis. Electrogram characteristics evaluated were cycle length (CL), amplitude, Shannon's entropy (ShEn), fractionation interval, dominant frequency, organizational index, and cycle length of most recurrent morphology (CLR ) from morphology recurrence plot analysis. RESULTS: Electrogram characteristics were correlated to each other. All pairwise comparisons were significant (p < .001) except for dominant frequency and CLR (p = .59), and amplitude and dominant frequency (p = .38). Only ShEn and fractionation interval demonstrated a strong negative correlation (r = -.94). All other pairwise comparisons were poor to moderately correlated. The relationships are highly conserved among patients, in the RA versus LA, and in those undergoing initial versus redo ablations. Antiarrhythmic drug therapy did not have a significant effect on electrogram characteristics, except minimum ShEn. Electrogram characteristics associated with ablation outcome were shorter minimum CLR , lower minimum ShEn, and longer mimimum CL. There was minimal overlap between the top 10 sites identified by one electrogram characteristic and the top 10 sites identified by the other 10 characteristics. CONCLUSION: Multiple techniques can be employed for electrogram analysis in AF. In this analysis of eight different electrogram characteristics, seven were poorly to moderately correlated and do not identify similar locations. Only some characteristics were predictive of ablation outcome. Further studies to consider electrogram properties, perhaps in combination, for categorizing and/or mapping AF are warranted.