Monitoring Land Degradation Dynamics to Support Landscape Restoration Actions in Remote Areas of the Mediterranean Basin (Murcia Region, Spain).

This study aims to develop a workflow methodology for collecting substantial amounts of Earth Observation data to investigate the effectiveness of landscape restoration actions and support the implementation of the Above Ground Carbon Capture indicator of the Ecosystem Restoration Camps (ERC) Soil Framework. To achieve this objective, the study will utilize the Google Earth Engine API within R (rGEE) to monitor the Normalized Difference Vegetation Index (NDVI). The results of this study will provide a common scalable reference for ERC camps globally, with a specific focus on Camp Altiplano, the first European ERC located in Murcia, Southern Spain. The coding workflow has effectively acquired almost 12 TB of data for analyzing MODIS/006/MOD13Q1 NDVI over a 20-year span. Additionally, the average retrieval of image collections has yielded 120 GB of data for the COPERNICUS/S2_SR 2017 vegetation growing season and 350 GB of data for the COPERNICUS/S2_SR 2022 vegetation winter season. Based on these results, it is reasonable to asseverate that cloud computing platforms like GEE will enable the monitoring and documentation of regenerative techniques to achieve unprecedented levels. The findings will be shared on a predictive platform called Restor, which will contribute to the development of a global ecosystem restoration model.

Custom selected reference genes outperform pre-defined reference genes in transcriptomic analysis.

BACKGROUND: RNA sequencing allows the measuring of gene expression at a resolution unmet by expression arrays or RT-qPCR. It is however necessary to normalize sequencing data by library size, transcript size and composition, among other factors, before comparing expression levels. The use of internal control genes or spike-ins is advocated in the literature for scaling read counts, but the methods for choosing reference genes are mostly targeted at RT-qPCR studies and require a set of pre-selected candidate controls or pre-selected target genes. RESULTS: Here, we report an R-based pipeline to select internal control genes based solely on read counts and gene sizes. This novel method first normalizes the read counts to Transcripts per Million (TPM) and then excludes weakly expressed genes using the DAFS script to calculate the cut-off. It then selects as references the genes with lowest TPM covariance. We used this method to pick custom reference genes for the differential expression analysis of three transcriptome sets from transgenic Arabidopsis plants expressing heterologous fungal effector proteins tagged with GFP (using GFP alone as the control). The custom reference genes showed lower covariance and fold change as well as a broader range of expression levels than commonly used reference genes. When analyzed with NormFinder, both typical and custom reference genes were considered suitable internal controls, but the custom selected genes were more stably expressed. geNorm produced a similar result in which most custom selected genes ranked higher (i.e. were more stably expressed) than commonly used reference genes. CONCLUSIONS: The proposed method is innovative, rapid and simple. Since it does not depend on genome annotation, it can be used with any organism, and does not require pre-selected reference candidates or target genes that are not always available.

Implementing climate change projections in System Dynamics models.

Desertification is the degradation of drylands, which occupy an increasing proportion of the Earth's surface due to global warming. It is currently the most extensive biome on Earth, occupying 45% and one out of every three inhabitants of the planet live in them. One of the most effective ways to face desertification, as Land Degradation Neutrality points out, is prevention. For this purpose, simulation models are very useful tools. Specifically, System Dynamics models are particularly effective, since they allow bringing together the biophysical and socioeconomic variables involved in the formation of the problem. These integrative models, coupled with other tools such as sensitivity analyses, are used to generate desertification early warning indicators. The objective of this programming routine is to implement climate change scenarios in these simulation models. The script presented here was used to evaluate the sensitivity of dehesa rangelands productivity to the increase in the frequency and intensity of droughts due to climate change.•Integrated simulation models are useful tools to understand complex socioecosystems.•Land-use changes foster the alteration of key hydro-bio-geochemical processes.•By means of automated import processes and data analysis programming, it is possible to implement desertification early warning systems.

A Semi-throughput Procedure for Assaying Plant NADP-malate Dehydrogenase Activity Using a Plate Reader.

Chloroplast NADP-dependent malate dehydrogenase (NADP-MDH) is a redox regulated enzyme playing an important role in plant redox homeostasis. Leaf NADP-MDH activation level is considered a proxy for the chloroplast redox status. NADP-MDH enzyme activity is commonly assayed spectrophotometrically by following oxaloacetate-dependent NADPH oxidation at 340 nm. We have developed a plate-adapted protocol to monitor NADP-MDH activity allowing faster data production and lower reagent consumption compared to the classic cuvette format of a spectrophotometer. We provide a detailed procedure to assay NADP-MDH activity and measure the enzyme activation state in purified protein preparations or in leaf extracts. This protocol is provided together with a semi-automatized data analysis procedure using an R script.

R-based method for quantitative analysis of biofilm thickness by using confocal laser scanning microscopy.

Microscopy is mostly the method of choice to analyse biofilms. Due to the high local heterogeneity of biofilms, single and punctual analyses only give an incomplete insight into the local distribution of biofilms. In order to retrieve statistically significant results a quantitative method for biofilm thickness measurements was developed based on confocal laser scanning microscopy and the programming language R. The R-script allows the analysis of large image volumes with little hands-on work and outputs statistical information on homogeneity of surface coverage and overall biofilm thickness. The applicability of the script was shown in microbial fuel cell experiments. It was found that Geobacter sulfurreducens responds differently to poised anodes of different material so that the optimum potential for MFC on poised ITO anodes had to be identified with respect to maximum current density, biofilm thickness and MFC start-up time. Thereby, a positive correlation between current density and biofilm thickness was found, but with no direct link to the applied potential. The optimum potential turned out to be +0.1 V versus SHE. The script proved to be a valuable stand-alone tool to quantify biofilm thickness in a statistically valid manner, which is required in many studies.

Proteomics and phosphoproteomics datasets of a muscle-specific STIM1 loss-of-function mouse model.

STIM1 is an ER/SR transmembrane protein that interacts with ORAI1 to activate store operated Ca2+ entry (SOCE) upon ER/SR depletion of calcium. Normally highly expressed in skeletal muscle, STIM1 deficiency causes significant changes to mitochondrial ultrastructure that do not occur with loss of ORAI1 or other components of SOCE. The datasets in this article are from large-scale proteomics and phosphoproteomics experiments in an inducible mouse model of skeletal muscle-specific STIM1 knock out (KO). These data reveal statistically significant changes in the relative abundance of specific proteins and sites of protein phosphorylation in STIM1 KO gastrocnemius. Protein samples from five biological replicates of each condition (+/- STIM1) were enzymatically digested, the resulting peptides labeled with tandem mass tag (TMT) reagents, mixed, and fractionated. Phosphopeptides were enriched and a small amount of each input retained for protein abundance analysis. All phosphopeptide and input fractions were analyzed by nano LC-MS/MS on a Q Exactive Plus Orbitrap mass spectrometer, searched with Proteome Discoverer software, and processed with in-house R-scripts for data normalization and statistical analysis. Article published in Molecular Metabolism [1].

Automated 16S Sequencing Using an R-Based Analysis Module for Bacterial Identification.

Sanger sequencing of the 16S rRNA gene is routinely used for the identification of bacterial isolates. However, this method is still performed mostly in more-specialized reference laboratories, and traditional protocols can be labor intensive. In this study, 99 clinical bacterial isolates were used to validate a fast, simplified, and largely automated protocol for 16S sequencing. The workflow combines real-time PCR of the first 500 bp of the bacterial 16S rRNA gene and amplicon sequencing on an automated, cartridge-based sequence analyzer. Sequence analysis, NCBI BLAST search, and result interpretation were performed using an automated R-based script. The automated workflow and R analysis described here produced results equal to those of manual sequence analysis. Of the 96 sequences with adequate quality, 90 were concordantly identified to the genus (n = 62) or species level (n = 28) compared with routine laboratory identification of the organism. One organism identification was discordant, and 5 resulted in an inconclusive identification. For sequences that gave a valid result, the overall accuracy of identification to at least the genus level was 98.9%. This simplified sequencing protocol provides a standardized approach to clinical 16S sequencing, analysis, and quality control that would be suited to frontline clinical microbiology laboratories with minimal experience. IMPORTANCE Sanger sequencing of the 16S rRNA gene is widely used as a diagnostic tool for bacterial identification, especially in cases where routine diagnostic methods fail to provide an identification, for organisms that are difficult to culture, or from specimens where cultures remain negative. Our simplified protocol is tailored toward use in frontline laboratories with little to no experience with sequencing. It provides a highly automated workflow that can deliver fast results with little hands-on time. Implementing 16S sequencing in-house saves additional time that is otherwise required to send out isolates/specimens for identification to reference laboratories. This makes results available much faster to physicians who can in turn initiate or adjust patient treatment accordingly.

The kernel-weighted local polynomial regression (KwLPR) approach: an efficient, novel tool for development of QSAR/QSAAR toxicity extrapolation models.

The ability of accurate predictions of biological response (biological activity/property/toxicity) of a given chemical makes the quantitative structure-activity/property/toxicity relationship (QSAR/QSPR/QSTR) models unique among the in silico tools. In addition, experimental data of selected species can also be used as an independent variable along with other structural as well as physicochemical variables to predict the response for different species formulating quantitative activity-activity relationship (QAAR)/quantitative structure-activity-activity relationship (QSAAR) approach. Irrespective of the models' type, the developed model's quality, and reliability need to be checked through multiple classical stringent validation metrics. Among the validation metrics, error-based metrics are more significant as the basic idea of a good predictive model is to improve the predictions' quality by lowering the predicted residuals for new query compounds. Following the concept, we have checked the predictive quality of the QSAR and QSAAR models employing kernel-weighted local polynomial regression (KwLPR) approach over the traditional linear and non-linear regression-based approaches tools such as multiple linear regression (MLR) and k nearest neighbors (kNN). Five datasets which were previously modeled using linear and non-linear regression method were considered to implement the KwPLR approach, followed by comparison of their validation metrics outcomes. For all five cases, the KwLPR based models reported better results over the traditional approaches. The present study's focus is not to develop a better or improved QSAR/QSAAR model over the previous ones, but to demonstrate the advantage, prediction power, and reliability of the KwLPR algorithm and establishing it as a novel, powerful cheminformatic tool. To facilitate the use of the KwLPR algorithm for QSAR/QSPR/QSTR/QSAAR modeling, the authors provide an in-house developed KwLPR.RMD script under the open-source R programming language.

Computer-Aided Medical Microbiology Monitoring Tool: A Strategy to Adapt to the SARS-CoV-2 Epidemic and That Highlights RT-PCR Consistency.

Since the beginning of the COVID-19 pandemic, important health and regulatory decisions relied on SARS-CoV-2 reverse transcription polymerase chain reaction (RT-PCR) results. Our diagnostic laboratory faced a rapid increase in the number of SARS-CoV-2 RT-PCR. To maintain a rapid turnaround time, we moved from a case-by-case validation of RT-PCR results to an automated validation and immediate results transmission to clinicians. A quality-monitoring tool based on a homemade algorithm coded in R was developed, to preserve high quality and to track aberrant results. We present the results of this quality-monitoring tool applied to 35,137 RT-PCR results. Patients tested several times led to 4,939 pairwise comparisons: 88% concordant and 12% discrepant. The algorithm automatically solved 428 out of 573 discrepancies. The most likely explanation for these 573 discrepancies was related for 44.9% of the situations to the clinical evolution of the disease, 27.9% to preanalytical factors, and 25.3% to stochasticity of the assay. Finally, 11 discrepant results could not be explained, including 8 for which clinical data was not available. For patients repeatedly tested on the same day, the second result confirmed a first negative or positive result in 99.2% or 88.9% of cases, respectively. The implemented quality-monitoring strategy allowed to: i) assist the investigation of discrepant results ii) focus the attention of medical microbiologists onto results requiring a specific expertise and iii) maintain an acceptable turnaround time. This work highlights the high RT-PCR consistency for the detection of SARS-CoV-2 and the necessity for automated processes to handle a huge number of microbiological results while preserving quality.

MCDCalc: Markov Chain Molecular Descriptors Calculator for Medicinal Chemistry.

AIMS: Cheminformatics models are able to predict different outputs (activity, property, chemical reactivity) in single molecules or complex molecular systems (catalyzed organic synthesis, metabolic reactions, nanoparticles, etc.). BACKGROUND: Cheminformatics models are able to predict different outputs (activity, property, chemical reactivity) in single molecules or complex molecular systems (catalyzed organic synthesis, metabolic reactions, nanoparticles, etc.). OBJECTIVE: Cheminformatics prediction of complex catalytic enantioselective reactions is a major goal in organic synthesis research and chemical industry. Markov Chain Molecular Descriptors (MCDs) have been largely used to solve Cheminformatics problems. There are different types of Markov chain descriptors such as Markov-Shannon entropies (Shk), Markov Means (Mk), Markov Moments (πk), etc. However, there are other possible MCDs that have not been used before. In addition, the calculation of MCDs is done very often using specific software not always available for general users and there is not an R library public available for the calculation of MCDs. This fact, limits the availability of MCMDbased Cheminformatics procedures. METHODS: We studied the enantiomeric excess ee(%)[Rcat] for 324 α-amidoalkylation reactions. These reactions have a complex mechanism depending on various factors. The model includes MCDs of the substrate, solvent, chiral catalyst, product along with values of time of reaction, temperature, load of catalyst, etc. We tested several Machine Learning regression algorithms. The Random Forest regression model has R2 > 0.90 in training and test. Secondly, the biological activity of 5644 compounds against colorectal cancer was studied. RESULTS: We developed very interesting model able to predict with Specificity and Sensitivity 70-82% the cases of preclinical assays in both training and validation series. CONCLUSION: The work shows the potential of the new tool for computational studies in organic and medicinal chemistry.

Bioinformatic tools for tRNA gene analyses in mitochondrial DNA sequence data.

The data presented here are related to the research article entitled "Hidden cases of tRNA genes duplication and remolding in mitochondrial genomes of amphipods" (Romanova et al., 2020) [1]. Correct tRNA gene sequence annotation in mitochondrial (mt) and nuclear genomes sometimes can be a challenging task because of the differential performances of tRNA annotation/prediction programmes. These programmes may cause false positive or false negative predictions. Moreover, additional difficulties with annotation may be caused by the presence of duplicated tRNA genes and those coding tRNAs with altered identities occurring as due to a mutation in their anticodon sequence (tRNA gene remolding/recruitment). We developed an R script automating the diagnosis of ancestor tRNA gene coding specificity regardless of anticodon sequence based on genetic distance comparison. Some of the predicted tRNA genes from the mt genomes of amphipods are presented. We also developed an R script for estimation of the best mode of sequence alignment, which was applied to determine the best alignment of tRNA genes in [1], but is also suitable for testing of any nucleotide alignment sets used in phylogenetic inferences.

Methylation-sensitive Amplified Polymorphism as a Tool to Analyze Wild Potato Hybrids.

Methylation-Sensitive Amplification Polymorphism (MSAP) is a versatile marker for analyzing DNA methylation patterns in non-model species. The implementation of this technique does not require a reference genome and makes it possible to determine the methylation status of hundreds of anonymous loci distributed throughout the genome. In addition, the inheritance of specific methylation patterns can be studied. Here, we present a protocol for analyzing DNA methylation patterns through MSAP markers in potato interspecific hybrids and their parental genotypes.

Targeting bioactive compounds in natural extracts - Development of a comprehensive workflow combining chemical and biological data.

In natural product drug discovery, several strategies have emerged to highlight specifically bioactive compound(s) within complex mixtures (fractions or crude extracts) using metabolomics tools. In this area, a great deal of interest has raised among the scientific community on strategies to link chemical profiles and associated biological data, leading to the new field called "biochemometrics". This article falls into this emerging research by proposing a complete workflow, which was divided into three major steps. The first one consists in the fractionation of the same extract using four different chromatographic stationary phases and appropriated elution conditions to obtain five fractions for each column. The second step corresponds to the acquisition of chemical profiles using HPLC-HRMS analysis, and the biological evaluation of each fraction. The last step evaluates the links between the relative abundances of molecules present in fractions (peak area) and the global bioactivity level observed for each fraction. To this purpose, an original bioinformatics script (encoded with R Studio software) using the combination of four statistical models (Spearman, F-PCA, PLS, PLS-DA) was here developed leading to the generation of a "Super list" of potential bioactive compounds together with a predictive score. This strategy was validated by its application on a marine-derived Penicillium chrysogenum extract exhibiting antiproliferative activity on breast cancer cells (MCF-7 cells). After the three steps of the workflow, one main compound was highlighted as responsible for the bioactivity and identified as ergosterol. Its antiproliferative activity was confirmed with an IC50 of 0.10 µM on MCF-7 cells. The script efficiency was further demonstrated by comparing the results obtained with a different recently described approach based on NMR profiling and by virtually modifying the data to evaluate the computational tool behaviour. This approach represents a new and efficient tool to tackle some of the bottlenecks in natural product drug discovery programs.

Quantitative Sex Estimation Based on Cranial Traits Using R Functions.

This paper presents an R script that quantifies the shape of selected cranial traits and automates sex estimation. The proposed functions were tested on two modern Greek assemblages. The discriminant variables input in the functions are calculated from a digital photograph of the lateral view of the cranium. The cranial outline is determined using the Canny edge detector and discriminant variables that quantify the shape of the glabella/frontal bone, mastoid process, and external occipital protuberance are computed. The best cross-validated results for pooled sexes in the Athens Collection range from 84.2% to 87.3%, and increase up to 93.9% when half of the sample is used for training and the rest for prediction, while correct classification for the Cretan material is 80-90% for optimum combinations of discriminant variables. The greatest advantage of the proposed method is its straightforward and time-efficient application.

Less Is Better. Avoiding Redundant Measurements in Studies on Wild Birds in Accordance to the Principles of the 3Rs.

The Principles of the 3Rs apply to animal use in research regardless where the research is conducted. In wildlife research, particularly research on wild birds, 3R implementation lags behind research using laboratory, farm, or pet animals. Raised 3R awareness and more field-adapted techniques and protocols are expected to improve the situation. Unpredictable access to animals entices the wildlife researcher to make the most of each caught animal, leading to potential over-use, and violation of the 3Rs. In this study, I statistically screened an existing set of Bean Goose biometric data for the presence of redundant measurements. The results show that it was possible to distinguish between the fabalis and rossicus subspecies (the original aim of the measurements) with fewer measurements (2 vs. 17). Avoidance of the redundant measurements was estimated to reduce both handling time and welfare impact with c. 80%. A robust scheme, supported by an R-script, is presented for continuously weeding out redundant measurements. This scheme is potentially applicable for measurement protocols in any wildlife study, and thus, contributes to the implementation of the principals of the 3Rs in wildlife research in general.

Detection of Unknown Chemical Adduct Modifications on Proteins: From Wet to Dry Laboratory.

The detection and characterization of chemical adducts on proteins is of increasing interest. Here, we described a step-by-step procedure to identify unknown chemical adduct modifications on proteins resulting from the interaction with a given reactive compound. The protocol can be divided into two equally important parts: (1) the wet laboratory work, to produce high quality mass spectrometry (MS) data of in vitro modified proteins and (2) the dry laboratory work, to analyze the generated MS data and provide highly confident qualitative and quantitative results on the chemical composition and amino acid localization of adducts. This protocol is applicable to the study of any pharmaceutical or chemical compound forming covalent protein adducts, detectable in LC-MS/MS experiments.

Rhea: a transparent and modular R pipeline for microbial profiling based on 16S rRNA gene amplicons.

The importance of 16S rRNA gene amplicon profiles for understanding the influence of microbes in a variety of environments coupled with the steep reduction in sequencing costs led to a surge of microbial sequencing projects. The expanding crowd of scientists and clinicians wanting to make use of sequencing datasets can choose among a range of multipurpose software platforms, the use of which can be intimidating for non-expert users. Among available pipeline options for high-throughput 16S rRNA gene analysis, the R programming language and software environment for statistical computing stands out for its power and increased flexibility, and the possibility to adhere to most recent best practices and to adjust to individual project needs. Here we present the Rhea pipeline, a set of R scripts that encode a series of well-documented choices for the downstream analysis of Operational Taxonomic Units (OTUs) tables, including normalization steps, alpha- and beta-diversity analysis, taxonomic composition, statistical comparisons, and calculation of correlations. Rhea is primarily a straightforward starting point for beginners, but can also be a framework for advanced users who can modify and expand the tool. As the community standards evolve, Rhea will adapt to always represent the current state-of-the-art in microbial profiles analysis in the clear and comprehensive way allowed by the R language. Rhea scripts and documentation are freely available at https://lagkouvardos.github.io/Rhea.