True Pattern Research Articles

Species‐specific root–shoot ratios in a diverse grassland community

Abstract Existing studies of root/shoot ratios (R/S) in the field show large differences among environments, from high values in unproductive environments to low values in productive environments. However, virtually, all such studies are community‐wide, that is compare biomasses of roots and shoots summed over all coexisting species. Therefore, we do not know to what extent this is due to innate interspecific differences in the R/S ratios or to plastic response to nutrient limitations. This is due to methodological difficulties in assigning roots into species, which preclude the contribution of species‐specific differences to the community‐wide R/S ratio. These limitations can be overcome by DNA analyses. We used qPCR to determine species‐specific R/S ratios in a montane grassland and compared these values to R/S ratios from a two‐season long single‐species pot experiment. To translate the qPCR data to root biomass units, we extracted DNA from the experimental plants to perform calibration for each species and to take into account potential change during the ontogeny of root systems. We used these calibration relationships to recalculate gene copies estimated in a set of field samples to species‐specific root biomass values. All species except one showed fairly high correlation between above‐ground biomass (determined by weighing) and below‐ground biomass (determined by qPCR) across all studied plots in the field. Root/shoot ratios determined from the field were for most species slightly lower than the aggregate values of the whole community determined by weighing both roots and above‐ground biomass. They showed significant correlation with the R/S ratios from the pot experiment, indicating innate differences among species that persist across two very different sets of conditions (multispecies community in the field, single species culture in pots). The field R/S ratios based on qPCR root estimation are informative on the true field patterns. This is supported by their correlation with the pot‐derived values. While the values seem to be slightly underestimated, they are well in the reasonable range of values. This opens a new field of study, namely examination how R/S ratios in the field respond to changed nutrient availability and diversity and species composition of neighbouring species. Read the free Plain Language Summary for this article on the Journal blog.

M2ara: unraveling metabolomic drug responses in whole-cell MALDI mass spectrometry bioassays.

Fast computational evaluation and classification of concentration responses for hundreds of metabolites represented by their mass-to-charge (m/z) ratios is indispensable for unraveling complex metabolomic drug actions in label-free, whole-cell Matrix-Assisted Laser Desorption/Ionization Mass Spectrometry (MALDI MS) bioassays. In particular, the identification of novel pharmacodynamic biomarkers to determine target engagement, potency and potential polypharmacology of drug-like compounds in high-throughput applications requires robust data interpretation pipelines. Given the large number of mass features in cell-based MALDI MS bioassays, reliable identification of true biological response patterns and their differentiation from any measurement artefacts that may be present is critical. To facilitate the exploration of metabolomic responses in complex MALDI MS datasets, we present a novel software tool, M2ara. Implemented as a user-friendly R-based shiny application, it enables rapid evaluation of Molecular High Content Screening (MHCS) assay data. Furthermore, we introduce the concept of Curve Response Score (CRS) and CRS fingerprints to enable rapid visual inspection and ranking of mass features. In addition, these CRS fingerprints allow direct comparison of cellular effects among different compounds. Beyond cellular assays, our computational framework can also be applied to MALDI MS-based (cell-free) biochemical assays in general. The software tool, code and examples are available at https://github.com/CeMOS-Mannheim/M2ara and https://dx.doi.org/10.6084/m9.figshare.25736541. Supplementary material is available at Bioinformatics online.

Revisiting each fracture size and spatial pattern: Inference from rock mass surface to interior

Estimating fracture size is a fundamental aspect of rock engineering. However, determining the most probable diameter (MPD) from a fracture’s surface trace remains challenging in the scientific community. The prevailing methodologies typically infer statistical distributions of fracture sizes rather than specific values. This research presents a novel approach to inferring the MPD and the true spatial distribution pattern of each fracture. The challenge lies in linking the inference process with the trace length of each fracture and the statistical characteristics of the entire outcrop. Additionally, it is necessary to address the non-unique inverse problem. The methodology comprises several key steps. Firstly, the issue of censoring bias is addressed by considering the lengths of the traces contained. Secondly, the orientation bias is corrected using the vector method, and the true mean trace length and standard deviation are estimated and derived. Thirdly, assuming a lognormal distribution for fracture sizes, the mean and standard deviation of diameters are derived through a high-order moment relationship between trace lengths and diameters, validated by Crofton’s theorem. Finally, the MPDs of all trace samples are determined by relating MPDs to trace lengths and the standard deviation of diameters using stereology techniques. Furthermore, the true fracture spatial patterns are inverted based on spatial geometric relationships. The proposed methodology is validated through rigorous Monte Carlo simulation and applied in a practical engineering case study, demonstrating its potential for use in rock engineering applications.

A 5-Year retrospective analysis of adverse events in dentistry at the Dental Hospital, Faculty of Dentistry, Chulalongkorn University

BackgroundPatient safety is a critical concern in dentistry. Adverse events (AEs) can harm patients, increase costs, and decrease satisfaction. Understanding AE types and frequencies is crucial for effective risk management and quality improvement. This study analyzes incident reports to identify preliminary incident patterns as a starting point for developing risk management strategies. However, under-reporting limits the ability to identify true incident patterns, highlighting the need for improved reporting systems and encouragement of incident reporting. Further research is underway to develop such a system and promote reporting to ensure sufficient data quality for effective risk management.MethodsA retrospective analysis of 1,618 incident reports from December 2018 to August 2023 was conducted. A validated classification system, developed from a 5-year retrospective analysis and approved by 14 experts, categorized patient safety incidents, aligning with Thailand’s Hospital Accreditation standards. Descriptive statistics summarized AE frequency and distribution.ResultsOf the reports, 752 were patient safety, 503 personnel safety, and 363 organizational safety incidents. Top patient safety incidents included medical record errors (176), accidental damage (66), post-operative complications (65), medical emergencies (64), and communication errors (53). Personnel safety incidents involved inappropriate working conditions (135) and work-related injuries with contact transmission risk (117). Organizational safety incidents mainly concerned policy and operational processes (131).ConclusionsThis study reveals the preliminary patterns of adverse events (AEs) in dental settings and underscores the limitations due to under-reporting, which affect the ability to fully understand true incident patterns. To effectively manage risks, there is a critical need for improving the existing incident reporting system and encouraging a culture of comprehensive reporting among dental professionals. Future efforts should focus on enhancing reporting systems to ensure high-quality data, enabling better identification of incident trends and supporting targeted risk management strategies to improve patient safety in dentistry.

Estimating the force of infection of four dengue serotypes from serological studies in two regions of Vietnam.

Dengue is endemic in Vietnam with circulation of all four serotypes (DENV1-4) all year-round. It is hard to estimate the disease's true serotype-specific transmission patterns from cases due to its high asymptomatic rate, low reporting rate and complex immunity and transmission dynamics. Seroprevalence studies have been used to great effect for understanding patterns of dengue transmission. We tested 991 population serum samples (ages 1-30 years, collected 2013 to 2017), 531 from Ho Chi Minh City and 460 from Khanh Hoa in Vietnam, using a flavivirus protein microarray assay. By applying our previously developed inference framework to the antibody profiles from this assay, we can (1) determine proportions of a population that have not been infected or infected, once, or more than once, and (2) infer the infecting serotype in those infected once. With these data, we then use mathematical models to estimate the force of infection (FOI) for all four DENV serotypes in HCMC and KH over 35 years up to 2017. Models with time-varying or serotype-specific DENV FOI assumptions fit the data better than constant FOI. Annual dengue FOI ranged from 0.005 (95%CI: 0.003-0.008) to 0.201 (95%CI: 0.174-0.228). FOI varied across serotypes, higher for DENV1 (95%CI: 0.033-0.048) and DENV2 (95%CI: 0.018-0.039) than DENV3 (95%CI: 0.007-0.010) and DENV4 (95%CI: 0.010-0.016). The use of the PMA on serial age-stratified cross-sectional samples increases the amount of information on transmission and population immunity, and should be considered for future dengue serological surveys, particularly to understand population immunity given vaccines with differential efficacy against serotypes, however, there remains limits to what can be inferred even using this assay.

Is the diel cycle of routine metabolic rate in mummichog (Fundulus heteroclitus) affected by near-infrared lighting used for visualizing behavior of fishes at night?

The metabolic rate of a freely moving fish (routine metabolic rate) is tightly coupled with volitional movement (spontaneous activity), both of which commonly show strong daily cycles linked to the species-specific diel activity pattern. Mummichog (Fundulus heteroclitus), an important estuarine fish in the north western Atlantic Ocean, are historically reported as diurnal (i.e., more active during daylight). Our recent laboratory studies on a Bay of Fundy population, however, showed a free-running (i.e., similarly active daytime and night-time) or even nocturnal (i.e., more active at night-time) diel activity pattern. In the laboratory, near-infrared (NIR) illumination is commonly used with a NIR-sensitive camera to visualize fish activity across the light-dark periods of the day. Because NIR light is close to the visible light spectrum and certain fishes show sensitivity to NIR, the use of NIR with mummichog possibly could disturb the animals and obscure the identification of their true diel activity pattern. We aimed to determine if NIR illumination (940 nm wavelength) influences the diel activity pattern of mummichog. We used measurements of routine metabolic rate (oxygen consumption rate, MO2) as a proxy for activity, as evaluating the effect of NIR requires treatments where NIR lights are off, which precludes visualization and direct assessment of fish activity at night-time. We measured routine MO2 of mummichogs over 6 days, exposed to either NIR off-on-off (2 days for each off or on period) or the opposite sequence of NIR on-off-on (to control for time-dependent effects). NIR lights did not influence the diel cycle of routine MO2, and activity by proxy, in mummichog. Thus, NIR illumination is a suitable method to visualize mummichog during light-dark diel cycles. Routine MO2, and presumably activity, was similar or higher during night-time periods compared to daytime periods, confirming a free-running or nocturnal activity pattern for at least certain populations of mummichog.

Noise leads to the perceived increase in evolutionary rates over short time scales.

Across a variety of biological datasets, from genomes to conservation to the fossil record, evolutionary rates appear to increase toward the present or over short time scales. This has long been seen as an indication of processes operating differently at different time scales, even potentially as an indicator of a need for new theory connecting macroevolution and microevolution. Here we introduce a set of models that assess the relationship between rate and time and demonstrate that these patterns are statistical artifacts of time-independent errors present across ecological and evolutionary datasets, which produce hyperbolic patterns of rates through time. We show that plotting a noisy numerator divided by time versus time leads to the observed hyperbolic pattern; in fact, randomizing the amount of change over time generates patterns functionally identical to observed patterns. Ignoring errors can not only obscure true patterns but create novel patterns that have long misled scientists.

The effects of the antenna power pattern uncertainty within a global 21 cm experiment

ABSTRACT Measuring the redshifted sky-averaged neutral hydrogen 21 cm signal with a wide-band antenna operating at metre wavelengths can probe the thermal history of the Universe and the first star and galaxy formation during the Cosmic Dawn. Measurement of this ‘global 21 cm’ signal is extremely challenging due to foreground signals that are orders of magnitude brighter than the cosmological signal, which must be modelled and removed first. The Radio Experiment for the Analysis of Cosmic Hydrogen (REACH) aims to improve this process by simultaneously fitting the full posterior distribution of both the cosmological and foreground signals with Bayesian inference. The method, however, relies on an informed prior; partially derived from a simulated antenna power pattern. This simulated antenna power pattern will differ from the true antenna power pattern of the deployed instrument, and the impact of this uncertainty is unknown. We investigate this problem by forward modelling mock data with different levels of power pattern uncertainty through the REACH pipeline. We construct perturbed antenna power patterns through truncation of a singular-value-decomposed simulated power pattern; using one to generate mock observation data and the others to inform the prior. The power pattern uncertainty is quantified as ΔD, the absolute mean of the difference between the original and perturbed power patterns. Comparing the evidence and root-mean-square error we find that ΔD better than −35 dB, equivalent to millimetre accuracy in the antennas dimensions, is necessary for confident detection of the global signal. We discuss potential solutions to achieve this high level of accuracy.

Stimuli-induced modulation of white light emission in colloidal polymer particles: Design of chemosensors and dynamic chromism toward unclonable patterning and its classification based on machine learning

Stimuli-responsive light-emitting materials have gained significant attention due to their potential applications in optoelectronic devices, chemosensors, anticounterfeiting inks, and unclonable encryption. Coumarin-containing fluorescent polymer particles with light-, pH-, and thermo-responsiveness were prepared by distillation-precipitation polymerization of hydroxyethyl methacrylate and 2-(dimethylamino)ethyl methacrylate. White light emission was observed by physical incorporation of hydroxyl-functionalized spiropyran (SPOH) into the colloidal polymer particles in a certain ratio of the colloidal polymer particles and SPOH content. The white light emission was obtained by modulating pH and temperature to finely tune the Förster resonance energy transfer process and cover the entire visible spectra from the emissive lights. This phenomenon was successfully used to prepare pH sensors, dynamic anticounterfeiting inks, unclonable patterning, and also white light-emitting colloids, films, coatings, and diodes. As an example, a white light-emitting diode was fabricated using the SPOH-doped colloidal polymer particles in poly(vinyl alcohol) matrix having Commission Internationale del'Eclairage color coordinates of (0.31, 0.33). Dynamic chromism and preparation of randomly distributed blue, red, and white light-emitting patterns are examples of advanced anticounterfeiting and encryption methods that were generated via the prepared low-cost environmentally friendly waterborne ink. For the authentication purpose of the unclonable patterns, the classification algorithms were designed based on machine learning to classify the true patterns from the false ones.

Comparative Evaluation of Cephalometric Parameters in Different Kinds of Vertical and Different Kinds of Horizontal Facial Patterns – A Cephalometric Study

Abstract Introduction: An individual can have a vertical, horizontal, or an average growth pattern, usually associated with an increased, decreased, or normal lower anterior facial height (LAFH), respectively. Irrespective of the growth pattern determined cephalometrically, the individual might have acceptable facial proportions. The treatment plan varies for such individuals. Materials and Methods: One hundred cephalograms with normal/increased and decreased clinical LAFH were included in this study. The sample was divided into four groups; 1A and 1B; 2A and 2B. Cephalometric parameters were compared among the vertical growers with increased and normal LAFH (1A and 1B); and horizontal growers with decreased and normal LAFH (2A and 2B) using an unpaired t-test. Results: Statistically significant differences were found between the two variants of vertical and horizontal growers. Cephalometrically hyperdivergent individuals (Group A) showed two variants; clinically normal and increased LAFH. Similarly, hypodivergent group (Group B) had both variants. Conclusions: The cephalometric parameters alone might not always be indicative of the true growth pattern of the individual. The clinical LAFH is a better parameter to determine the facial pattern and must be considered during orthodontic treatment planning.

Dual perspective denoising model for session-based recommendation

Session-based recommendation predicts the next interaction option for an anonymous user based on his/her short-term behavior records. A key challenge faced by session-based recommendation is the processing of noise information. That is, the precise identification of noise items and noise connections. Existing research typically follows two assumptions: (1) the latest interaction item is directly treated as the user’s real-time preference; (2) the temporal order relationship between items is directly treated as the user’s true interaction intention conversion pattern. These strict assumptions ignore two facts: (1) the items that the user accidentally clicked on also exist in the interaction session. (2) the connection edges caused by noise items or user interaction intention drift are also fused into the explicit sequential structure. To address the above two issues, we propose a new Dual Perspective Denoising Model for session-based recommendation called DPDM. Specifically, DPDM mainly consists of two parts: Noise Item Recognition Network and Noise Structure Reconstruction Network. The Noise Item Recognition Network removes noise items from the session through a sparse attention mechanism and virtual target representation. The Noise Structure Reconstruction Network composed of Sequence Module and Co-occurrence Module is used to mine users’ real interaction intention conversion patterns. The Sequence Module captures sequence relationships after removing noise items. The Co-occurrence Module eliminates noise connections by optimizing explicit interaction sequences with L0 activation regularization. Extensive experiments were conducted on three representative real-world datasets, and DPDM achieved positive results, demonstrating the effectiveness of the components in the model.

LOVE Is in the R—Two R Tools for Local Vegetation Reconstruction

Pollen deposition in small lakes and peatlands is composed of local pollen deposition arriving from the nearby vegetation and of regional pollen deposition arriving from farther away. The LOVE model aims to reconstruct past vegetation on a small local scale by extracting only the local pollen signal. To this end, pollen deposition from small sites is related to pollen deposition from large lakes with predominantly regional pollen deposition. We here present a new implementation of the LOVE model in the R environment for statistical computing that is more user friendly than existing implementations. It allows more readily application with fossil pollen data and facilitates extensive testing of the approach in simulated landscapes. The LOVE model derives from critical mathematical assumptions that strongly limit its application. We additionally present LOVEoptim as an adjusted approach to local-scale reconstructions. Other than in LOVE, past local plant abundances are approximated using numerical optimization. Tests in a simulated landscape, which is based on true landscape patterns from digital maps, show that both approaches are valid. Due to fewer mathematical assumptions, LOVEoptim is more widely applicable. The modeling results help to better interpret the spatial scale of vegetation reflected in LOVE/LOVEoptim reconstructions.

Trustworthy interaction model: continuous authentication using time–frequency joint analysis of mouse biometrics

ABSTRACT The rapid development of information technology attracts more attention to information security. Static authentication (SA) technology is widely used in account protection. However, once this shield is broken, it will lead to a serious information security crisis. Therefore, we propose a trustworthy interaction model that continuously authenticates users’ human–computer interaction behaviour, which is a supplement to SA. Specifically, the Hilbert–Huang transform is used to extract time-frequency domain features of user mouse behaviour. Then, the users’ unique mouse behaviour patterns are modelled by LSBT to quantify the deviation between current mouse behaviour and true patterns. Finally, the dynamic trust model is deployed to continuously monitor the current user’s identity credibility score in human-computer interaction. Notably, a 30+ month dataset of the mouse behaviours of 32 participants is collected from a real website to prove the effectiveness of TIM. Two real-world scenarios, comprising 1,344 attacks, were simulated to evaluate the trustworthy interaction model performance. All 1,344 attacks were successfully detected, with an average time of 1.63 min to lock imposters out. The TIM is easy to deploy to continuously authenticate users and can accurately and quickly detect imposters.

Hyphomycetes occurring on Bambusa vulgaris debris: new species and records from the American continent and Brazil

ABSTRACT Bambusa vulgaris is an exotic species for Brazil with high economic value, widely-distributed, highly-adaptable and fast growing. While many studies on fungal diversity on bamboo species have been conducted in the past, most utilised generic or family-level designations for the host, limiting our understanding of the true diversity and distribution patterns of fungi among these plants. Such investigations are particularly limited and conducted intermittently over long periods of time in Brazil. To address this, we conducted a survey of hyphomycetes associated with Bambusa vulgaris debris on the campus of the State University of Feira de Santana, located in Bahia, Brazil. Four new species, Endophragmiella pseudostromatica, Kramasamuha subramanianii, Rhexoampullifera castanedae and Ceratocladium bambusicola are reported. Our results additionally provide the second record for the American continent of Staphylotrichum acaciicola and the first record for this host. Taxonomic treatments, morphological descriptions, comments, and illustrations are provided for the aforementioned species.

Clay larvae do not accurately measure biogeographic patterns in predation

AbstractAimSpatial variation in predation can shape geographic patterns in ecology and evolution, but testing how predation varies across ecosystems is challenging as differing species compositions and defensive adaptations can mask underlying patterns. Recently, biogeography has borrowed a tool from ecology: clay prey models. But clay models have not been adequately tested for geographic comparisons, and a well‐known problem –that clay prey only appeal to a subset of potential predators– could bias detected geographic patterns whenever the relative importance of predator guilds varies among sites. Here, we test whether clay larvae accurately capture geographic differences in predation on real larvae.Location90° of latitude and >2000 m elevation across the Americas.TaxonVertebrate and invertebrate predation on ‘superworms’ (Zophobas larvae).MethodsAcross six sites that vary dramatically in latitude, elevation, and biome, we quantified predation on live, dead, and clay larvae. We physically excluded vertebrate predators from some larvae to distinguish total predation and invertebrate‐only predation.ResultsPredation on live superworms almost doubled from our high‐elevation high‐latitude site to our low‐elevation tropical site. Geographic patterns were consistent among live and dead larvae, but clay larvae missed extremely high predation at some sites and therefore mis‐measured true geographic patterns. Clay larvae did a particularly bad job at capturing geographic patterns in predation by invertebrates, although sample sizes for invertebrate predation were small.Main ConclusionsClay larvae are inappropriate for comparing predation rates across sites. They should be abandoned for biogeographic studies and reserved for comparisons within, rather than across, predator communities.

Analyzing Cattle Activity Patterns with Ear Tag Accelerometer Data.

In this study, we equip two breeds of cattle located in tropical and temperate climates with smart ear tags containing triaxial accelerometers to measure their activity levels across different time periods. We produce activity profiles when measured by each of four statistical features, the mean, median, standard deviation, and median absolute deviation of the Euclidean norm of either unfiltered or high-pass-filtered accelerometer readings over five-minute windows. We then aggregate the values from the 5 min windows into hourly or daily (24 h) totals to produce activity profiles for animals kept in each of the test environments. To gain a better understanding of the variation between the peak and nadir activity levels within a 24 h period, we divide each day into multiple equal-length intervals, which can range from 2 to 96 intervals. We then calculate a statistical measure, called daily differential activity (DDA), by computing the differences in feature values for each interval pair. Our findings demonstrate that patterns within the activity profile are more clearly visualised from readings that have been subject to high-pass filtering and that the median of the acceleration vector norm is the most reliable feature for characterising activity and calculating the DDA measure. The underlying causes for these differences remain elusive and is likely attributable to environmental factors, cattle breeds, or management practices. Activity profiles produced from the standard deviation (a feature routinely applied to the quantification of activity level) showed less uniformity between animals and larger variation in values overall. Assessing activity using ear tag accelerometers holds promise for monitoring animal health and welfare. However, optimal results may only be attainable when true diurnal patterns are detected and accounted for.

Examining Miliary Disease Etiology in a Coccidioides-Endemic Center: A Retrospective Cohort Study.

Background: A miliary pattern on chest imaging is often attributed to tuberculosis (TB) infection. However, a myriad of conditions can cause a miliary pattern, many of which are imminently life-threatening. Research Question: The primary aim of our study is to elucidate the potential causes of miliary chest imaging patterns to improve workup and empiric therapy selection. The secondary aims are to discern the predictors of miliary disease etiology and to determine whether appropriate empiric antimicrobial therapies were given. Study Design and Methods: In this retrospective cohort study, we searched a radiology database for patients with chest imaging studies described by the word "miliary". Subjects were excluded if they were under 18 years of age and if there were insufficient objective data to support a miliary disease etiology. A radiologist independently reviewed all imaging studies, and studies that did not appear to have a true miliary pattern were excluded. The collected data include patient demographics, immunocompromising risk factors, conditions associated with miliary disease, β-D-glucan levels, serum eosinophil count, and empiric therapies received. Results: From our 41-patient cohort, 22 patients (53.7%) were clinically diagnosed with coccidioidomycosis, 8 (19.5%) with TB, 7 (17.1%) with metastatic solid cancer, 1 (2.4%) with lymphoma, 1 (2.4%) with other (Mycobacterium simiae), and 3 (7.3%) with unknown diseases (the sum equals 42 patients because one individual was diagnosed with both coccidioidomycosis and TB). All six patients with greater than 500 eosinophils/μL were diagnosed with coccidioidomycosis. Of the 22 patients diagnosed with coccidioidomycosis, 20 (90.91%) were empirically treated with an antifungal regimen. Of the eight patients with TB, six were empirically treated for TB. Interpretation: Based on our data from a Coccidioides-endemic region with close proximity to tuberculosis-endemic areas, the leading cause of miliary disease is coccidioidomycosis, although TB and cancer are also common etiologies. Serum eosinophilia and elevated β-D-glucan levels were strongly predictive of coccidioidomycosis in our patient cohort with a miliary chest imaging pattern.

Identifying dietary consumption patterns from survey data: a Bayesian nonparametric latent class model.

Dietary assessments provide the snapshots of population-based dietary habits. Questions remain about how generalisable those snapshots are in national survey data, where certain subgroups are sampled disproportionately. We propose a Bayesian overfitted latent class model to derive dietary patterns, accounting for survey design and sampling variability. Compared to standard approaches, our model showed improved identifiability of the true population pattern and prevalence in simulation. We focus application of this model to identify the intake patterns of adults living at or below the 130% poverty income level. Five dietary patterns were identified and characterised by reproducible code/data made available to encourage further research.

Revisiting chromatin packaging in mouse sperm.

Mammalian sperm show an unusual and heavily compacted genomic packaging state. In addition to its role in organizing the compact and hydrodynamic sperm head, it has been proposed that sperm chromatin architecture helps to program gene expression in the early embryo. Scores of genome-wide surveys in sperm have reported patterns of chromatin accessibility, nucleosome localization, histone modification, and chromosome folding. Here, we revisit these studies in light of recent reports that sperm obtained from the mouse epididymis are contaminated with low levels of cell-free chromatin. In the absence of proper sperm lysis, we readily recapitulate multiple prominent genome-wide surveys of sperm chromatin, suggesting that these profiles primarily reflect contaminating cell-free chromatin. Removal of cell-free DNA, and appropriate lysis conditions, are together required to reveal a sperm chromatin state distinct from most previous reports. Using ATAC-seq to explore relatively accessible genomic loci, we identify a landscape of open loci associated with early development and transcriptional control. Histone modification and chromosome folding profiles also strongly support the hypothesis that prior studies suffer from contamination, but technical challenges associated with reliably preserving the architecture of the compacted sperm head prevent us from confidently assaying true localization patterns for these epigenetic marks. Together, our studies show that our knowledge of chromosome packaging in mammalian sperm remains largely incomplete, and motivate future efforts to more accurately characterize genome organization in mature sperm.

Feature-based and shape-match classifications of animal population time series

Estimation of long-term population trends can help population ecologists and conservation biologists assess the status and conditions of wildlife populations to inform the decision making of wildlife management and conservation. Machine learning methods such as time series clustering classify population time series into different clusters that have different trends and dynamic patterns as an alternative to statistical models. We used dynamic time warping (DTW) clustering and feature-based clustering to classify 15 rabbit population times in Mississippi, the United States from 1985 to 2005. Our feature-based method used the parameters of the order-2 Gompertz population models (or autoregressive time series models) as the features of population dynamics. We also estimated the long-term trends of the rabbit populations with the Theil-Sen analysis (TSA). The DTW clustering identified two clusters as optimal classification having the highest Silhouette score. Dynamic time warping clustering had 80% consistency in time series memberships with the TSA, whereas feature-based clustering had 87% consistency. Shape-match DTW clustering not only matches the shapes of trajectories, but also trends between time series. The features based on the Gompertz models capture trend, linear, and nonlinear dynamics. Our shape-match and feature-based time series clustering can cluster time series of different lengths and missing data. The Theil-Sen analysis generates an explicit estimate of linear trends and accounts for temporal autocorrelation. Since the true trends or patterns are often unknown, we recommend using two or all three methods of this study to assess the trends of wildlife populations.