Exact Sampling Research Articles

Improving signal-to-noise ratio of Raman measurements based on ensemble learning approach.

Raman spectroscopy is an extensively explored vibrational spectroscopic technique to analyzethebiochemical composition and molecular structure of samples, which is often assumed to be non-destructive when carefully using proper laser power and exposure time. However, the inherentlyweak Raman signal and concurrent fluorescence interference often lead to Raman measurements withalow signal-to-noise ratio (SNR), especially for biological samples. Great efforts have been made to develop experimental approaches and/or numerical algorithms to improvetheSNR. In this study, we proposed an ensemble learning approach to recover and denoise Raman measurements withalow SNR. The proposed ensemble learning approach was evaluated on 986 pairs of Raman measurements, each pair of which consists of a low SNR Raman spectrum and a high SNR reference Raman spectrum from the exact same fungal sample but uses 200 timestheintegration time. Compared with conventional methods, the Raman measurementsrecoveredby the proposed ensemble learning approach are more identical to high SNR reference Raman measurements, with anaverage RMSE and MAE of only 1.337 × 10-2 and 1.066 × 10-2, respectively; thus, the proposed ensemble learning approach is expected to be a powerful tool for numerically improving the SNR of Raman measurements and further benefits rapid Raman acquisition from biological samples.

State Divorce Laws, Reproductive Care Policies, and Pregnancy-Associated Homicide Rates, 2018-2021

Barriers to divorce and reproductive health care can threaten the health and safety of pregnant and recently pregnant females. To examine state laws about divorce, reproductive health care (access to contraception, family planning services, and abortion), and pregnancy-associated homicide rates in US states over a 4-year period (2018-2021). In this cross-sectional study, bivariate tests and regressions were used to analyze crude rates of pregnancy-associated homicide from the National Violent Death Reporting System in 181 state-years for calendar years 2018 to 2021, with analyses conducted on September 8, 2024. Access to divorce while pregnant and reproductive health care over a 4-year period in the US. Primary outcomes (pregnancy-associated homicide by intimate partners vs non-intimate partners and rates among younger Black, Hispanic, and White females) were assessed using the National Violent Death Reporting System. Negative binomial regression was used to test 2 hypotheses: access to divorce while pregnant and reproductive health care are associated with pregnancy-associated homicide rates. Individual level data, including exact sample size, were not available in this study of state-level homicide rates. Negative binomial regression analysis showed that, where finalizing divorce during pregnancy is prohibited, intimate partner homicide rates (incidence rate ratio [IRR], 2.11; 95% CI, 1.09-4.08; P = .03) and rates among younger (age 10-24 years) White females (IRR, 2.39; 95% CI, 1.12-5.09; P = .02) were significantly higher. In state-years with greater access to reproductive health care, rates were significantly lower for non-intimate partner homicide (IRR, 0.92; 95% CI, 0.87-0.98; P = .01) and for younger Black females (IRR, 0.91; 95% CI, 0.87-0.96; P < .001) and younger Hispanic females (IRR, 0.87; 95% CI, 0.79-0.96; P = .007). In this cross-sectional study of pregnancy-associated homicide rates, barriers to divorce were associated with higher homicide rates and access to reproductive health care was associated with lower homicide rates. This study highlights the association between state legislation and pregnancy-associated homicide in the US, which is important information for policymakers.

Exact conditional samples for the NEF-QVF family in generalized linear models and their use in goodness of fit

This paper aims to simulate conditional samples for generalized linear models and to show their use in goodness of fit. The particular case of the natural exponential family with quadratic variance function and categorical covariates is studied. The conditional samples are drawn from the conditional distribution having conditioned on a sufficient statistic. A procedure that uses the Rao-Blackwell estimate for the cumulative distribution function is employed. The performance of the proposed procedure is shown through a simulation study and the analysis of a data set.

Exact samples sizes for clinical trials subject to size and power constraints

SummaryThis paper first describes the difficulties in providing the required sample sizes for clinical trials that guarantee type 1 and type 2 error control. The required sample sizes obviously depend on the test employed, and in this study we use the so‐called E‐test, which is known to have extremely favourable size properties and higher power than alternatives. To compute exact powers for this test in real time is not currently feasible, so a corpus of pre‐computed exact powers (and sizes) was created, covering sample sizes up to 500. When there are no solutions within the corpus, a novel extrapolation technique is used. Exact size can be computed after the sample sizes have been extracted; however, for the E‐test the exact size is virtually always very close to the nominal target. All the code has been converted into an R‐package, which is available on CRAN and illustrated.

Overlap Analysis in Progressive Hybrid Censoring: A Focus on Adaptive Type-II and Lomax Distribution

This article explores the adaptive type-II progressive hybrid censoring scheme, introduced by Ng et al. (2009), which is used to make inferences about three measures of overlap: Matusita's measure ($\rho $), Morisita's measure ($\lambda $), and Weitzman's measure ($\Delta $) for two Lomax distributions with different parameters. The article derives the bias and variance of these overlap measures' estimators. If sample sizes are limited, the precision or bias of these estimators is difficult to determine because there are no closed-form expressions for their variances and exact sampling distributions, so Monte Carlo simulations are used. Also, confidence intervals for these measures are constructed using both the bootstrap method and Taylor approximation. To demonstrate the practical significance of the proposed estimators, an illustrative application is provided by analyzing real data.

Sample size calculation for the sequential parallel comparison design with binary endpoint using exact methods

High placebo responses in clinical trial could reduce the treatment effect leading to the failure of a promising drug. Several strategies have been developed to minimize high placebo responses, including the sequential parallel comparison design (SPCD). For a study with binary outcome, the existing statistical methods to test the drug effectiveness always rely on the asymptotic limiting distribution. When a study's sample size is small to medium, the asymptotic approaches do not have satisfactory performance with regard to type I error rate and statistical power. For that reason, we propose utilizing exact conditional approach to calculate sample size based on the existing test statistics to order the sample space. The proposed method controls the type I error rate under the unconditional framework. We compare the proposed exact sample sizes using different test statistics and the sample size for a randomized parallel study. We would recommend using exact sample sizes for the SPCD with small- to medium sample sizes and the SPCD with extreme response rates.

Evaluation of multiparametric MRI differentiating pleomorphic adenoma from schwannoma in parapharyngeal space.

This study aimed to investigate whether multiparametric magnetic resonance imaging (MRI) including dynamic contrast-enhanced (DCE) and diffusion weighted (DW) MRI can differentiate pleomorphic adenoma (PA) from schwannoma in the parapharyngeal space. Forty-six patients with pathologically proven PAs and 47 schwannomas in the parapharyngeal space were enrolled. All patients underwent conventional MRI, and DW-MRI and DCE-MRI were performed in 30 and 33 patients, respectively. Fisher's exact, Mann-Whitney-U tests and Independent samples t-test were used to compare variables between PAs and schwannomas. Multivariate logistic regression analysis was used to examine the diagnostic performance of MRI parameters. The PAs usually show lobulation sign, posterior displacement of ICA and attached to the parotid gland deep leaf, while bird beak configuration, anterior displacement of ICA and involvement of foramen jugular were more commonly seen in the schwannomas(all p < 0.001). The washout rate of PAs was found to be higher than that of schwannomas (p = 0.035), whereas no significance was found in the other DCE-MRI parameters and in ADCs(p > 0.05). Using a combination of conventional MRI features including lobulation sign, bird beak configuration, direction of internal carotid artery(ICA) displacement and attached to the parotid gland in multivariate logistic regression analysis, sensitivity, specificity, and accuracy in differential diagnosis of PAs and schwannomas were 97.8%, 91.5% and 94.6%, respectively. Conventional MRI can effectively differentiate PAs from schwannomas in the parapharyngeal space with a high diagnostic accuracy. The DCE-MRI and DWI have limited added diagnostic value to conventional MRI in the differential diagnosis.

Pollution concentration monitoring using a new Birnbaum–Saunders control chart

AbstractAir pollution monitoring is an important issue in environmental science. The Birnbaum–Saunders (BS) distribution, originally applied to describe product failure time distribution to fatigue failures and general random wear failures, is also well to describe the pollutant concentration data due to accumulations of various pollutants in the air over time. Sulfur dioxide (SO2) is a critical factor in air pollution. Hence, it is important to monitor its concentration variation for air pollution prevention. Due to the complexity of its distribution form, there is no reliable and easy‐to‐use control chart for monitoring pollutant concentrations based on the BS distribution. We found that the SO2 concentration data follows the BS distribution. In this study, we propose a new median control chart based on the exact sampling distribution of the monitoring statistic to detect shifts in the median of BS distribution. Thus, given the false alarm rate, the control limits for such control charts can be obtained precisely satisfying a preset in‐control average run length using Monte Carlo simulations. The out‐of‐control average run lengths are calculated by simulation to evaluate the detection performance of the proposed chart when the median shifts occur. We further compare the detection performance of the proposed chart and those of the existing control charts based on asymptotic sampling distributions. In order to improve the detection ability of the proposed chart for small median shifts, an exponentially weighted moving average (EWMA) control chart is constructed. The results of numerical analyses demonstrated that the proposed EWMA chart performs much better than all existing control charts for monitoring the median of BS distribution. Finally, the proposed control charts are applied to monitor the median of SO2 concentrations for air pollution control, showing that both charts can effectively detect a shift in the median of SO2 concentrations. The proposed EWMA control chart even detects out a small shift in the median of SO2 concentrations. The results provide a continuous monitoring solution for air pollution prevention.

Updated fiducial distribution of parameters in the associated delta-lognormal population.

In this paper we consider a special kind of semicontinous distribution. We try to concern with the situation where the probability of zero observation is associated with the location and scale parameters in lognormal distribution. We first propose a goodness-of-fit test to ensure that the data can be fit by the associated delta-lognormal distribution. Then we define the updated fiducial distributions of the parameters and establish the results that the confidence interval has asymtotically correct level while the significance level of the hypothesis testing is also asymtotically correct. We propose an exact sampling method to sample from the updated fiducial distribution. It can be seen in our simulation study that the inference on the parameters is largely improved. A real data example is also used to illustrate our method.

Monte Carlo Based Sampling Distribution of Annual Rate of Exceedance for Earthquake Insurance

ABSTRACT Seismic hazard expressed in terms of annual rate of exceedance and is used to calculate the earthquake insurance premium. Annual rate of exceedance is a complicated function of magnitudes, distances from site to earthquake sources and attenuation. Due to its complexity, determination of exact sampling distribution of earthquake insurance premium is not an easy task. This research proposes Monte Carlo simulation approach to determine the sampling distribution of earthquake insurance premium. Annual rate of exceedance was simulated first and then the insurance premium calculated based on simulation of annual rate of exceedance. The simulation involves quantifying synthetic catalogue similar to historical catalogue. Its simulation is conducted in order to construct annual rate of exceedance as an indicator of earthquake risk used in earthquake insurance. The simulation of 25 iteration and sample size of 100 shows that the sampling distribution of insurance premium is skewed to the right. The idea of using Monte Carlo simulation to study the sampling distribution of annual rate of exceedance is the originality of the study and the study contributes to the methodology of earthquake insurance.

Analyzing Passenger Experience: A Study On Airline Mishandling Incidents At Ninoy Aquino International Airport Terminal 3

This research delves deep into the complex dynamics of airline mishandling incidents at NAIA Terminal 3, aiming to reveal their intricate effects on passenger experiences. With a focus on understanding the connections between mishandling, flight disruptions, and broader implications for airline operations, the study intends to offer a comprehensive view. Using a precise approach, the researchers adjusted Slovin's formula to pinpoint an exact sample size of 300 respondents, ensuring a detailed exploration of passenger encounters despite time constraints. They achieved a 14% response rate, providing insights into the diverse landscape of travelers. Going beyond traditional surveys, the study incorporated qualitative aspects through interviews with informants, including airport and airline personnel. This dual-method strategy facilitated a nuanced analysis of mishandling incidents, capturing the subtle challenges faced by both passengers and industry professionals. The results emphasized the significant impact of mishandling on operational efficiency, highlighting the need for improved baggage handling and security measures. The study concludes with practical recommendations, advocating for clear communication, technological advancements, and ongoing staff training to enhance the overall airport experience. This research serves as a valuable guide for aviation stakeholders, offering a roadmap to address mishandling incidents and improve passenger satisfaction.

Optimal designing of skip-lot sampling plan with a provision for reducing the normal inspection using the Taguchi process capability index

A product with a strong track record of quality might have less inspection according to the skip-lot sampling plan. As a result, skip-lot sampling plans are created to cut down the inspection cost with desired protection. The skip-lot sampling method is also reliable, practical, and favorable from an economic standpoint. As a result, this work develops the SkSP-V plan, a novel system of skip-lot sampling plan, based on the Taguchi process capability index. The proposed plan stipulates that any lot is rejected during sampling inspection, normal inspection must resume, however, it also allows for a reduced level of normal inspection in the event that higher product quality is demonstrated. The exact sampling distribution of the estimated Taguchi capability index is used to derive the operating characteristic function of the proposed plan. An optimization model is employed to establish the optimal plan parameters, guaranteeing adherence to the specified quality levels and risk criteria. The performance measures such as probability of acceptance and average sample number of the proposed plan are examined and compared with existing plans. This article also includes real-world example to effectively demonstrate the practical implementation of the proposed SkSP-V plan based on the Taguchi process capability index.

Faster classical boson sampling

Since its introduction boson sampling has been the subject of intense study in the world of quantum computing. In the context of Fock-state boson sampling, the task is to sample independently from the set of all n × n submatrices built from possibly repeated rows of a larger m × n complex matrix according to a probability distribution related to the permanents of the submatrices. Experimental systems exploiting quantum photonic effects can in principle perform the task at great speed. For classical computing, Aaronson and Arkhipov (2011) showed that exact boson sampling problem cannot be solved in polynomial time unless the polynomial hierarchy collapses to the third level. Indeed for a number of years the fastest known exact classical algorithm ran in O(m+n−1nn2n) time per sample, emphasising the potential speed advantage of quantum computation. The advantage was reduced by Clifford and Clifford (2018), who gave a significantly faster classical solution taking O(n2n+poly(m,n)) time and linear space, matching the complexity of computing the permanent of a single matrix when m is polynomial in n. We continue by presenting an algorithm for Fock boson sampling whose average-case time complexity is much faster when m is proportional to n. In particular, when m = n our algorithm runs in approximately O(n · 1.69 n ) time on average. This result further increases the problem size needed to establish quantum computational advantage via the Fock scheme of boson sampling.

Testing the fit of data and external sets via an imprecise Sargan-Hansen test

In empirical sciences such as psychology, the term cumulative science mostly refers to the integration of theories, while external (prior) information may also be used in statistical inference. This external information can be in the form of statistical moments and is subject to various types of uncertainty, e.g., because it is estimated, or because of qualitative uncertainty due to differences in study design or sampling. Before using it in statistical inference, it is therefore important to test whether the external information fits a new data set, taking into account its uncertainties. As a frequentist approach, the Sargan-Hansen test from the generalized method of moments framework is used in this paper. It tests, given a statistical model, whether data and point-wise external information are in conflict. A separability result is given that simplifies the Sargan-Hansen test statistic in most cases. The Sargan-Hansen test is then extended to the imprecise scenario with (estimated) external sets using stochastically ordered credal sets. Furthermore, an exact small sample version is derived for normally distributed variables. As a Bayesian approach, two prior-data conflict criteria are discussed as a test for the fit of external information to the data. Two simulation studies are performed to test and compare the power and type I error of the methods discussed. Different small sample scenarios are implemented, varying the moments used, the level of significance, and other aspects. The results show that both the Sargan-Hansen test and the Bayesian criteria control type I errors while having sufficient or even good power. To facilitate the use of the methods by applied scientists, easy-to-use R functions are provided in the R script in the supplementary materials.

Exploring ZnO nanoparticles: UV–visible analysis and different size estimation methods

The study investigates several theoretical and empirical methods for determining the size of zinc oxide nanoparticles (ZnO NPs) and studies their characteristics by using solely UV–visible spectroscopy. The investigation begins with the green synthesis of three samples of ZnO NPs using three different reducing agents Aspergillus Niger fungal biomass (AN), Aloe barbadensis Mille (ABM) and Ocimum tenuiflorum (OT) leaves, respectively with Zinc Sulphate as a precursor involving precipitation method. The study analyses the UV–visible data of each sample to obtain optical bandgap, Urbach energy, refractive index, optical conductivity. and apply several methods or Models such as Effective Mass Approximation (EMA) or Brus Equation (BE), Hyperbolic Band Model (HBM), Meulenkamp's Expression (ME), Tight Binding Model (TBM) and a simplified version of EMA (SEMA) to estimate the size of ZnO NPs. The effectiveness of size-determining models was assessed using two sets of one-way analysis of variance (ANOVA) for each ZnO sample and models Followed by Tukey post-hoc analysis to identify the exact sample that is different from other samples.

Abstract PO4-07-02: Development of an Artificial Intelligence-based breast cancer detection model using Plasma Lipidomic Signature

Abstract Background: Mammography is the current diagnostic standard for breast cancer screening and monitoring. However, accessibility challenges, accuracy issues and patient discomfort all contribute to reduced patient compliance and utilization, resulting in a need for more effective diagnostic tools. An artificial intelligence (AI)-based lipidomic blood test may add significant value to early breast cancer detection rate and improve outcomes for patients. We have previously reported a series of lipidomic studies (n=793) and derived a lipid signature from plasma-enriched extracellular vesicles (EVs) that effectively distinguished people with localized breast cancer from cancer-free controls. Here we report the development of a breast cancer detection AI model from lipidomic data assessed directly using plasma samples. Methods: Lipids in both EVs and plasma collected from fasted breast cancer and control blood samples (n=256) were extracted and analysed by liquid chromatography-high resolution mass spectrometry (LC-HRAM-MS). Over 400 manually curated lipids were quantified. A bootstrapped analysis using Boruta, a robust and statistically rigorous feature selection algorithm based on random forest feature importance, was employed to identify cancer discriminatory lipid signatures in EV and plasma lipidomes consistently selected across 2000 bootstrap samples. The resulting lipid signature was then used to train an ensemble of 18 distinct machine learning models for cancer status prediction using a majority vote to aggregate the individual predictions. Model performance and variability were assessed over 2000 iterations of leave-group-out cross-validation (LGOCV) using an 80/20 train-test split. Average patient-level predictions across LGOCV iterations were recorded for both EV-and plasma-derived models and the two modalities were compared using an exact paired samples test (McNemar’s test). Results: Both the EV- and plasma-derived lipid signatures performed well in distinguishing breast cancer samples from controls. The development of a bioinformatics AI pipeline enabled the creation of a robust ensemble model achieving an F1 score of 0.89 in plasma with LGOCV. The final plasma ensemble predictive performance of 86.1% (±4.5%) in accuracy, 91.4% (±5.4%) in sensitivity, and 78.7% (±8.6%) specificity was achieved, which is comparable to that of EV (accuracy: 86.1±4.4%, sensitivity: 90.4±5.3%, specificity: 80.2±8.7%). Paired samples analysis using McNemar’s test indicated no significant differences between models trained on EV- and plasma-derived lipid signatures in either the sensitivity (p=0.65), specificity (p=0.49), or accuracy (p=0.42). Conclusion: The initial study demonstrated the high performance of a plasma-enriched extracellular vesicle-derived lipid biomarker signature for early breast cancer detection. Direct assessment of the lipidomic signature from plasma showed promise in simplifying the test. Assessing plasma directly offered advantages in terms of scalability, higher throughput, and ease of implementation. Further verification of the lipid signature in an upcoming study involving 500 plasma samples is planned. Ongoing studies will further optimize the plasma lipidomic signature and strengthen our AI pipeline. These findings support the potential clinical application of AI-based lipidomic profiling as a blood-based screening tool for breast cancer detection. Citation Format: Ameline Lim, Cheka Kehelpannala, Fatemeh Vafaee, Forrest Koch, Dana Pascovici, Desmond Li, Kerry Heffernan, Gillian Lamoury, Amani Batarseh, Bruce Mann. Development of an Artificial Intelligence-based breast cancer detection model using Plasma Lipidomic Signature [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO4-07-02.

LGBTQ+ outpatients present to eating disorder treatment earlier and with more severe depressive symptoms than cisgender heterosexual peers

ABSTRACT Community evidence indicates high eating disorder (ED) and comorbid symptom severity among LGBTQ+ compared to cisgender heterosexual (CH) individuals. Little is known about such disparities in ED treatment samples, especially in outpatient treatment. We aimed to descriptively characterize and investigate baseline group differences in symptom severity between LGBTQ+ and CH ED outpatients at treatment intake. Data from 60 (22.3%) LGBTQ+ and 209 (77.7%) CH ED outpatients were used to examine: (1) demographic and diagnostic differences; (2) differences in ED, depressive, and emotion dysregulation symptoms. Objectives were tested using Fisher-Freeman-Halton exact and independent samples t-tests, and analyses of covariance adjusted for age and diagnosis, respectively. Most LGBTQ+ outpatients were bisexual (55.2%), and 6.5% identified as transgender and non-binary. LGBTQ+ outpatients presented to treatment at younger ages (Mean Difference [MD] = -3.39, p = .016) and reported more severe depressive symptoms (MD = 5.73, p = .004) than CH patients, but endorsed similar ED symptom and emotion dysregulation severity. Groups did not differ in other demographic or diagnostic characteristics. LGBTQ+ individuals may develop more severe depression and similarly severe EDs at earlier ages but seek outpatient care sooner than CH peers. Managing depressive symptoms may be particularly important for LGBTQ+ ED patients.

Message-passing on hypergraphs: detectability, phase transitions and higher-order information

Hypergraphs are widely adopted tools to examine systems with higher-order interactions. Despite recent advancements in methods for community detection in these systems, we still lack a theoretical analysis of their detectability limits. Here, we derive closed-form bounds for community detection in hypergraphs. Using a message-passing formulation, we demonstrate that detectability depends on the hypergraphs’ structural properties, such as the distribution of hyperedge sizes or their assortativity. Our formulation enables a characterization of the entropy of a hypergraph in relation to that of its clique expansion, showing that community detection is enhanced when hyperedges highly overlap on pairs of nodes. We develop an efficient message-passing algorithm to learn communities and model parameters on large systems. Additionally, we devise an exact sampling routine to generate synthetic data from our probabilistic model. Using these methods, we numerically investigate the boundaries of community detection in synthetic datasets, and extract communities from real systems. Our results extend our understanding of the limits of community detection in hypergraphs and introduce flexible mathematical tools to study systems with higher-order interactions.

The expected sample allele frequencies from populations of changing size via orthogonal polynomials

In this article, discrete and stochastic changes in (effective) population size are incorporated into the spectral representation of a biallelic diffusion process for drift and small mutation rates. A forward algorithm inspired by Hidden-Markov-Model (HMM) literature is used to compute exact sample allele frequency spectra for three demographic scenarios: single changes in (effective) population size, boom-bust dynamics, and stochastic fluctuations in (effective) population size. An approach for fully agnostic demographic inference from these sample allele spectra is explored, and sufficient statistics for stepwise changes in population size are found. Further, convergence behaviours of the polymorphic sample spectra for population size changes on different time scales are examined and discussed within the context of inference of the effective population size. Joint visual assessment of the sample spectra and the temporal coefficients of the spectral decomposition of the forward diffusion process is found to be important in determining departure from equilibrium. Stochastic changes in (effective) population size are shown to shape sample spectra particularly strongly.

Infrared: a declarative tree decomposition-powered framework for bioinformatics.

Many bioinformatics problems can be approached as optimization or controlled sampling tasks, and solved exactly and efficiently using Dynamic Programming (DP). However, such exact methods are typically tailored towards specific settings, complex to develop, and hard to implement and adapt to problem variations. We introduce the Infrared framework to overcome such hindrances for a large class of problems. Its underlying paradigm is tailored toward problems that can be declaratively formalized as sparse feature networks, a generalization of constraint networks. Classic Boolean constraints specify a search space, consisting of putative solutions whose evaluation is performed through a combination of features. Problems are then solved using generic cluster tree elimination algorithms over a tree decomposition of the feature network. Their overall complexities are linear on the number of variables, and only exponential in the treewidth of the feature network. For sparse feature networks, associated with low to moderate treewidths, these algorithms allow to find optimal solutions, or generate controlled samples, with practical empirical efficiency. Implementing these methods, the Infrared software allows Python programmers to rapidly develop exact optimization and sampling applications based on a tree decomposition-based efficient processing. Instead of directly coding specialized algorithms, problems are declaratively modeled as sets of variables over finite domains, whose dependencies are captured by constraints and functions. Such models are then automatically solved by generic DP algorithms. To illustrate the applicability of Infrared in bioinformatics and guide new users, we model and discuss variants of bioinformatics applications. We provide reimplementations and extensions of methods for RNA design, RNA sequence-structure alignment, parsimony-driven inference of ancestral traits in phylogenetic trees/networks, and design of coding sequences. Moreover, we demonstrate multidimensional Boltzmann sampling. These applications of the framework-together with our novel results-underline the practical relevance of Infrared. Remarkably, the achieved complexities are typically equivalent to the ones of specialized algorithms and implementations. Infrared is available at https://amibio.gitlabpages.inria.fr/Infrared with extensive documentation, including various usage examples and API reference; it can be installed using Conda or from source.