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Complex biodiversity patterns arise in marine systems due to overlapping ecological processes, including organism interactions, resource distribution, and environmental conditions. Despite the importance of documenting these patterns, describing diversity in natural ecosystems remains challenging. Here, we investigate three nearshore sub-Arctic sites to describe benthic macroinfaunal taxa and biological traits, with the ultimate aim of determining whether common diversity metrics and typical sampling efforts adequately capture community composition in these systems. First, we assess how diversity relates to sediment depth, and examine relationships among commonly used taxonomic and functional diversity indices. Second, using a power analysis, we explore how sampling effort influences the interpretation of diversity patterns in coastal systems. We report significant variation in community composition among sites, even across small spatial scales of kilometers, and find that taxonomically diverse communities do not necessarily correspond to high functional diversity. We further find that although environmental factors such as sediment depth consistently affect macroinfaunal diversity, the direction and magnitude of these relationships are site dependent. Finally, we demonstrate that typical sampling effort for coastal benthic studies may not capture macroinfaunal community composition adequately, potentially obscuring hotspots in common diversity metrics such as taxonomic or functional richness. Conversely, indices such as Simpson's diversity may be well-suited to resource-limited studies with restricted sampling capacity. Our results highlight the importance of adopting a multi-pronged approach to biodiversity assessment and determining optimal sample sizes for a wide range of marine benthic systems, particularly in the context of biodiversity monitoring for conservation purposes. Funding provided by: Ocean Frontier InstituteCrossref Funder Registry ID: https://ror.org/048py7387Award Number: Funding provided by: Natural Sciences and Engineering Research CouncilCrossref Funder Registry ID: https://ror.org/01h531d29Award Number:

Release version used in Yang et al. 2023 Methods in Ecology and Evolution PiCAM is a custom-grade phenocam system designed by the Terrestrial Ecosystem Science & Technology group at Brookhaven National Laboratory. The PiCAM has the advantage of being compact, low power cost, and lightweight, particularly suitable for Arctic environments. It was desinged to operate for at least a fiscal year (6 images per day) with three AA lithium batteries. Given its compact and light-weight feature, PiCAM can be deployed on small hosts (e.g., stakes), addressing the challenges of deploying heavy infrastructure commonly needed for commercial phenocams.

2D long-term marsh evolution model based on tidal dispersion. See also: https://csdms.colorado.edu/wiki/Model:MarshMorpho2D

r-DMFA protocol which can be used to reproduce the results in the paper. To reproduce the results presented in the paper, we have incorporated a supplementary module into the original computational framework. This module encompasses necessary modifications to enhance the analysis capabilities. We have also employed a customized version of the MATLAB implementation originally developed by Leighty and Antoniewicz in 2011, which is referenced in our paper. This dynamic model, along with the complete set of scripts needed for generating the study's results, has been compiled and made publicly accessible. For researchers and practitioners interested in replicating or extending our study, the dynamic metabolic flux analysis (r-DMFA) protocol, including all the requisite scripts, can be downloaded from the following GitHub repository: https://github.com/ssbio/r-DMFA. Please note that this repository contains all the updated scripts and the dynamic model necessary for the simulations discussed in the paper. By following the instructions and using the provided scripts, users can validate the findings or further explore the model's capabilities.. Prerequisites Before running the scripts, make sure to install all required dependencies listed below: IBM CPLEX solver - An academic license is obtainable for qualified institutions. Baron Solver interfaced with Matlab - MATLAB-BARON interface baron function available at MINLP. Licensed for a fee. Python 3 os (standard library, no need to install) pandas sklearn (specifically sklearn.manifold for TSNE and sklearn.decomposition for PCA) matplotlib (specifically matplotlib.pyplot and mpl_toolkits.mplot3d.Axes3D) numpy seaborn adjustText (can be installed via conda from conda-forge or via pip) scipy (specifically scipy.cluster.hierarchy for linkage and dendrogram) sklearn.cluster for DBSCAN sklearn.metrics for silhouette_score MATLAB: Version 2016b or newer is required. The code was implemented using MATLAB 2022b on the University of Nebraska-Lincoln (UNL) Holland Computing Center’s (HCC) high-performance cluster, SWAN. Running the Workflow This workflow consists of a series of scripts (./code) that should be run in sequential order from S1 to S7. Below is the order of execution along with a brief description of each script: S1a_GetN15AbundanceProfiles.m This script is responsible for obtaining N15 abundance profiles. It is the starting point of the analysis. S2a_Estimate_kcat_values.m Estimates kcat values based on the proposed model in the paper and within the system. S3_GetWTFluxesAndConc.m Calculates the wild type fluxes and concentrations, setting a baseline for further analysis. S4a_GetEffectiveEnzymeDemand.m Determines the effective enzyme demand in the system, which can inform on system bottlenecks. S5_r_dmfa_WithFluxSampling.m Performs flux sampling for robustness analysis of the dynamic metabolic flux analysis (DMFA) model. work with small sample size, say 1E2 or as much as your computing resource can handle. We had more resources at our disposal and thus worked with very big file (4E6 dynamic flux profile samples...) S5b_GetResidualsCorrelationFromDynamicNFluxSamples.m Analyzes residuals and their correlation derived from the DMFA, crucial for model validation. S5c_GetOutliersIndicesBasedonRhoValues.m Identifies outlier indices based on the Rho statistic, aiding in quality control. S6a_GetFoldChangesOfDynFluxSamplesforClustering.m Prepares fold changes of dynamic flux samples for clustering analysis. S6b_Cluster_FC_data.py Clusters flux fold change data, providing insights into control patterns within the metabolic network for the samples size 1E6 as to reproduce Fig. 6 of the paper. S6c_Cluster_FC_data.py A Python script that complements or iterates upon the clustering of flux fold change data for other sample size. S7a_GetMutantsFluxes_EnzKO.m Analyzes the fluxes in various mutant strains with enzyme knockouts to study the effects on the metabolic network. S7b_GetMutantsFluxes_RxnsKO.m Focuses on the flux changes due to reaction knockouts in mutant strains. S7c_GetZindexandRxnsCorrelationsforEnzsKO.m Computes Z-index and correlates it with reactions, for identifying key regulatory reactions. S7d_GetZindexandRxnsCorrelationsforRxnsKO.m An extension or variation of S7c script for deeper analysis at the reaction Level of Z-index and reaction correlations. Color Bar File color_bar.id.mat: This file is used for generating color bars in plots. Additional Scripts DMFASampler_constr.m: r-DMFA framework with constraints and sampler. Get_EnrichmentProfiles.m: Script for obtaining enrichment profiles. knee_pt.m: Determines the knee point which is used in various optimization and cutoff scenarios. README: This file, contains the necessary information to run the scripts. The code has been developed and implemented with MATLAB version 2022b and Python version 3 installed. It is necessary to have near or suitable environments for the scripts to run properly. Additionally, the code is compatible only with MATLAB versions 2016b or newer. Support For any questions or issues, please contact ssbio209@gmail.com Dataset (./data) and Code for submitting the associated manuscript for publication. Implementation protocol for a regularized and constraint-based Dynamic Metabolic Flux Analysis (r-DMFA) framework to predict metabolic shifts due to enzymatic changes. DMFA methodology uses a DOA approach to fit concentration measurements directly and relies on non-steady state mass balances for metabolite pools.

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Mining CRISPRs in Environmental Datasets

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Four related cows showed hairless streaks on various parts of the body with no correlation to the pigmentation pattern. The stripes occurred in a consistent pattern resembling the lines of Blaschko. The non-syndromic hairlessness phenotype observed occurred across three generations of a single family and was compatible with an X-linked mode of inheritance. Linkage analysis and subsequent whole genome sequencing of one affected female identified two perfectly associated non-synonymous sequence variants in the critical interval on bovine chromosome X. Both variants occurred in complete linkage disequilibrium and were absent in more than 3900 controls. An ERCC6L missense mutation was predicted to cause an amino acid substitution of a non-conserved residue. Analysis in mice showed no specific Ercc6l expression pattern related to hair follicle development and therefore ERCC6L was not considered as causative gene. A point mutation at the 5'-splice junction of exon 5 of the TSR2, 20S rRNA accumulation, homolog (S. cerevisiae), gene led to the production of two mutant transcripts, both of which contain a frameshift and generate a premature stop codon predicted to truncate approximately 25% of the protein. Interestingly, in addition to the presence of both physiological TSR2 transcripts, the two mutant transcripts were predominantly detected in the hairless skin of the affected cows. Immunohistochemistry, using an antibody against the N-terminal part of the bovine protein demonstrated the specific expression of the TSR2 protein in the skin and the hair of the affected and the control cows as well as in bovine fetal skin and hair. The RNA hybridization in situ showed that Tsr2 was expressed in pre- and post-natal phases of hair follicle development in mice. Mammalian TSR2 proteins are highly conserved and are known to be broadly expressed, but their precise in vivo functions are poorly understood. Thus, by dissecting a naturally occurring mutation in a domestic animal species, we identified TSR2 as a regulator of hair follicle development.