Sparse testing designs for optimizing predictive ability in sugarcane populations.

Sugarcane is a crucial crop for sugar and bioenergy production. Saccharose content and total weight are the two main key commercial traits that compose sugarcane's yield. These traits are under complex genetic control and their response patterns are influenced by the genotype-by-environment (G×E) interaction. An efficient breeding of sugarcane demands an accurate assessment of the genotype stability through multi-environment trials (METs), where genotypes are tested/evaluated across different environments. However, phenotyping all genotype-in-environment combinations is often impractical due to cost and limited availability of propagation-materials. This study introduces the sparse testing designs as a viable alternative, leveraging genomic information to predict unobserved combinations through genomic prediction models. This approach was applied to a dataset comprising 186 genotypes across six environments (6×186=1,116 phenotypes). Our study employed three predictive models, including environment, genotype, and genomic markers as main effects, as well as the G×E to predict saccharose accumulation (SA) and tons of cane per hectare (TCH). Calibration sets sizes varying between 72 (6.5%) to 186 (16.7%) of the total number of phenotypes were composed to predict the remaining 930 (83.3%). Additionally, we explored the optimal number of common genotypes across environments for G×E pattern prediction. Results demonstrate that maximum accuracy for SA ( ρ = 0.611 ) and for TCH ( ρ=0.341 ) was achieved using in training sets few (3) to no common (0) genotype across environments maximizing the number of different genotypes that were tested only once. Significantly, we show that reducing phenotypic records for model calibration has minimal impact on predictive ability, with sets of 12 non-overlapped genotypes per environment (72=12×6) being the most convenient cost-benefit combination.

Lignin and functional polymer-based materials: Synthesis, characterization and application for Cr (VI) and As (V) removal from aqueous media.

In this study, lignin derived from corncobs was chemically modified by substituting the hydroxyl groups present in its structure with methacrylate groups through a catalytic reaction using methacrylic anhydride, resulting in methacrylated lignin (ML). These MLs were incorporated in polymerization reaction of the monomer 2-[(acryloyloxy)ethyl trimethylammonium] chloride (Cl-AETA) and Cl-AETA, Cl-AETA/ML polymers were obtained, characterized (spectroscopic, thermal and microscopic analysis), and evaluated for removing Cr (VI) and As (V) from aqueous media in function of pH, contact time, initial metal concentrations and adsorbent amount. The Cl-AETA/ML polymers followed the Langmuir adsorption model for the evaluated metal anions and were able to remove up to 91 % of Cr (VI) with a qmax (maximum adsorption capacity) of 201 mg/g, while for As (V), up to 60 % could be removed with a qmax of 58 mg/g. The results demonstrate that simple modifications in lignin enhance its functionalization and properties, making it suitable for removing contaminants from aqueous media, showing promising results for potential future applications.

A Viral-Encoded Homologue of IPS-1 Modulates Innate Immune Signaling During KSHV Lytic Replication.

Modulation of innate immunity is critical for virus persistence in a host. In particular, viral-encoded disruption of type I interferon, a major antiviral cytokine induced to fight viral infection, is a key component in the repertoire of viral pathogenicity genes. We have identified a previously undescribed open reading frame within the Kaposi's sarcoma-associated herpesvirus (KSHV) genome that encodes a homologue of the human IPS-1 (also referred to as MAVS) protein that we have termed viral-IPS-1 (v-IPS-1). This protein is expressed during the lytic replication program of KSHV, and expression of v-IPS-1 blocks induction of type I interferon upstream of the TRAF3 signaling node including signaling initiated via both the RLR and TLR3/4 signaling axes. This disruption of signaling coincides with destabilization of the cellular innate signaling adaptors IPS-1 and TRIF along with a concatenate stabilization of the TRAF3 protein. Additionally, expression of v-IPS-1 leads to decreased antiviral responses indicating a blot to type I interferon induction during viral infection. Taken together, v-IPS-1 is the first described viral homologue of IPS-1 and this viral protein leads to reprogramming of innate immunity through modulation of type I interferon signaling during KSHV lytic replication.

Principles and challenges of whole cell microbial biosensors in the food industry.

Whole cell microbial biosensors (WCMB) are mostly genetically modified microorganisms used to detect target molecules as indicators of biological and chemical contaminants as well as in the identification of compounds of interest in the food industry. The specificity and sensitivity of these biosensors are achieved through the design of genetic circuits that make use of genetic sequences such as promoters, terminators, genes encoding regulatory proteins or reporter proteins, among others. Despite the advances of WCMBs for their application, significant challenges are faced, such as cell stability, regulatory restrictions, and the need to optimize response times so that they can be a competitive detection tool in the market. This review explores the technological progress, potential and limitations of WCMBs in the food industry, starting by reviewing the operating principles of biosensors. The importance of selecting appropriate chassis cells and the integration of recognition elements and transducers to maximize their effectiveness in the detection of contaminants and compounds of interest in the food industry is highlighted.

Sodium lignosulfonate as an extracting agent of methylene blue dye using a polymer-enhanced ultrafiltration technique.

The purpose of this research was to evaluate the efficacy of sodium lignosulfonate (LS) as a dye adsorbent in the removal of methylene blue (MB) from water by polymer-enhanced ultrafiltration. Various parameters were evaluated, such as membrane molecular weight cut-off, pH, LS dose, MB concentration, applied pressure, and the effect of interfering ions. The results showed that the use of LS generated a significant increase in MB removal, reaching an elimination of up to 98.0 % with 50.0 mg LS and 100 mg L-1 MB. The maximum MB removal capacity was 21 g g-1 using the enrichment method. In addition, LS was reusable for up to four consecutive cycles of dye removal-elution. The removal test in a simulated liquid industrial waste from the textile industry was also effective, with a MB removal of 97.2 %. These findings indicate that LS is highly effective in removing high concentrations of MB dye, suggesting new prospects for its application in water treatment processes.

BULL Database - Spanish Basin attributes for Unravelling Learning in Large-sample hydrology.

We present a novel basin dataset for large-sample hydrological studies in Spain. BULL comprises data for 484 basins, combining hydrometeorological time series with several attributes related to geology, soil, topography, land cover, anthropogenic influence and hydroclimatology. Thus, we followed recommendations in the CARAVAN initiative for generating a truly open global hydrological dataset to collect these attributes. Several climatological data sources were used, and their data were validated by hydrological modelling. One of the main novelties of BULL compared to other national-scale datasets is the analysis of the hydrological alteration of the basins included in this dataset. This aspect is critical in countries such as Spain, which are characterised by rivers suffering from the highest levels of anthropisation. The BULL dataset is freely available at https://zenodo.org/records/10605646 .

Revisiting superiority and stability metrics of cultivar performances using genomic data: derivations of new estimators.

The selection of highly productive genotypes with stable performance across environments is a major challenge of plant breeding programs due to genotype-by-environment (GE) interactions. Over the years, different metrics have been proposed that aim at characterizing the superiority and/or stability of genotype performance across environments. However, these metrics are traditionally estimated using phenotypic values only and are not well suited to an unbalanced design in which genotypes are not observed in all environments. The objective of this research was to propose and evaluate new estimators of the following GE metrics: Ecovalence, Environmental Variance, Finlay-Wilkinson regression coefficient, and Lin-Binns superiority measure. Drawing from a multi-environment genomic prediction model, we derived the best linear unbiased prediction for each GE metric. These derivations included both a squared expectation and a variance term. To assess the effectiveness of our new estimators, we conducted simulations that varied in traits and environment parameters. In our results, new estimators consistently outperformed traditional phenotype-based estimators in terms of accuracy. By incorporating a variance term into our new estimators, in addition to the squared expectation term, we were able to improve the precision of our estimates, particularly for Ecovalence in situations where heritability was low and/or sparseness was high. All methods are implemented in a new R-package: GEmetrics. These genomic-based estimators enable estimating GE metrics in unbalanced designs and predicting GE metrics for new genotypes, which should help improve the selection efficiency of high-performance and stable genotypes across environments.

NCS-1 protein regulates TRPA1 channel through the PI3K pathway in breast cancer and neuronal cells.

The physical and functional interaction between transient receptor potential channel ankyrin 1 (TRPA1) and neuronal calcium sensor 1 (NCS-1) was assessed. NCS-1 is a calcium (Ca2+) sensor found in many tissues, primarily neurons, and TRPA1 is a Ca2+ channel involved not only in thermal and pain sensation but also in conditions such as cancer and chemotherapy-induced peripheral neuropathy, in which NCS-1 is also a regulatory component.We explored the interactions between these two proteins by employing western blot, qRT-PCR, co-immunoprecipitation, Ca2+ transient monitoring with Fura-2 spectrophotometry, and electrophysiology assays in breast cancer cells (MDA-MB-231) with different levels of NCS-1 expression and neuroblastoma cells (SH-SY5Y).Our findings showed that the expression of TRPA1 was directly correlated with NCS-1 levels at both the protein and mRNA levels. Additionally, we found a physical and functional association between these two proteins. Physically, the NCS-1 and TRPA1 co-immunoprecipitate. Functionally, NCS-1 enhanced TRPA1-dependent Ca2+ influx, current density, open probability, and conductance, where the functional effects depended on PI3K. Conclusion: NCS-1 appears to act not only as a Ca2+ sensor but also modulates TRPA1 protein expression and channel function in a direct fashion through the PI3K pathway. These results contribute to understanding how Ca2+ homeostasis is regulated and provides a mechanism underlying conditions where Ca2+ dynamics are compromised, including breast cancer. With a cellular pathway identified, targeted treatments can be developed for breast cancer and neuropathy, among other related diseases.

Genomic selection in plant breeding: Key factors shaping two decades of progress.

Genomic selection, the application of genomic prediction (GP) models to select candidate individuals, has significantly advanced in the past two decades, effectively accelerating genetic gains in plant breeding. This article provides a holistic overview of key factors that have influenced GP in plant breeding during this period. We delved into the pivotal roles of training population size and genetic diversity, and their relationship with the breeding population, in determining GP accuracy. Special emphasis was placed on optimizing training population size. We explored its benefits and the associated diminishing returns beyond an optimum size. This was done while considering the balance between resource allocation and maximizing prediction accuracy through current optimization algorithms. The density and distribution of single-nucleotide polymorphisms, level of linkage disequilibrium, genetic complexity, trait heritability, statistical machine-learning methods, and non-additive effects are the other vital factors. Using wheat, maize, and potato as examples, we summarize the effect of these factors on the accuracy of GP for various traits. The search for high accuracy in GP-theoretically reaching one when using the Pearson's correlation as a metric-is an active research area as yet far from optimal for various traits. We hypothesize that with ultra-high sizes of genotypic and phenotypic datasets, effective training population optimization methods and support from other omics approaches (transcriptomics, metabolomics and proteomics) coupled with deep-learning algorithms could overcome the boundaries of current limitations to achieve the highest possible prediction accuracy, making genomic selection an effective tool in plant breeding.

Maximizing efficiency in sunflower breeding through historical data optimization.

Genomic selection (GS) has become an increasingly popular tool in plant breeding programs, propelled by declining genotyping costs, an increase in computational power, and rediscovery of the best linear unbiased prediction methodology over the past two decades. This development has led to an accumulation of extensive historical datasets with genotypic and phenotypic information, triggering the question of how to best utilize these datasets. Here, we investigate whether all available data or a subset should be used to calibrate GS models for across-year predictions in a 7-year dataset of a commercial hybrid sunflower breeding program. We employed a multi-objective optimization approach to determine the ideal years to include in the training set (TRS). Next, for a given combination of TRS years, we further optimized the TRS size and its genetic composition. We developed the Min_GRM size optimization method which consistently found the optimal TRS size, reducing dimensionality by 20% with an approximately 1% loss in predictive ability. Additionally, the Tails_GEGVs algorithm displayed potential, outperforming the use of all data by using just 60% of it for grain yield, a high-complexity, low-heritability trait. Moreover, maximizing the genetic diversity of the TRS resulted in a consistent predictive ability across the entire range of genotypic values in the test set. Interestingly, the Tails_GEGVs algorithm, due to its ability to leverage heterogeneity, enhanced predictive performance for key hybrids with extreme genotypic values. Our study provides new insights into the optimal utilization of historical data in plant breeding programs, resulting in improved GS model predictive ability.

PTEN loss in glioma cell lines leads to increased extracellular vesicles biogenesis and PD-L1 cargo in a PI3K-dependent manner.

Phosphatase and Tensin Homologue (PTEN) is one of the most frequently lost tumor suppressors in cancer and the predominant negative regulator of the PI3K/AKT signaling axis. A growing body of evidence has highlighted the loss of PTEN with immuno-modulatory functions including the upregulation of the programmed death ligand-1 (PD-L1), an altered tumor derived secretome that drives an immunosuppressive tumor immune microenvironment (TIME), and resistance to certain immunotherapies. Given their roles in immunosuppression and tumor growth, we examined whether the loss of PTEN would impact the biogenesis, cargo, and function of extracellular vesicles (EVs) in the context of the anti-tumor associated cytokine interferon-γ (IFN-γ). Through genetic and pharmacological approaches, we show that PD-L1 expression is regulated by JAK/STAT signaling, not PI3K signaling. Instead, we observe that PTEN loss positively upregulates cell surface levels of PD-L1 and enhances the biogenesis of EVs enriched with PD-L1 in a PI3K-dependent manner. We demonstrate that because of these changes, EVs derived from glioma cells lacking PTEN have a greater ability to suppress T cell receptor (TCR) signaling. Taken together, these findings provide important new insights into how the loss of PTEN can contribute to an immunosuppressive TIME, facilitate immune evasion, and highlight a novel role for PI3K signaling in the regulation of EV biogenesis and the cargo they contain.

Incidence and clinical manifestations of giant cell arteritis in Spain: results of the ARTESER register.

OBJECTIVE: This study aimed to estimate the incidence of giant cell arteritis (GCA) in Spain and to analyse its clinical manifestations, and distribution by age group, sex, geographical area and season. METHODS: We included all patients diagnosed with GCA between 1 June 2013 and 29 March 2019 at 26 hospitals of the National Health System. They had to be aged ≥50 years and have at least one positive results in an objective diagnostic test (biopsy or imaging techniques), meet 3/5 of the 1990 American College of Rheumatology classification criteria or have a clinical diagnosis based on the expert opinion of the physician in charge. We calculated incidence rate using Poisson regression and assessed the influence of age, sex, geographical area and season. RESULTS: We identified 1675 cases of GCA with a mean age at diagnosis of 76.9±8.3 years. The annual incidence was estimated at 7.42 (95% CI 6.57 to 8.27) cases of GCA per 100 000 people ≥50 years with a peak for patients aged 80-84 years (23.06 (95% CI 20.89 to 25.4)). The incidence was greater in women (10.06 (95% CI 8.7 to 11.5)) than in men (4.83 (95% CI 3.8 to 5.9)). No significant differences were found between geographical distribution and incidence throughout the year (p=0.125). The phenotypes at diagnosis were cranial in 1091 patients, extracranial in 337 patients and mixed in 170 patients. CONCLUSIONS: This is the first study to estimate the incidence of GCA in Spain at a national level. We found a predominance among women and during the ninth decade of life with no clear variability according to geographical area or seasons of the year.

Dissecting the complex genetic basis of pre- and post-harvest traits in Vitis vinifera L. using genome-wide association studies.

Addressing the pressing challenges in agriculture necessitates swift advancements in breeding programs, particularly for perennial crops like grapevines. Moving beyond the traditional biparental quantitative trait loci (QTL) mapping, we conducted a genome-wide association study (GWAS) encompassing 588 Vitis vinifera L. cultivars from a Chilean breeding program, spanning three seasons and testing 13 key yield-related traits. A strong candidate gene, Vitvi11g000454, located on chromosome 11 and related to plant response to biotic and abiotic stresses through jasmonic acid signaling, was associated with berry width and holds potential for enhancing berry size in grape breeding. We also mapped novel QTL associated with post-harvest traits across chromosomes 2, 4, 9, 11, 15, 18, and 19, broadening our grasp on the genetic intricacies dictating fruit post-harvest behavior, including decay, shriveling, and weight loss. Leveraging gene ontology annotations, we drew parallels between traits and scrutinized candidate genes, laying a robust groundwork for future trait-feature identification endeavors in plant breeding. We also highlighted the importance of carefully considering the choice of the response variable in GWAS analyses, as the use of best linear unbiased estimators (BLUEs) corrections in our study may have led to the suppression of some common QTL in grapevine traits. Our results underscore the imperative of pioneering non-destructive evaluation techniques for long-term conservation traits, offering grape breeders and cultivators insights to improve post-harvest table grape quality and minimize waste.

Synthesis, Characterization and Catechol-Based Bioinspired Adhesive Properties in Wet Medium of Poly(2-Hydroxyethyl Methacrylate-co-Acrylamide) Hydrogels.

Hydrogels consist of crosslinked hydrophilic polymers from which their mechanical properties can be modulated for a wide variety of applications. In the last decade, many catechol-based bioinspired adhesives have been developed following the strategy of incorporating catechol moieties into polymeric backbones. In this work, in order to further investigate the adhesive properties of hydrogels and their potential advantages, several hydrogels based on poly(2-hydroxyethyl methacrylate-co-acrylamide) with N'N-methylene-bisacrylamide (MBA), without/with L-3,4-dihydroxyphenylalanine (DOPA) as a catecholic crosslinker, were prepared via free radical copolymerization. 2-Hydroxyethyl methacrylate (HEMA) and acrylamide (AAm) were used as comonomers and MBA and DOPA both as crosslinking agents at 0.1, 0.3, and 0.5 mol.-%, respectively. The polymeric hydrogels were characterized by Fourier transform infrared spectroscopy (FT-IR), thermal analysis and swelling behavior analysis. Subsequently, the mechanical properties of hydrogels were determined. The elastic properties of the hydrogels were quantified using Young's modulus (stress-strain curves). According to the results herein, the hydrogel with a feed monomer ratio of 1:1 at 0.3 mol.-% of MBA and DOPA displayed the highest rigidity and higher failure shear stress (greater adhesive properties). In addition, the fracture lap shear strength of the biomimetic polymeric hydrogel was eight times higher than the initial one (only containing MBA); however at 0.5 mol.-% MBA/DOPA, it was only two times higher. It is understood that when two polymer surfaces are brought into close contact, physical self-bonding (Van der Waals forces) at the interface may occur in an -OH interaction with wet contacting surfaces. The hydrogels with DOPA provided an enhancement in the flexibility compared to unmodified hydrogels, alongside reduced swelling behavior on the biomimetic hydrogels. This approach expands the possible applications of hydrogels as adhesive materials, in wet conditions, within scaffolds that are commonly used as biomaterials in cartilage tissue engineering.

Use of a rapid triage assessment tool to discriminate the need for hospitalisation in patients with severe COVID-19 infection presenting to an outpatient clinic: a single-centre, prospective cohort study.

OBJECTIVES: The WHO designated individuals with low oxygen saturation, SpO2<94%, as severe SARS-CoV2 infection (COVID-19) and recommendations to seek care in a hospital setting were advised. A rapid, office-based method to select patients with severe COVID-19 who need intensive care was necessary during the peak of the pandemic. DESIGN, SETTING AND PARTICIPANTS: This is a prospective cohort study of patients with confirmed severe COVID-19 between September 2020 and April 2021. OUTCOME MEASURES AND ANALYSIS: Oxygen saturation was obtained at rest (SpO2r), following exertion from a 20 m walk test (SpO2e), and the difference was calculated (SpO2Δ). Radiographs and laboratory values were obtained and recorded. Logistic regression models were used to determine variables associated with hospitalisation. A lung injury score was used to quantify pulmonary involvement. RESULTS: Out of 103 patients enrolled with severe COVID-19 infection, 19 (18.4%) were admitted to the hospital (no deaths). Patients managed as outpatients had a standard treatment protocol. The SpO2Δ and SpO2e were associated with hospitalisation (p<0.005) while SpO2r was no different between non-hospitalised and hospitalised patients (90.7%±2.7% vs 90.8%±2.3%, p=0.87). By contrast, exertional SpO2e was significantly different between non-hospitalised and hospitalised (87.3%±2.6% vs 84.4%±3.4%, p=0.0005). The mean lung injury score was 11.0±3.5 (18-point scale) and did not discriminate against those who would need hospitalisation. Lower lung fields were significantly more involved than the upper (p<0.0001). All patients had elevated biomarkers of inflammation, C reactive protein (CRP) median 82.5 IQR (43-128.6) mg/L and evidence of elevated liver enzymes. A logistic regression model was constructed including SpO2Δ, CRP and alanine aminotransferase to predict hospitalisation. Only SpO2Δ was significant, p=0.012, 95% CI (1.128 to 2.704) and correctly classified 85.71% of patients who could remain at home or would need to receive treatment in the hospital. CONCLUSION: An office-based, 20 m walk test can help diverge patients with severe COVID-19 who need escalated care. Further, an aggressive standardised treatment protocol can be used to successfully manage patients outside of hospitals despite having severe COVID-19.

Highly effective mlo-based powdery mildew resistance in hexaploid wheat without pleiotropic effects.

Application of the mlo-based resistance in barley against powdery mildew attacks is a major success in crop breeding, since it confers durable disease resistance. Resistance caused by mutations in the Mlo gene seems to be ubiquitous across a range of species. This work addresses the introduction of mlo-based resistance into hexaploid wheat, which is complicated by the occurrence of three homoeologous genes: Mlo-A1, Mlo-B1 and Mlo-D1. EMS-generated mutant plants were screened for mutations in the three homoeologues. We selected and combined 6, 8, and 4 mutations, respectively, to obtain triple homozygous mlo mutant lines. Twenty-four mutant lines showed highly effective resistance towards attack by the powdery mildew pathogen under field conditions. All 18 mutations appeared to contribute to resistance; however, they had different effects on the occurrence of symptoms such as chlorotic and necrotic spots, which are pleiotropic to the mlo-based powdery mildew resistance. We conclude that to obtain highly effective powdery mildew resistance in wheat and to avoid detrimental pleiotropic effects, all three Mlo homoeologues should be mutated; however, at least one of the mutations should be of the weaker type in order to alleviate strong pleiotropic effects from the other mutations.

An Expectation-Maximization Algorithm for Combining a Sample of Partially Overlapping Covariance Matrices.

The generation of unprecedented amounts of data brings new challenges in data management, but also an opportunity to accelerate the identification of processes of multiple science disciplines. One of these challenges is the harmonization of high-dimensional unbalanced and heterogeneous data. In this manuscript, we propose a statistical approach to combine incomplete and partially-overlapping pieces of covariance matrices that come from independent experiments. We assume that the data are a random sample of partial covariance matrices sampled from Wishart distributions and we derive an expectation-maximization algorithm for parameter estimation. We demonstrate the properties of our method by (i) using simulation studies and (ii) using empirical datasets. In general, being able to make inferences about the covariance of variables not observed in the same experiment is a valuable tool for data analysis since covariance estimation is an important step in many statistical applications, such as multivariate analysis, principal component analysis, factor analysis, and structural equation modeling.