True Replication Research Articles

Manipulating the water–air interface to drive protein assembly for functional silk-like fibroin fibre production

Silk’s remarkable properties arise from its hierarchical structure, formed through natural transformation from an aqueous solution to a solid fibre driven by pH and flow stress under low-energy conditions. In contrast, artificial silk fabrication typically relies on extrusion-based methods using coagulating baths and unnatural solvents, limiting true biomimetic replication. Here, we find that native-like silk fibroin forms viscoelastic films at the air-water interface. Utilizing this, we demonstrate a mild, all-aqueous method to seamlessly pull silk-like fibres with co-aligned nanofibrillar bundles. The fiber structure transitioned from hexagonally packed β-solenoid units at low pulling speeds to β-sheet-rich structures at higher speeds. Fibers pulled near physiological speeds (26.3 mm s-¹) exhibited optimal mechanical properties, with an elastic modulus of 8 ± 1 GPa and toughness of 8 ± 5 MJ m-³, comparable to natural silk. This platform also enables embedding nanoparticles and biologics, offering broad applications in sensors, biocatalysis, and tissue engineering, expanding the potential of silk-based composite materials.

“Fractional replication” in single‐visit multi‐season occupancy models: Impacts of spatiotemporal autocorrelation on identifiability

Abstract Understanding variation in species occupancy is an important task for conservation. When assessing occupancy patterns over multiple temporal seasons, it is recommended to visit at least a subset of sites multiple times within a season during a period of closure to account for observation biases. However, logistical constraints can inhibit re‐visitation of sites within a season, resulting in the use of single‐visit multi‐season occupancy models. Some have suggested that autocorrelation in space and/or time can provide “fractional replication” to separately estimate occupancy probability from detection probability, but the reliability of such approaches is not well understood. We perform an extensive simulation study to assess the reliability of estimates from single‐visit multi‐season occupancy models under differing amounts of spatial and temporal autocorrelation (“fractional replication”). We assess model performance under both correctly specified models and multiple forms of model mis‐specification, and compare estimates from single‐visit models to models with varying amounts of within‐season replication. We also assess the reliability of single‐visit models to estimate occupancy probability of ovenbirds (Seiurus aurocapilla) in New Hampshire, USA. We found less bias in estimates from single‐visit occupancy models with long‐range spatial autocorrelation in occupancy probability compared to short‐range spatial autocorrelation when the model is correctly specified. However, under certain forms of model mis‐specification, estimates from single‐visit multi‐season occupancy models were biased and had low coverage rates regardless of the characteristics of the “fractional replication”. In contrast, models with varying amounts of additional replication were robust to model mis‐specification. Our findings suggest that “fractional replication” cannot replace true replication in terms of occupancy probability identifiability and that researchers should consider the potential inaccuracies when using single‐visit multi‐season occupancy models. We show that a little true replication can go a long way with even 10% of sites being revisited within a season leading to reasonably robust estimates even in the presence of extreme model mis‐specifications. When possible, we recommend performing multiple within‐season visits to at least a subset of spatial locations or integrating single‐visit data with other data sources to mitigate reliance on parametric assumptions required for reliable inference in single‐visit multi‐season occupancy models.

How Can I Study from Below, that which Is Above?

Z-curve is an analytic technique with which one can estimate the percent of a set of studies of interest that would replicate if one were to run actual replication studies. I compared the estimates z-curve yields to the outcome of real large-scale replication studies, such as the Open Science Collaboration (2015) work or the various ManyLabs projects (e.g., Klein et al., 2014). I collected p-values from the original studies examined in six different large-scale replication efforts to the extent possible, ran z-curves on all the original studies, and compared the z-curve results to the results of the actual replication studies. My results show that across 163 replication studies taken from the six replication efforts, 85 (52.15%) showed statistically significant resultsin the expected direction as indicated by the authors of the replication studies. The outcome of the z-curve of all these studies was accurate, with the midpoint between the expected replication rate and the expected discovery rate, 50.55%, being almost exactly the same as the true replication rate. Its replicability estimate was also more accurate than that of p-curve analysis. Comparison of z-curve analysis of studies that did successfully replicate to studies that did not does suggest heterogeneity in the accuracy of its estimates, however. The pros and cons of z-curve analysis are discussed.

Pupil dilation as a function of pitch discrimination difficulty: A replication of Kahneman and Beatty, 1967.

In the present paper, we carry out a replication of a seminal paper by Kahneman, D. & Beatty, J. (1967). Perception & Psychophysics, 2(3),101-105 for using pupillometry as an implicit measure of auditory processing load, specifically, non-verbal auditory processing. While numerous papers since have supported the notion that pupillometry is a fairly reliable index of processing load in general (Zekveld, A. A., Koelewijn, T., and Kramer, S. E. (2018). Trends in Hearing, 22,1-25; Winn, M. B., Wendt, D., Koelewijn, T., and Kuchinsky, S. E. (2018). Trends in Hearing, 22,1-32), they typically have relied on memory recall, and/or more sophisticated cognitive tasks such as language comprehension or split attention. Kahneman and Beatty's paper, despite that it was published more than 50 years ago, continues to be the primary citation to support the claim that pupillometry is a reliable index of task difficulty for a simple non-verbal pitch discrimination task therefore giving us an implicit measure for listening effort (e.g.,Kramer, S. E., Lorens, A., Coninx, F., Zekveld, A. A., Piotrowska, A., & Skarzynski, H. (2013). Language and Cognitive Processes, 28(4),426-442; Schlemmer, K. B., Kulke, F., Kuchinke, L., & Van Der Meer, E. (2005). Psychophysiology, 42(4),465-472; Lisi, M., Bonato, M., and Zorzi, M. (2015). Biological Psychology, 112,39-45). This type of task takes very little explicit memory, is non-verbal, and relies heavily on more low-level, automatic perceptual processing. Using two different replication studies, one exact, and one modified, we only replicated the main result in the modified replication. The true replication failed to replicate on all nine statistical tests. Overall, our findings suggest that pupil dilation can be used as an implicit measure of task difficulty for a simple, non-semantic, auditory task, however, the robustness of the effect appears relatively weak in comparison with the original study, and the amount of variation across participants much greater.

An image‐based methodology to evaluate oat panicle architecture

AbstractPanicle size and architecture are traits of major relevance for oat production as they play a key role in conditioning the potential number of grains per spikelet and in consequence grain shape, size, and distribution. The aim of this research was to propose an image‐based strategy and methodology to evaluate oat panicles. Specifically, the goals were to create and deploy the foundations for a high‐throughput methodology to phenotype oat panicles and to determine the optimum number of panicles needed to detect differences between genotypic means for oat panicle traits with high power. A total of 48 genotypes were evaluated in four field experiments with three true replications in two locations and 2 years. Ten panicles per experimental unit were harvested and evaluated with image analysis using MATLAB. Panicle length, width, aspect ratio, compactness, and digital biomass were obtained from the images. The heritability of all panicle traits was high between 0.76 and 0.93. A high power of 0.80 to detect significant differences at an alpha of 0.05 among genotypic means for all panicle architecture traits was reached with the use of six panicles per replication. Our results showed a highly efficient and powerful strategy to study panicle architecture in oat. This methodology could be scaled‐up to efficiently phenotype large populations and provide the basis for understanding panicle architecture and grain morphology in oat.

Mythmaking In Modern Literature: Harry Potter by J.K. Rolling

Up to date myths are regarded as universal and enduring for their depicting human’s understanding and knowledge. It presents clues and intimations to Man’s origins of belief and life. Harry Potter, a series of storytelling written by J.K. Rolling, is a metaphoric presentation of myths and cultural background behind each one of them. This study investigates and explores how J.K. Rolling involves in origins of cultural textually while sharing mythological ideas in modern literature as a creative way to give new senses to each of them. With its unique demonstration, Harry Potter places an outstanding position in giving myth a new dimension and ties ancient with present via a new style of mythmaking in modern literature. The study conducts an analytic explanation of the importance of mythmaking to literature in general and specifically in Harry Potter. The findings that the study arrives at are that myths are true replications of cultures and societies, and Rolling's stories make new connection with the depth of human superficiality as well as it renders the possibility to revive mythological mentality in modern era.

Abstract 2707: Factoring expression data of high-grade serous ovarian cancer tumors for unbiased longitudinal analysis

Abstract We address challenges in longitudinal RNA-seq analysis of patient tumors, which can reveal powerful insights on cancer evolution at personalized level, proposing methods for unbiased analysis and integration Experimental procedures: 214 whole-genome bulk RNA-seq samples from 61 high-grade serous ovarian cancer (HGSC) patients were collected before and after chemotherapy, along with single-cell RNA-seq data from >93,000 cells in 11 patients. 308 TCGA treatment-naive bulk RNA-seq samples from HGSC, 474 from melanoma (SKCM), and 581 from endometrial carcinoma (UCEC) were used for validation. Results: We show that in a longitudinal analysis, cancer progression and chemotherapy exert both microenvironmental and phenotypic changes. We developed PRISM [1] to factor the expression data at individual bulk level, and show the adjustment improves the association between expression profiles and patient survival. Our findings extend to pathway and expression-derived tumor subtype levels in HGSC and SKCM. Technical batch effects pose challenges for multi-institute or long-running sample collections. Unbiased correction requires replicates, which are typically unavailable for patient data. We developed POIBM [submitted] to simultaneously infer a suitable reference and factor out the batch effects. We show that POIBM effectively discovers true replicates, batch effects plague many cancer types in TCGA data, and batch correction allows more meaningful expression subtyping in UCEC. Distinct genomic backgrounds of the patients hinders stratification and discovering shared functional states. We developed PRIMUS [submitted] to simultaneously factor the patient background (or other confounders) and a sample clustering, which is necessary when the underlying stratification is uneven or shared by patient subsets. Among the commonly aberrant cell states, such as EMT, PRIMUS analysis on HGSC scRNA identified a novel stress signature, enriched by chemotherapy and indicating poor survival, which were validated in PRISM-factored TCGA bulk ovarian data. Conclusions: Our methodology facilitates unbiased longitudinal expression analysis and integration. The more accurate phenotypic changes at gene, pathway, and expression subtype level may confer sensitivity/resistance chemotherapy, and, as shown, allow enhance patient survival prediction and discovery of novel chemotherapy related subtypes. Consequently, our work aids ranking putative intervention strategies for overcoming ovarian cancer chemoresistance for future validation experiments. [1]: Hakkinen et al., Bioinformatics 37: 2882 (2021) Citation Format: Antti Häkkinen, Kaiyang Zhang, Susanna Holmström, Sanaz Jamaldazeh, Johanna Hynninen, Sakari Hietanen, Kaisa Huhtinen, Sampsa Hautaniemi. Factoring expression data of high-grade serous ovarian cancer tumors for unbiased longitudinal analysis [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 2707.

Estimation of output measurement variances for error‐in‐variables model parameter estimation

AbstractError‐in‐variables model (EVM) methods require information about variances of input and output measured variables when estimating the parameters in mathematical models for chemical processes. In EVM, using replicate experiments for estimating output measurement variances is complicated because true values of inputs may be different when multiple attempts are made to repeat an experiment. To address this issue, we categorize attempted replicate experiments as: (i) true replicates (TRs) when uncertain inputs are the same in replicated runs and (ii) pseudo replicates (PRs) when measured inputs are the same, but unknown true values of inputs are different. We propose methodologies to obtain output measurement variance estimates and associated parameter estimates for both situations. We also propose bootstrap methods for obtaining joint‐confidence information for the resulting parameter estimates. A copolymerization case study is used to illustrate the proposed techniques. We show that different assumptions noticeably affect the uncertainties in the resulting reactivity‐ratio estimates.

A comparison of strategies for generating artificial replicates in RNA-seq experiments

Due to the overall high costs, technical replicates are usually omitted in RNA-seq experiments, but several methods exist to generate them artificially. Bootstrapping reads from FASTQ-files has recently been used in the context of other NGS analyses and can be used to generate artificial technical replicates. Bootstrapping samples from the columns of the expression matrix has already been used for DNA microarray data and generates a new artificial replicate of the whole experiment. Mixing data of individual samples has been used for data augmentation in machine learning. The aim of this comparison is to evaluate which of these strategies are best suited to study the reproducibility of differential expression and gene-set enrichment analysis in an RNA-seq experiment. To study the approaches under controlled conditions, we performed a new RNA-seq experiment on gene expression changes upon virus infection compared to untreated control samples. In order to compare the approaches for artificial replicates, each of the samples was sequenced twice, i.e. as true technical replicates, and differential expression analysis and GO term enrichment analysis was conducted separately for the two resulting data sets. Although we observed a high correlation between the results from the two replicates, there are still many genes and GO terms that would be selected from one replicate but not from the other. Cluster analyses showed that artificial replicates generated by bootstrapping reads produce it p values and fold changes that are close to those obtained from the true data sets. Results generated from artificial replicates with the approaches of column bootstrap or mixing observations were less similar to the results from the true replicates. Furthermore, the overlap of results among replicates generated by column bootstrap or mixing observations was much stronger than among the true replicates. Artificial technical replicates generated by bootstrapping sequencing reads from FASTQ-files are better suited to study the reproducibility of results from differential expression and GO term enrichment analysis in RNA-seq experiments than column bootstrap or mixing observations. However, FASTQ-bootstrapping is computationally more expensive than the other two approaches. The FASTQ-bootstrapping may be applicable to other applications of high-throughput sequencing.

POIBM: batch correction of heterogeneous RNA-seq datasets through latent sample matching.

MotivationRNA sequencing and other high-throughput technologies are essential in understanding complex diseases, such as cancers, but are susceptible to technical factors manifesting as patterns in the measurements. These batch patterns hinder the discovery of biologically relevant patterns. Unbiased batch effect correction in heterogeneous populations currently requires special experimental designs or phenotypic labels, which are not readily available for patient samples in existing datasets.ResultsWe present POIBM, an RNA-seq batch correction method, which learns virtual reference samples directly from the data. We use a breast cancer cell line dataset to show that POIBM exceeds or matches the performance of previous methods, while being blind to the phenotypes. Further, we analyze The Cancer Genome Atlas RNA-seq data to show that batch effects plague many cancer types; POIBM effectively discovers the true replicates in stomach adenocarcinoma; and integrating the corrected data in endometrial carcinoma improves cancer subtyping.Availability and implementation https://bitbucket.org/anthakki/poibm/ (archived at https://doi.org/10.5281/zenodo.6122436).Supplementary information Supplementary data are available at Bioinformatics online.

Overcoming Obstacles - Biomimetic Lessons from the Swarming Behavior of Artemia Franciscana

We investigated the formation of Artemia franciscana swarms of freshly hatched instar I nauplii larvae. Nauplii were released into light gradients but then interrupted by light-direction changes, small obstacles, or long barriers. All experiments were carried out horizontally. Each experiment used independent replicates. Freshly produced Artemia broods were harvested from independent incubators thus providing true replicate cohorts of Artemia subjected as replicates to the experimental treatments. We discovered that Artemia nauplii swarms can: 1. repeatedly react to non-obstructed light gradients that undergo repeated direction-changes and do so in a consistent way, 2. find their way to a light source within maze-like arrangements made from small transparent obstacles, 3. move as a swarm around extended transparent barriers, following a light gradient. This paper focuses on the recognition of whole-swarm behaviors, the description thereof and the recognition of differences in whole-swarm movements comparing non-obstructed swarming with swarms encountering obstacles. Investigations of the within-swarm behaviors of individual Artemia nauplii and their interactions with neighboring nauplii are in progress, e.g. in order to discover the underlying swarming algorithms and differences thereof comparing non-obstructed vs. obstructed pathways.

Urea and Acetamide Rich Solutions Circumvent the Strand Inhibition Problem to Allow Multiple Rounds of DNA and RNA Copying.

For decades prebiotic chemists have attempted to achieve replication of RNA under prebiotic conditions with only limited success. One of the long-recognized impediments to achieving true replication of a duplex (copying of both strands) is the so-called strand inhibition problem. Specifically, while the two strands of an RNA (or DNA) duplex can be separated by heating, upon cooling the strands of a duplex will reanneal before mononucleotide or oligonucleotide substrates can bind to the individual strands. Here we demonstrate that a class of plausible prebiotic solvents, when coupled with thermal cycling and varying levels of hydration, circumvents the strand inhibition problem, and allows multiple rounds of information transfer from both strands of a duplex (replication). Replication was achieved by simultaneous ligation of oligomers that bind to their templates with the aid of the solvents. The solvents used consisted of concentrated solutions of urea and acetamide in water (UAcW), components that were likely abundant on the early Earth. The UAcW solvent system favors the annealing of shorter strands over the re-annealing of long strands, thereby circumventing strand inhibition. We observed an improvement of DNA and RNA replication yields by a factor of 100× over aqueous buffer. Information transfer in the UAcW solvent system is robust, being achieved for a range of solvent component ratios, various drying conditions, and in the absence or presence of added salts.

The Sedimentary Geochemistry and Paleoenvironments Project.

The Sedimentary Geochemistry and Paleoenvironments Project.

Importance of sources of variability, scales and experimental design: A case study about the effects of biochar and slurry application on soil properties in agricultural silty loam soils

AbstractIn designed experiments, different sources of variability and an adequate scale of measurement need to be considered, but not all approaches in common usage are equally valid. In order to elucidate the importance of sources of variability and choice of scale, we conducted an experiment where the effects of biochar and slurry applications on soil properties related to soil fertility were studied for different designs: (a) for a field‐scale sampling design with either a model soil (without natural variability) as an internal control or with composited soils, (b) for a design with a focus on amendment variabilities, and (c) for three individual field‐scale designs with true field replication and a combined analysis representative of the population of loess‐derived soils. Three silty loam sites in Germany were sampled and the soil macroaggregates were crushed. For each design, six treatments (0, 0.15 and 0.30 g slurry‐N kg−1 with and without 30 g biochar kg−1) were applied before incubating the units under constant soil moisture conditions for 78 days. CO2 fluxes were monitored and soils were analysed for macroaggregate yields and associated organic carbon (C). Mixed‐effects models were used to describe the effects. For all soil properties, results for the loess sites differed with respect to significant contributions of fixed effects for at least one site, suggesting the need for a general inclusion of different sites. Analysis using a multilevel model allowed generalizations for loess soils to be made and showed that site:slurry:biochar and site:slurry interactions were not negligible for macroaggregate yields. The use of a model soil as an internal control enabled observation of variabilities other than those related to soils or amendments. Experiments incorporating natural variability in soils or amendments resulted in partially different outcomes, indicating the need to include all important sources of variability.Highlights Effects of biochar and slurry applications were studied for different designs and mixed‐effects models were used to describe the effects. Including an internal control allowed observation of, e.g., methodological and analytical variabilities. The results suggested the need for a general inclusion of different sites. Analysis using a multilevel model allowed generalizations for loess soils. The results indicated the need to include all important sources of variability.

Replication, pseudoreplication and model experiment in the study of population genetics

The paper is dedicated to the problem of true and pseudoreplication of a biological experiment, in particular in the educational process. It was found that this issue is relatively new and actual for the methodology of biological experiments in general. Its solution in science ensures the veracity of the results obtained and the relevancy of the formulated conclusions. In biology teaching methods at school, the problem of true and pseudoreplication of the experiment was not reflected. The author covers an issue of true replication teaching when setting up a model experiment to study genetic-evolutionary processes in populations. The paper discloses the experience in evolution of a model experiment and its development aimed at formation of ideas about technical and biological replication by the example of study of the genetic structure of an ideal population in generations. For this purpose, there was developed a web page that allows to automatically implement technical and biological experiment replication. There was described an experience of approbation of the proposed variant of the experiment, and its difficulties and advantages were revealed.

Residual SARS-CoV-2 RNA in nasal swabs of convalescent COVID-19 patients: Is prolonged quarantine always justified?

Real-time reverse transcription PCR is currently the most sensitive method to detect severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Defining whether a patient could be contagious or not contagious in the presence of residual SARS-CoV-2 RNA is of extreme importance in the context of public health. In this prospective multicenter study, virus isolation was prospectively attempted in 387 nasal swabs from clinically recovered patients showing low viral load (quantification cycle, Cq, value greater than 30). The median Cq value was 36.8 (range 30.0–39.4). Overall, a cytopathic effect was detected in nine samples, corresponding to a culture positivity rate of 2.3% (9/387). The results of this study help to dissect true virus replication and residual viral RNA detection in recovered patients.

Fauna edáfica e suas relações com atributos químicos, físicos e microbiológicos em Floresta de Araucária

A permanente cobertura vegetal do solo, em Florestas de Araucária, contribui na conservação de sua biodiversidade edáfica. O objetivo deste trabalho foi avaliar o potencial da fauna edáfica e das variáveis ambientais explicativas (físico-químicas e microbiológicas do solo) na discriminação de florestas com araucária nativa e reflorestada. Avaliaram-se florestas com Araucaria angustifolia nativa (NF) e reflorestada (RF) em três regiões distintas no estado de São Paulo, representando três repetições. Em cada área, 15 amostras de solo foram coletadas para avaliação dos atributos físicos, químicos e microbiológicos e, nos mesmos pontos, procedeu-se às coletas da fauna utilizando-se o método de armadilhas de queda (Pitfall traps). A fauna do solo foi influenciada pela sazonalidade, apresentando maior abundância de indivíduos no verão. Os grupos Collembola, Formicidae e Coleoptera foram os mais abundantes, independentemente da época de amostragem e tipo de floresta. A Análise de Componentes Principais (ACP) e Análise Canônica Discriminante (ACD) claramente diferenciaram as áreas de araucárias. Na ACP, Hemiptera, Collembola e Diplopoda ficaram associados à RF no inverno, principalmente pelos maiores valores de matéria seca da serapilheira. Já em NF, foram os grupos Orthoptera, Hymenoptera, Araneae e Coleoptera, explicados pela melhor qualidade do solo e da serapilheira. No verão, ficaram associados à NF, os grupos Formicidae, Hemiptera, Orthoptera, Araneae, Coleoptera e Collembola, explicados pelos maiores valores de CBM, Ca, P, C-org, macroporosidade e atividade de desidrogenase. Na ACD, a abundância de grupos taxonômicos foi o atributo da fauna edáfica mais importante para a discriminação das florestas. Da mesma forma, a umidade do solo, teor de P no solo, porosidade total e teor de S na serapilheira contribuíram na discriminação das florestas. A semelhança entre as áreas, em relação aos grupos da fauna edáfica, indica que está ocorrendo uma estabilidade das áreas reflorestadas comparável às nativas.

Assessing Replicability of Machine Learning Results: An Introduction to Methods on Predictive Accuracy in Social Sciences

Machine learning methods have become very popular in diverse fields due to their focus on predictive accuracy, but little work has been conducted on how to assess the replicability of their findings. We introduce and adapt replication methods advocated in psychology to the aims and procedural needs of machine learning research. In Study 1, we illustrate these methods with the use of an empirical data set, assessing the replication success of a predictive accuracy measure, namely, R2 on the cross-validated and test sets of the samples. We introduce three replication aims. First, tests of inconsistency examine whether single replications have successfully rejected the original study. Rejection will be supported if the 95% confidence interval (CI) of R2 difference estimates between replication and original does not contain zero. Second, tests of consistency help support claims of successful replication. We can decide apriori on a region of equivalence, where population values of the difference estimates are considered equivalent for substantive reasons. The 90% CI of a different estimate lying fully within this region supports replication. Third, we show how to combine replications to construct meta-analytic intervals for better precision of predictive accuracy measures. In Study 2, R2 is reduced from the original in a subset of replication studies to examine the ability of the replication procedures to distinguish true replications from nonreplications. We find that when combining studies sampled from same population to form meta-analytic intervals, random-effects methods perform best for cross-validated measures while fixed-effects methods work best for test measures. Among machine learning methods, regression was comparable to many complex methods, while support vector machine performed most reliably across a variety of scenarios. Social scientists who use machine learning to model empirical data can use these methods to enhance the reliability of their findings.

A survey of bacteria associated with various life stages of primary colonizers: Lucilia sericata and Phormia regina

Blow flies are common primary colonizers of carrion, play an important role in the transfer of microbes between environments, and serve as a vector for many human pathogens. While some investigation has begun regarding the bacteria associated with different life stages of blow flies, a well replicated study is currently not available for the majority of blow flies. This study investigated bacteria associated with successive life stages of blow fly species Lucilia sericata and Phormia regina. A total of 38 samples were collected from four true replicates of L. sericata and P. regina. Variable region four (V4) of 16S ribosomal DNA (16S rDNA) was amplified and sequenced on MiSeq FGx sequencing platform using universal 16S rDNA primers and dual-index sequencing strategy. Bacterial communities associated with different life stages of L. sericata and P. regina didn't differ significantly from each other. In both blow fly species, Bacilli (e.g., Lactococcus) and Gammaproteobacteria (e.g., Providencia) constituted >95% of all bacterial classes across all life stages. At the genus level, Vagococcus and Leuconostoc were present at relatively high abundances in L. sericata whereas Yersinia and Proteus were present at comparatively high abundances in P. regina. Overall, information on bacterial structures associated with various life stages of blow flies can help scientists in better understanding or management of vector-borne pathogen dispersal and in increasing the accuracy of microbial evidence based postmortem interval (PMI) prediction models.

Clustering-independent analysis of genomic data using spectral simplicial theory.

The prevailing paradigm for the analysis of biological data involves comparing groups of replicates from different conditions (e.g. control and treatment) to statistically infer features that discriminate them (e.g. differentially expressed genes). However, many situations in modern genomics such as single-cell omics experiments do not fit well into this paradigm because they lack true replicates. In such instances, spectral techniques could be used to rank features according to their degree of consistency with an underlying metric structure without the need to cluster samples. Here, we extend spectral methods for feature selection to abstract simplicial complexes and present a general framework for clustering-independent analysis. Combinatorial Laplacian scores take into account the topology spanned by the data and reduce to the ordinary Laplacian score when restricted to graphs. We demonstrate the utility of this framework with several applications to the analysis of gene expression and multi-modal genomic data. Specifically, we perform differential expression analysis in situations where samples cannot be grouped into distinct classes, and we disaggregate differentially expressed genes according to the topology of the expression space (e.g. alternative paths of differentiation). We also apply this formalism to identify genes with spatial patterns of expression using fluorescence in-situ hybridization data and to establish associations between genetic alterations and global expression patterns in large cross-sectional studies. Our results provide a unifying perspective on topological data analysis and manifold learning approaches to the analysis of large-scale biological datasets.