Information Metrics Research Articles

Regularized entanglement entropy of electron-positron scattering with a witness photon

Regularized quantum information metrics are calculated for the scattering process e−e+→γ,Z→μ−μ+ that has a witness photon entangled with the initial electron-positron state. Unitarity implies the correct regularization of divergences that appear in both the final density matrix and von Neumann entanglement entropies. The entropies are found to quantify uncertainty or randomness. The variation of information, entanglement entropy, and correlation between the muon’s and witness photon’s helicities are found to convey equivalent information. The magnitude of the muon’s expected helicity rises (falls) as the helicity entropy falls (rises). Area, or the scattering cross section, is a source of entropy for the muon’s helicity entropy and momentum entropy. The muon’s differential angular entropy distribution is similar to the differential angular cross section distribution, capturing the forward-backward asymmetry at high center-of-mass energies. Published by the American Physical Society 2024

Early Estimation in Agile Software Development Projects: A Systematic Mapping Study

Estimating during the early stages is crucial for determining the feasibility and conducting the budgeting and planning of agile software development (ASD) projects. However, due to the characteristics of ASD and limited initial information, these estimates are often complicated and inaccurate. This study aims to systematically map the literature to identify the most used estimation techniques; the reasons for their selection; the input artifacts, predictors, and metrics associated with these techniques; as well as research gaps in early-stage estimations in ASD. This study was based on the guidelines proposed by Kitchenham for systematic literature reviews in software engineering; a review protocol was defined with research questions and criteria for the selection of empirical studies. Results show that data-driven techniques are preferred to reduce biases and inconsistencies of expert-driven techniques. Most selected studies do not mention input artifacts, and software size is the most commonly used predictor. Machine learning-based techniques use publicly available data but often contain records of old projects from before the agile movement. The study highlights the need for tools supporting estimation activities and identifies key areas for future research, such as evaluating hybrid approaches and creating datasets of recent projects with sufficient contextual information and standardized metrics.

Neutron tomography and image registration methods to study local physical deformations and attenuation variations in treated archaeological iron nail samples

This study presents a preliminary examination of the effects of environment changes post-excavation on heavily corroded archaeological Roman iron nails using neutron tomography and image registration techniques. Roman nails were exposed to either a high relative humidity environment, or fast thermal drying as primary experiments to show the power of this imaging technique to monitor and quantify the structural changes of corroded metal artifacts. This research employed a series of pre- and post-treatment tomography acquisitions (time-series) complemented by advanced image registration methods. Based on mutual information (MI) metrics, we performed rigid body and affine image registrations to meticulously account for sample repositioning challenges and variations in imaging parameters. Using non-affine local registration results, in a second step, we detected localized expansion and shrinkage in the samples attributable to imposed environmental changes. Specifically, we observed local shrinkage on the nail that was dried, mostly in their Transformed Medium (TM), the outer layer where corrosion products are cementing soil and sand particles. Conversely, the sample subjected to high relative humidity environment exhibited localized expansion, with varying degrees of change across different regions. This work highlights the efficacy of our registration techniques in accommodating manual removal or loss of extraneous material (loosely adhering soil and TM layers around the nails) post-initial tomography, successfully capturing local structural changes with high precision. Using differential analysis on the accurately registered samples we could also detect and volumetrically quantify the variation in moisture and detect changes in active corrosion sites (ACS) in the sample. These preliminary experiments allowed us to advance and optimize the application of a neutron tomography and image registration workflow for future, more advanced experiments such as humidity fluctuations, corrosion removal through micro-blasting, dechlorination and other stabilization treatments.

A Comprehensive Method for Example-Based Color Transfer with Holistic–Local Balancing and Unit-Wise Riemannian Information Gradient Acceleration

Color transfer, an essential technique in image editing, has recently received significant attention. However, achieving a balance between holistic color style transfer and local detail refinement remains a challenging task. This paper proposes an innovative color transfer method, named BHL, which stands for Balanced consideration of both Holistic transformation and Local refinement. The BHL method employs a statistical framework to address the challenge of achieving a balance between holistic color transfer and the preservation of fine details during the color transfer process. Holistic color transformation is achieved using optimal transport theory within the generalized Gaussian modeling framework. The local refinement module adjusts color and texture details on a per-pixel basis using a Gaussian Mixture Model (GMM). To address the high computational complexity inherent in complex statistical modeling, a parameter estimation method called the unit-wise Riemannian information gradient (uRIG) method is introduced. The uRIG method significantly reduces the computational burden through the second-order acceleration effect of the Fisher information metric. Comprehensive experiments demonstrate that the BHL method outperforms state-of-the-art techniques in both visual quality and objective evaluation criteria, even under stringent time constraints. Remarkably, the BHL method processes high-resolution images in an average of 4.874 s, achieving the fastest processing time compared to the baselines. The BHL method represents a significant advancement in the field of color transfer, offering a balanced approach that combines holistic transformation and local refinement while maintaining efficiency and high visual quality.

Neural encoding of melodic expectations in music across EEG frequency bands.

The human brain tracks regularities in the environment and extrapolates these to predict future events. Prior work on music cognition suggests that low-frequency (1-8Hz) brain activity encodes melodic predictions beyond the stimulus acoustics. Building on this work, we aimed to disentangle the frequency-specific neural dynamics linked to melodic prediction uncertainty (modelled as entropy) and prediction error (modelled as surprisal) for temporal (note onset) and content (note pitch) information. By using multivariate temporal response function (TRF) models, we re-analysed the electroencephalogram (EEG) from 20 subjects (10 musicians) who listened to Western tonal music. Our results show that melodic expectation metrics improve the EEG reconstruction accuracy in all frequency bands below the gamma range (< 30 Hz). Crucially, we found that entropy contributed more strongly to the reconstruction accuracy enhancement compared to surprisal in all frequency bands. Additionally, we found that the encoding of temporal, but not content, information metrics was not limited to low frequencies, rather it extended to higher frequencies (> 8Hz). An analysis of the TRF weights revealed that the temporal predictability of a note (entropy of note onset) may be encoded in the delta- (1-4Hz) and beta-band (12-30 Hz) brain activity prior to the stimulus, suggesting that these frequency bands associate with temporal predictions. Strikingly, we also revealed that melodic expectations selectively enhanced EEG reconstruction accuracy in the beta band for musicians, and in the alpha band (8-12 Hz) for non-musicians, suggesting that musical expertise influences the neural dynamics underlying predictive processing in music cognition.

Preprocessing and postprocessing analysis for hot-mix asphalt dynamic modulus experimental data

Dynamic modulus (|E*|) measurements of hot-mix asphalt (HMA) mixtures are critical for understanding material behavior but present significant challenges due to the complex testing procedures and precision required. While substantial efforts have focused on developing predictive models for |E*|, the critical role of experimental data preprocessing has been largely overlooked in the existing literature. This study addresses this gap by proposing a novel, comprehensive framework for both preprocessing and post-processing of dynamic modulus data. Leveraging the well-established ASU |E*| database and the NCHRP 1–40D Witczak prediction model as benchmarks, we introduce advanced empirical techniques, including probability distribution analysis, scatter and box plots, correlation coefficients, and mutual information metrics, to refine data quality and enhance the interpretability of predictive models. Our approach reveals new insights into the interaction of various input factors and |E*| values, leading to improved model robustness and reliability. The post-processing techniques further substantiate the predictive power of the Witczak model, yielding significant enhancements in accuracy and reliability. This research pioneers a standardized data preparation methodology that sets a new precedent in asphalt material engineering, offering a robust foundation for future |E*| modeling and analysis.

Detecting Fake Accounts on Instagram Using Machine Learning and Hybrid Optimization Algorithms

In this paper, we propose a hybrid method for detecting fake accounts on Instagram by using the Binary Grey Wolf Optimization (BGWO) and Particle Swarm Optimization (PSO) algorithms. By combining these two algorithms, we aim to leverage their complementary strengths and enhance the overall optimization performance. We evaluate the proposed hybrid method using four classifiers: Artificial Neural Network (ANN), K-Nearest Neighbor (KNN), Support Vector Machine (SVM), and Logistic Regression (LR). The dataset for the experiments contains 65,329 Instagram accounts. We extract features from each account, including profile information, posting behavior, and engagement metrics. The Binary Grey Wolf and Particle Swarm Optimizations, when combined to form a hybrid method (BGWOPSO), improved the performance in accurately detecting fake accounts on Instagram.

The Impact of Social Media and Socio-Cultural Attitudes toward Body Image on the Risk of Orthorexia among Female Football Players of Different Nationalities.

Orthorexia Nervosa (ON) is an emerging behavioral pattern characterized by an obsessive focus on healthy eating. Despite its prevalence, ON lacks formal diagnostic criteria in major classification systems like the DSM-5 and the ICD-10. This study aims to investigate the impact of socio-cultural attitudes towards body image and the role of social media on the risk of ON among female football players from Poland, Turkey, and India. This study hypothesizes that socio-cultural pressures and media usage significantly influence the risk of developing ON, particularly in cultures more exposed to Western beauty ideals. The study was conducted from May to August 2024, employing the Computer-Assisted Web Interview method. A total of 142 female football players aged 16-36 from Poland, Turkey, and India participated. Data were collected using a structured questionnaire that included demographic information and health metrics, the Socio-Cultural Attitudes Towards Appearance Questionnaire, and the Duesseldorf Orthorexia Scale. Statistical analyses included an ANOVA, the Kruskal-Wallis test, the chi-square test, and Pearson's correlation coefficient. The study found that nearly half of the participants were at risk of or presented with ON, with the highest prevalence being among Indian athletes. Statistically significant relationships were observed between the risk of ON and factors such as age, dietary exclusions, social media usage, and sources of nutritional information. However, no significant correlation was found between socio-cultural attitudes and the risk of ON, suggesting that other factors may play a more critical role. While socio-cultural pressures and media use are contributing factors to the risk of ON, psychological factors and individual behaviors appear to be equally, if not more, significant. This study highlights the importance of targeted educational programs and psychological support for young athletes, with a focus on promoting healthy dietary practices and positive body image perceptions across varying cultural contexts. Additionally, the results suggest the need for further research into the specific psychological and behavioral mechanisms underlying ON.

Assessing the quality and readability of online patient information: ENT UK patient information e-leaflets vs responses by a Generative Artificial Intelligence.

The evolution of artificial intelligence has introduced new ways to disseminate health information, including natural language processing models like ChatGPT. However, the quality and readability of such digitally-generated information remains understudied. This study is the first to compare the quality and readability of digitally-generated health information against leaflets produced by professionals. Patient information leaflets for five ENT UK leaflets and their corresponding ChatGPT responses were extracted from the Internet. Assessors with various degree of medical knowledge evaluated the content using the Ensuring Quality Information for Patients (EQIP) tool and readability tools including the Flesch-Kincaid Grade Level (FKGL). Statistical analysis was performed to identify differences between leaflets, assessors, and sources of information. ENT UK leaflets were of moderate quality, scoring a median EQIP of 23. Statistically significant differences in overall EQIP score were identified between ENT UK leaflets but ChatGPT responses were of uniform quality. Non-specialist doctors rated the highest EQIP scores while medical students scored the lowest. The mean readability of ENT UK leaflets was higher than ChatGPT responses. The information metrics of ENT UK leaflets were moderate and varied between topics. Equivalent ChatGPT information provided comparable content quality, but with reduced readability. ChatGPT patient information and professionally-produced leaflets had comparable content, but LLM content were required a higher reading age. With the increasing use of online health resources, this study highlights the need for a balanced approach that considers optimises both the quality and readability of patient education materials.

In situ observations of zooplankton show changes in abundance and swimming speed in response to hypoxia and acidification

AbstractZooplankton exhibit diverse swimming behaviors to reposition themselves in the water column, feed, find mates, and avoid predation. Environmental stressors that modify behavior can have cascading effects on population distributions and predator–prey interactions. Understanding zooplankton population dynamics is challenging, largely because traditional methods for quantifying zooplankton distributions are costly, limited in scope, and require extended analysis by trained analysts. We developed a novel methodology that combined remotely deployed camera systems, machine learning‐based identification of zooplankton, and video‐based tracking technology to quantify copepod and amphipod in situ swimming behavior in Hood Canal, WA, USA, a seasonally hypoxic and acidified fjord. Behavioral analysis showed copepods of all sizes swam on average 24% slower in stressful (hypoxic and acidified) waters relative to non‐stressful waters. Copepods exhibited less frequent escape responses in stressful waters, with a 68% decrease in the amount of time spent “jumping” for copepods 1–2 mm in length. Interestingly, abundances of small copepods increased in stressful waters, with 56% more 1–2 mm long copepods in stressful vs. non‐stressful conditions. In contrast, amphipods' average “darting” speeds did not differ between environmental conditions, but the abundance of amphipods significantly decreased in stressful waters relative to non‐stressful waters, suggesting avoidance of stressful conditions. Changes in swimming behavior are informative metrics in understanding ecosystem impacts of environmental stress because swimming speed has individual, population, and community‐level implications. Our results suggest that, among copepods, in situ behaviors may be useful proxies in monitoring the impacts of climate change on coastal ecosystems.

Secure multi-server coded caching

This paper considers multiple cache-enabled end-users connected to multiple servers through a linear network. We also prevent an external eavesdropper from obtaining any information about the original files in cache-aided networks. The eavesdropper does not exist in the placement phase. He can analyze and capture the multi-cast messages in the content delivery phase. He receives a single linear combination derived from each multi-cast message. Hence, we only consider the security of the delivery phase. Our work generalizes the secure caching problem with one server to a multi-server setup. A secure centralized multi-server coded caching scenario is considered, and closed-form coding delay and secret shared key storage expression are provided. Regarding our security guarantee, we show that the delivery phase does not reveal any information to the eavesdropper in terms of the mutual information metric. We analyze the system’s performance in terms of coding delay and guarantee the security of our scheme using the mutual information metric. Numerical evaluations verify that security incurs a negligible cost in terms of memory usage when the number of files and users increases, i.e., the secure and insecure bounds almost coincide. Also, we numerically show that our proposed scheme outperforms the secure coded caching problem with one server.

Beyond Linear Response: Equivalence between Thermodynamic Geometry and Optimal Transport.

A fundamental result of thermodynamic geometry is that the optimal, minimal-work protocol that drives a nonequilibrium system between two thermodynamic states in the slow-driving limit is given by a geodesic of the friction tensor, a Riemannian metric defined on control space. For overdamped dynamics in arbitrary dimensions, we demonstrate that thermodynamic geometry is equivalent to L^{2} optimal transport geometry defined on the space of equilibrium distributions corresponding to the control parameters. We show that obtaining optimal protocols past the slow-driving or linear response regime is computationally tractable as the sum of a friction tensor geodesic and a counterdiabatic term related to the Fisher information metric. These geodesic-counterdiabatic optimal protocols are exact for parametric harmonic potentials, reproduce the surprising nonmonotonic behavior recently discovered in linearly biased double well optimal protocols, and explain the ubiquitous discontinuous jumps observed at the beginning and end times.

The relationship between 25-hydroxy vitamin D and serum asprosin in patients with type 2 diabetes in the community.

This study aimed to investigate the link between 25-hydroxy vitamin D and serum asprosin in individuals with type 2 diabetes within the community. The goal was to provide a foundation for clinical interventions. Between November 2019 and July 2021, data from 463 patients with type 2 diabetes were consistently gathered at a community health service station in Southeast Shanxi Province. General information and laboratory metrics were compiled, including serum asprosin levels. The participants were categorized based on three serum asprosin quantiles, allowing for a comparison of various factors among the groups. The correlation between serum asprosin levels and other factors was analyzed. Employing a general linear model, the connection between 25-hydroxy vitamin D and serum asprosin levels was studied. Utilizing three quantiles of 25-hydroxy vitamin D, serum asprosin was treated as the dependent variable, while 25-hydroxy vitamin D served as the independent variable for linear regression analysis. As serum asprosin increased, there were gradual increments in age, disease duration, SBP, BMI, WC, creatinine, and SUA levels (P<0.05). Conversely, HbA1c, HDL-C, GFR, and 25-hydroxy vitamin D levels exhibited gradual declines (P<0.05). Age, 25-hydroxy vitamin D, SUA, creatinine, and LDL-C emerged as independent influencing factors for serum asprosin. Across the 1st to 3rd 25-hydroxy vitamin D quantiles, elevated 25-hydroxy vitamin D levels correlated with a gradual reduction in mean serum asprosin (P<0.05). Serum asprosin levels demonstrate an inverse correlation with 25-hydroxy vitamin D levels in community-dwelling individuals with type 2 diabetes. Serum asprosin levels might independently contribute to 25-hydroxy vitamin D levels.

Squamous Cell Carcinoma in Never Smokers: An Insight into SMARCB1 Loss.

Lung cancer remains the leading cause of cancer-related mortality worldwide, with non-small cell lung cancer (NSCLC) constituting 85% of cases. Among NSCLCs, squamous cell carcinoma (SqCC) is strongly associated with smoking. However, lung cancer in never smokers (LCINS) represents approximately 25% of lung cancer cases globally and shows increasing incidence, particularly in East Asia. LCINS-SqCC is less well-characterized, especially regarding its genomic alterations and their impact on clinical outcomes. We conducted a retrospective analysis over a 20-year period (July 2003-July 2023) at two major tertiary centers in the UK. The cohort included 59 patients with LCINS-SqCC who underwent radical surgical resection. Data collected included demographic information, comorbidities, histopathological details, and outcome metrics such as disease-free and overall survival. Molecular sequencing of tumor specimens was performed to identify genomic aberrations. The cohort had a median age of 71 years (IQR 62-77) and a median BMI of 25.4 (IQR 22.8-27.8), with a slight male predominance (53%). The majority of patients (93%) had a preoperative MRC of 1-2. Recurrent disease was observed in 23 patients (39%), and 32 patients (54%) had died at a median follow-up of 3 years. Median disease-free survival was 545 days (IQR 132-1496), and overall survival was 888 days (IQR 443-2071). Preoperative creatinine levels were higher in patients who experienced recurrence (p = 0.037). Molecular analysis identified biallelic SMARCB1 loss in two younger patients, associated with rapid disease progression despite R0 resection. These patients' tumors were PDL1-negative, TTF-1-negative, and positive for cytokeratin, CD56, and p40. SMARCB1-deficient SqCC in never smokers represents a highly aggressive variant with poor disease-free survival, highlighting the importance of integrating advanced molecular diagnostics in clinical practice. This study underscores the necessity for personalized treatment strategies, including targeted therapies such as EZH2 inhibitors and immune checkpoint blockade, to address the unique molecular pathways in SMARCB1-deficient cancers. Further clinical trials are essential to optimize therapeutic approaches for this challenging subgroup of lung cancer.

Eye-Tracking for Examining Nurses’ Attention During Cardiac Arrest Simulations: A Feasibility and Acceptability Study

&lt;p&gt;Introduction: Eye-tracking offers a distinctive opportunity to assess nurses’ clinical decision-making in simulation. Although its feasibility has been established in various scenarios, most studies have focused on a single participant, typically a physician in a leadership role. The application of eye-tracking in the challenging context of in-hospital cardiac arrest (IHCA) simulations, where nurses juggle diverse roles and undertake physical tasks such as chest compressions, has yet to be explored.&lt;/p&gt;&lt;p&gt;Objectives: This study aimed to assess the feasibility and acceptability of eye-tracking with nurses’ during IHCA simulations. Additionally, the study aimed to describe eye-tracking metrics based on different resuscitation roles and to explore the relationship between eye-tracking metrics to pinpoint the most informative metrics for the design of future studies.&lt;/p&gt;&lt;p&gt;Methods: In this single-group observational study, 56 newly hired nurses wore eye-tracking glasses during IHCA simulations. The primary feasibility criterion was the proportion of usable eye-tracking data. Secondary criteria included recruitment rate, calibration time, and glasses acceptability. The relationship among eye-tracking metrics was investigated through correlation analyses.&lt;/p&gt;&lt;p&gt;Results: Calibration of the devices was rapid, and 85.7% of the data was usable. The glasses were comfortable, non-distracting, and did not impede nurses’ vision or performance. Data were mapped for five areas of interest: the patient’s head and chest, cardiac monitor, teammates, and resuscitation cart. Eye-tracking metrics exhibited variations based on resuscitation roles. Fixation count, fixation duration, and time to first fixation appeared to be the most informative metrics in IHCA simulation.&lt;/p&gt;&lt;p&gt;Discussion and conclusion: These findings demonstrate the feasibility and acceptability of analyzing nurses’ eye-tracking data during IHCA simulations using a role-based approach. Future research should explore correlations with additional attention measures to enhance our understanding of nurse decision-making during cardiac arrest and improve educational strategies and outcomes.&lt;/p&gt;

Information metrics: impact factor in practice

Neste artigo são apresentados conceitos e considerações sobre estudos métricos da informação e os principais métodos e técnicas utilizados como a infometria, a bibliometria, a cienciometria e a webometria. É discutida a importância de medir e publicar os resultados da investigação científica. Neste sentido, são abordados os conceitos e os métodos relacionados com o Fator de Impacto para avaliação da produção científica, sendo feita uma abordagem prática sobre o mesmo utilizando para o efeito o JCR – Journal Citation Reports e o SJR – SCImago Journal &amp; Country Rank. São igualmente apresentados conceitos e feitas considerações sobre as variáveis que são utilizadas no cálculo do Fator de Impacto da Web.

Risk and Value Informed Structural Health Monitoring System Design for the Miter Gates

The ultimate goal of a Structural Health Monitoring and Prognostics system (SHM/DP) is to interrogate the data gathered in situ to infer the state of the structure (diagnostics), evaluate the system's evolution over time (prognos-tics), and leverage the diagnostic and prognostic results to facilitate decision-making. As a compelling business proposition, SHM/DP is expected to deliv-er economic returns through data-informed lifecycle management decisions, surpassing the estimated costs encompassing the design, initial deployment operations, and maintenance of the SHM/DP system. Therefore, it is prudent to approach a system design problem with a cost-benefit analysis perspective, aiming to achieve a design that maximizes the Value of Information (VoI) ac-quired by an information-acquisition system, evaluated in a pre-posterior sense. In this paper, our focus is on a sensor optimization framework that aims to maximize the VoI over the lifecycle of a miter gate structure. We em-ploy the proposed Bayesian sensor optimization framework to assess the ef-fectiveness of various Value of Information (VoI) metrics in quantifying the overall benefits of an SHM/DP system. Additionally, we explore the impact of the decision maker's risk profile and the adopted decision policies on the system design.

Radiation-induced DNA damage by proton, helium and carbon ions in human fibroblast cell: Geant4-DNA and MCDS-based study

Background. Radiation-induced DNA damages such as Single Strand Break (SSB), Double Strand Break (DSB) and Complex DSB (cDSB) are critical aspects of radiobiology with implications in radiotherapy and radiation protection applications. Materials and Methods. This study presents a thorough investigation into the effects of protons (0.1–100 MeV/u), helium ions (0.13–100 MeV/u) and carbon ions (0.5–480 MeV/u) on DNA of human fibroblast cells using Geant4-DNA track structure code coupled with DBSCAN algorithm and Monte Carlo Damage Simulations (MCDS) code. Geant4-DNA-based simulations consider 1 μm × 1 μm × 0.5 μm water box as the target to calculate energy deposition on event-by-event basis and the three-dimensional coordinates of the interaction location, and then DBSCAN algorithm is used to calculate yields of SSB, DSB and cDSB in human fibroblast cell. The study investigated the influence of Linear Energy Transfer (LET) of protons, helium ions and carbon ions on the yields of DNA damages. Influence of cellular oxygenation on DNA damage patterns is investigated using MCDS code. Results. The study shows that DSB and SSB yields are influenced by the LET of the particles, with distinct trends observed for different particles. The cellular oxygenation is a key factor, with anoxic cells exhibiting reduced SSB and DSB yields, underscoring the intricate relationship between cellular oxygen levels and DNA damage. The study introduced DSB/SSB ratio as an informative metric for evaluating the severity of radiation-induced DNA damage, particularly in higher LET regions. Conclusions. The study highlights the importance of considering particle type, LET, and cellular oxygenation in assessing the biological effects of ionizing radiation.

VAMPirus: A versatile amplicon processing and analysis program for studying viruses.

Amplicon sequencing is an effective and increasingly applied method for studying viral communities in the environment. Here, we present vAMPirus, a user-friendly, comprehensive, and versatile DNA and RNA virus amplicon sequence analysis program, designed to support investigators in exploring virus amplicon sequencing data and running informed, reproducible analyses. vAMPirus intakes raw virus amplicon libraries and, by default, performs nucleotide- and amino acid-based analyses to produce results such as sequence abundance information, taxonomic classifications, phylogenies and community diversity metrics. The vAMPirus analytical framework leverages 16 different opensource tools and provides optional approaches that can increase the ratio of biological signal-to-noise and thereby reveal patterns that would have otherwise been masked. Here, we validate the vAMPirus analytical framework and illustrate its implementation as a general virus amplicon sequencing workflow by recapitulating findings from two previously published double-stranded DNA virus datasets. As a case study, we also apply the program to explore the diversity and distribution of a coral reef-associated RNA virus. vAMPirus is streamlined within Nextflow, offering straightforward scalability, standardization and communication of virus lineage-specific analyses. The vAMPirus framework is designed to be adaptable; community-driven analytical standards will continue to be incorporated as the field advances. vAMPirus supports researchers in revealing patterns of virus diversity and population dynamics in nature, while promoting study reproducibility and comparability.

Quantum Mechanics Based on an Extended Least Action Principle and Information Metrics of Vacuum Fluctuations

We show that the formulations of non-relativistic quantum mechanics can be derived from an extended least action principle. The principle can be considered as an extension of the least action principle from classical mechanics by factoring in two assumptions. First, the Planck constant defines the minimal amount of action a physical system needs to exhibit during its dynamics in order to be observable. Second, there is constant vacuum fluctuation along a classical trajectory. A novel method is introduced to define the information metrics to measure additional observability due to vacuum fluctuations, which is then converted to an additional action through the first assumption. Applying the variational principle to minimize the total actions allows us to recover the basic quantum formulations including the uncertainty relation and the Schrödinger equation in the position representation. In the momentum representation, the same method can be applied to obtain the Schrödinger equation for a free particle while further investigation is still needed for a particle with an external potential. Furthermore, the principle brings in new results on two fronts. At the conceptual level, we find that the information metrics for vacuum fluctuations are responsible for the origin of the Bohm quantum potential. Even though the Bohm potential for a bipartite system is inseparable, the underlying vacuum fluctuations are local. Thus, inseparability of the Bohm potential does not justify a non-local causal relation between the two subsystems. At the mathematical level, quantifying the information metrics for vacuum fluctuations using more general definitions of relative entropy results in a generalized Schrödinger equation that depends on the order of relative entropy. The extended least action principle is a new mathematical tool. It can be applied to derive other quantum formalisms such as quantum scalar field theory.