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Peptides are at the cutting edge of contemporary research for new potent, selective, and safe therapeutical agents. Their rise has reshaped the pharmaceutical landscape, providing solutions to challenges that traditional small molecules often cannot address. A wide variety of natural and modified peptides have been obtained and studied, and many others are advancing in clinical trials, covering multiple therapeutic areas. As the demand for peptide-based therapies grows, so does the need for sustainable and environmentally friendly synthesis methods. Traditional peptide synthesis, while effective, often involves environmentally draining processes, generating significant waste and consuming vast resources. The integration of green chemistry offers sustainable alternatives, prioritizing eco-friendly processes, waste reduction, and energy conservation. This review delves into the transformative potential of applying green chemistry principles to peptide synthesis by discussing relevant examples of the application of such approaches to the production of active pharmaceutical ingredients (APIs) with a peptide structure and how these efforts are critical for an effective green transition era in the pharmaceutical field.

Envirotyping is an essential technique used to unfold the nongenetic drivers associated with the phenotypic adaptation of living organisms. Here, we introduce the EnvRtype R package, a novel toolkit developed to interplay large-scale envirotyping data (enviromics) into quantitative genomics. To start a user-friendly envirotyping pipeline, this package offers: (1) remote sensing tools for collecting (get_weather and extract_GIS functions) and processing ecophysiological variables (processWTH function) from raw environmental data at single locations or worldwide; (2) environmental characterization by typing environments and profiling descriptors of environmental quality (env_typing function), in addition to gathering environmental covariables as quantitative descriptors for predictive purposes (W_matrix function); and (3) identification of environmental similarity that can be used as an enviromic-based kernel (env_typing function) in whole-genome prediction (GP), aimed at increasing ecophysiological knowledge in genomic best-unbiased predictions (GBLUP) and emulating reaction norm effects (get_kernel and kernel_model functions). We highlight literature mining concepts in fine-tuning envirotyping parameters for each plant species and target growing environments. We show that envirotyping for predictive breeding collects raw data and processes it in an eco-physiologically smart way. Examples of its use for creating global-scale envirotyping networks and integrating reaction-norm modeling in GP are also outlined. We conclude that EnvRtype provides a cost-effective envirotyping pipeline capable of providing high quality enviromic data for a diverse set of genomic-based studies, especially for increasing accuracy in GP across untested growing environments.

Frequency and severity of heat waves is expected to increase as a consequence of climate change with important impacts on human and ecosystems health. However, while many studies explored the projected occurrence of hot extremes on terrestrial systems, few studies dealt with marine systems, so that both the expected change in marine heat waves occurrence and the effects on marine organisms and ecosystems remain less understood and surprisingly poorly quantified. Here we: i) assess how much more frequent, severe, and depth-penetrating marine heat waves will be in the Mediterranean area in the next decades by post-processing the output of an ocean general circulation model; and ii) show that heat waves increase will impact on many species that live in shallow waters and have reduced motility, and related economic activities. This information is made available also as a dataset of temperature threshold exceedance indexes that can be used in combination with biological information to produce risk assessment maps for target species or biomes across the whole Mediterranean Sea. As case studies we compared projected heat waves occurrence with thermotolerance thresholds of low motility organisms. Results suggest a deepening of the survival horizon for red coral (Corallium rubrum, a commercially exploited benthic species already subjected to heat-related mass mortality events) and coralligenous reefs as well as a reduction of suitable farming sites for the mussel Mythilus galloprovincialis. In recent years Mediterranean circalittoral ecosystems (coralligenous) have been severely and repeatedly impacted by marine heat waves. Our results support that equally deleterious events are expected in the near future also for other ecologically important habitats (e.g. seagrass meadows) and aquaculture activities (bivalvae), and point at the need for mitigation strategies.

The snpReady R package is a new instrument developed to help breeders in genomic projects such as genomic prediction and association studies. This package offers three different methods to build the genomic relationship matrix, a new imputation method for missing markers based on Wright’s theory, and a population genetic overview. Therefore, we implemented three functions ( raw.data , G.matrix , and popgen ). Hence, this tool allows the raw data to be transformed from different genotyping platforms to numeric matrices and performs quality control (missing data and allele frequency). Moreover, the package generates and exports four different relationship matrices (proposed by Yang et al. (N 569:565–569, 2010), VanRaden (JDS 91:4414–23, 2008), and the Gaussian kernel) depending on the purpose and software to be used in further analysis. Finally, based on the genotypic matrix, the package estimates the genetic variability, effective population size, and endogamy, among other population genetic parameters. Empirical comparisons between the method of imputation proposed and other well-known approaches have shown a lower accuracy of imputation, however, with no significant impact on the genome prediction accuracies when a lower amount of missing data is allowed. The functions and arguments were designed to carry out the preparation of genomic datasets in a straightforward, fast, and more computationally efficient way. The package and its details are available at CRAN or http://www.github.com/italo-granato/snpReady .

Based on molecular markers, genomic prediction enables us to speed up breeding schemes and increase the response to selection. There are several high-throughput genotyping platforms able to deliver thousands of molecular markers for genomic study purposes. However, even though its widely applied in plant breeding, species without a reference genome cannot fully benefit from genomic tools and modern breeding schemes. We used a method to assemble a population-tailored mock genome to call single-nucleotide polymorphism (SNP) markers without an available reference genome, and for the first time, we compared the results with standard genotyping platforms (array and genotyping-by-sequencing (GBS) using a reference genome) for performance in genomic prediction models. Our results indicate that using a population-tailored mock genome to call SNP delivers reliable estimates for the genomic relationship between genotypes. Furthermore, genomic prediction estimates were comparable to standard approaches, especially when considering only additive effects. However, mock genomes were slightly worse than arrays at predicting traits influenced by dominance effects, but still performed as well as standard GBS methods that use a reference genome. Nevertheless, the array-based SNP markers methods achieved the best predictive ability and reliability to estimate variance components. Overall, the mock genomes can be a worthy alternative for genomic selection studies, especially for those species where the reference genome is not available.

Genome-wide association studies (GWAS) is one of the most popular methods of studying the genetic control of traits. This methodology has been intensely performed on inbred genotypes to identify causal variants. Nonetheless, the lack of covariance between the phenotype of inbred lines and their offspring in cross-pollinated species (such as maize) raises questions on the applicability of these findings in a hybrid breeding context. To address this topic, we incorporated previously reported parental lines GWAS information into the prediction of a low heritability trait in hybrids. This was done by marker-assisted selection based on significant markers identified in the lines and by genomic prediction having these markers as fixed effects. Additive-dominance GWAS of hybrids, a non-conventional procedure, was also performed for comparison purposes. Our results suggest that incorporating information from parental inbred lines GWAS led to decreases in the predictive ability of hybrids. Correspondingly, inbred lines and hybrids-based GWAS yielded different results. These findings do not invalidate GWAS on inbred lines for selection purposes, but mean that it may not be directly useful for hybrid breeding.

Usually, the comparison among genomic prediction models is based on validation schemes as Repeated Random Subsampling (RRS) or K-fold cross-validation. Nevertheless, the design of training and validation sets has a high effect on the way and subjectiveness we compare models. Those procedures cited above have an overlap across replicates that might cause an overestimated estimate and lack of residuals independence due to resampling issues and might cause less accurate results. Furthermore, ANOVA and multiple-comparison tests, such as Tukey, are not recommended due to assumptions unfulfilled regarding residuals' independence. Thus, we propose a new way to sample observations to build training and validation sets based on cross-validation alpha-based design (CV-α). The CV-α was meant to create several validation scenarios (replicates x folds), regardless of the number of genotypes. Using CV-α, the number of genotypes in the same fold across replicates was much lower than K-fold cross-validation, indicating higher residual independence. Therefore, based on the CV-α results, as proof of concept, via ANOVA, we could compare the proposed methodology to RRS and K-fold cross-validation, applying four genomic prediction models with a simulated and real dataset. Concerning the predictive ability and bias, all validation methods showed similar performance. However, regarding the mean squared error and coefficient of variation, the CV-α method presented the best performance under the evaluated scenarios. Moreover, as it has no additional cost or complexity, it is more reliable and allows non-subjective methods to compare models and factors. Therefore, CV-α can be considered a more precise validation methodology for model selection.

Zooxanthellate corals are known to increase calcification rates when exposed to light, a phenomenon called light-enhanced calcification that is believed to be mediated by symbionts’ photosynthetic activity. There is controversy over the mechanism behind this phenomenon, with hypotheses coarsely divided between abiotic and biologically-mediated mechanisms. At the same time, accumulating evidence shows that calcification in corals relies on active ion transport to deliver the skeleton building blocks into the calcifying medium, making it is an energetically costly activity. Here we build on generally accepted conceptual models of the coral calcification machinery and conceptual models of the energetics of coral-zooxanthellae symbiosis to develop a model that can be used to isolate the biologically-mediated and abiotic effects of photosynthesis, respiration, temperature, and seawater chemistry on coral calcification rates and related metabolic costs. We tested this model on data from the Mediterranean scleractinian Cladocora caespitosa, an acidification resistant species. We concluded that most of the variation in calcification rates due to photosynthesis, respiration and temperature can be attributed to biologically-mediated mechanisms, in particular to the ATP supplied to the active ion transports. Abiotic effects are also present but are of smaller magnitude. Instead, the decrease in calcification rates caused by acidification, albeit small, is sustained by both abiotic and biologically-mediated mechanisms. However, there is a substantial extra cost of calcification under acidified conditions. Based on these findings and on a literature review we suggest that the energy aspect of coral calcification might have been so far underappreciated.

Key messageIt is possible to make inferences regarding the feasibility and applicability of plant high-throughput phenotyping via computer simulations.Protocol validation has been a key challenge to the establishment of high-throughput phenotyping (HTP) in breeding programs. We add to this matter by proposing an innovative way for designing and validating aerial imagery-based HTP approaches with in silico 3D experiments for plant breeding purposes. The algorithm is constructed following a pipeline composed of the simulation of phenotypic values, three-dimensional modeling of trials, and image rendering. Our tool is exemplified by testing a set of experimental setups that are of interest in the context of maize breeding using a comprehensive case study. We report on how the choice of (percentile of) points in dense clouds, the experimental repeatability (heritability), the treatment variance (genetic variability), and the flight altitude affect the accuracy of high-throughput plant height estimation based on conventional structure-from-motion (SfM) and multi-view stereo (MVS) pipelines. The evaluation of both the algorithm and the case study was driven by comparisons of the computer-simulated (ground truth) and the HTP-estimated values using correlations, regressions, and similarity indices. Our results showed that the 3D experiments can be adequately reconstructed, enabling inference-making. Moreover, it suggests that treatment variance, repeatability, and the choice of the percentile of points are highly influential over the accuracy of HTP. Conversely, flight altitude influenced the quality of reconstruction but not the accuracy of plant height estimation. Therefore, we believe that our tool can be of high value, enabling the promotion of new insights and further understanding of the events underlying the practice of high-throughput phenotyping.

Habitat classifications provide guidelines for mapping and comparing marine resources across geographic regions. Calcareous bio-concretions and their associated biota have not been exhaustively categorized. Furthermore, for management and conservation purposes, species and habitat mapping is critical. Recently, several developments have occurred in the field of predictive habitat modeling, and multiple methods are available. In this study, we defined the habitats constituting northern Adriatic biogenic reefs and created a predictive habitat distribution model. We used an updated dataset of the epibenthic assemblages to define the habitats, which we verified using the fuzzy k-means (FKM) clustering method. Redundancy analysis was employed to model the relationships between the environmental descriptors and the FKM membership grades. Predictive modelling was carried out to map habitats across the basin. Habitat A (opportunistic macroalgae, encrusting Porifera, bioeroders) characterizes reefs closest to the coastline, which are affected by coastal currents and river inputs. Habitat B is distinguished by massive Porifera, erect Tunicata, and non-calcareous encrusting algae (Peyssonnelia spp.). Habitat C (non-articulated coralline, Polycitor adriaticus) is predicted in deeper areas. The onshore-offshore gradient explains the variability of the assemblages because of the influence of coastal freshwater, which is the main driver of nutrient dynamics. This model supports the interpretation of Habitat A and C as the extremes of a gradient that characterizes the epibenthic assemblages, while Habitat B demonstrates intermediate characteristics. Areas of transition are a natural feature of the marine environment and may include a mixture of habitats and species. The habitats proposed are easy to identify in the field, are related to different environmental features, and may be suitable for application in studies focused on other geographic areas. The habitat model outputs provide insight into the environmental drivers that control the distribution of the habitat and can be used to guide future research efforts and cost-effective management and conservation plans.