The scarcely investigated myxobacterium Corallococcus coralloides holds a large genome containing many uncharacterized biosynthetic gene clusters (BGCs) that potentially encode the synthesis of entirely new natural products. Despite its promising genomic potential, suitable cultivation conditions have not yet been found to activate the synthesis of new secondary metabolites (SMs). Finding the right cultivation conditions to activate BGCs in the genome remained a major bottleneck and its full biosynthetic potential was so far not retrieved. Here, we therefore applied a bivariate OSMAC approach, using a combination of two elicitor changes at once for activation of BGCs and concomitant SM production by C. coralloides. The bivariate OSMAC screening was carried out in 24-well System Duetz-plates, applying univariate and bivariate OSMAC conditions. We combined biotic additives and organic solvents with minimal media and complex growth medium. The success of the method was evaluated by the number of new mass features detected in the respective extracts. We found synergistic effects in bivariate OSMAC designs. The number of new mass features detected in bivariate OSMAC exceeded the sum of new mass features found in the respective univariate OSMAC with only one elicitor. Overall, the bivariate OSMAC screening led to 26 new mass features, which were not detected in the univariate OSMAC design. Hence, the presence of multiple elicitors in the bivariate OSMAC designs successfully activated the biosynthetic potential in C. coralloides. We propose the bivariate OSMAC designs with a complex combination of elicitors as a straightforward strategy to robustly expand the SM space of microorganisms with large genomes.

Childhood glaucoma encompasses congenital and juvenile primary glaucoma, which are heterogeneous, uncommon, and irreversible optic neuropathies leading to visual impairment with poorly understood genetic basis. Our goal was to identify gene variants associated with these glaucoma types by assessing the mutational burden in 76 matrix metalloproteinase-related genes. We studied 101 childhood glaucoma patients with no identified monogenic alterations using next-generation sequencing. Gene expression was assessed through immunohistochemistry. Functional analysis of selected gene variants was conducted in cultured cells and in zebrafish. Patients presented a higher proportion of rare variants in four metalloproteinase-related genes, including CPAMD8 and ADAMTSL4, compared to controls. ADAMTSL4 protein expression was observed in the anterior segment of both the adult human and zebrafish larvae's eye, including tissues associated with glaucoma. In HEK-293T cells, expression of four ADAMTSL4 variants identified in this study showed that two variants (p.Arg774Trp and p.Arg98Trp) accumulated intracellularly, inducing endoplasmic reticulum stress. Additionally, overexpressing these ADAMTSL4 variants in zebrafish embryos confirmed partial loss-of-function effects for p.Ser719Leu and p.Arg1083His. Double heterozygous functional suppression of adamtsl4 and cpamd8 zebrafish orthologs resulted in reduced volume of both the anterior eye chamber and lens within the chamber, supporting a genetic interaction between these genes. Our findings suggest that accumulation of partial functional defects in matrix metalloproteinases-related genes may contribute to increased susceptibility to early-onset glaucoma and provide further evidence supporting the notion of a complex genetic inheritance pattern underlying the disease.

Socially Responsible Investments, also referred to as ethical or sustainable investments, have experienced rapid global growth in recent years. They represent an investment approach that incorporates social, environmental, and ethical considerations into decision-making processes. Consequently, the significance of SRIs has captured the attention of academics, prompting inquiries into the impact of integrating social criteria on portfolio performance. The primary objective of this work was to conduct a comparative study of the performance between socially responsible and non-socially responsible investment funds, using funds domiciled in Portugal and Spain. Various multi-factor models, including the three-factor model of Fama and French (1992), the four-factor model of Carhart (1997), and the five-factor model of Fama and French (2015), were employed to assess performance. The sample comprised 125 investment funds, with 43 identified as socially responsible and 82 as non-socially responsible. The study's findings indicate that there are no significant differences between socially responsible funds and their conventional counterparts. The majority of funds experience performance alterations during periods of crisis compared to crisis-free periods. Additionally, when comparing non-conditional models with conditional models, an improvement in the explanatory power of the latter is observed. This suggests that the inclusion of the dummy variable enhances the quality of fit for the models.

Simultaneous extraction of trypsin inhibitors and soy isoflavones from soybean meal was investigated using the non-destructive phytochemical extraction process, namely aqueous micellar system. The ethoxylated aliphatic alcohols Genapol X-080, Tergitol 15-S-7, and Tergitol 15-S-9, all non-toxic and biodegradable surfactants, were assessed as potential extractants. A Box-Behnken multifactorial design with the application of the Derringer desirability was used to determine the conditions that maximized the trypsin inhibitors and isoflavone extraction while minimizing the protein extraction. The optimum condition of 5% m/m of surfactant in 50 mM aqueous sodium citrate solution pH 4.5, at 45 °C for 45 min, was established for the three surfactants. The novel methodology would allow the extraction of the main soybean antinutritional factors, trypsin inhibitors, and the valuable isoflavones, preserving the nutritional quality of the treated material. This represents a sustainable alternative methodology for industrial purposes due to its low cost, biodegradability, non-toxicity, and easy scaling up.

Recent advances in nanomaterials have been heavily influenced by low-dimensional nanocarbon allotropes. In particular, carbyne has attracted attention for its potential as a true one-dimensional carbon chain with sp1 hybridization. To maximize the capabilities of this material, we employ a focused data-driven inverse design approach based on the carbon nanomaterials genome concept. This involves using deep learning neural network models to identify key descriptors tied to desired properties, enabling property prediction and reverse engineering of nanocarbons. Our iterative approach entails: (i) gathering growth/property data on nanostructures; (ii) identifying informative numerical/categorical predictors; (iii) developing deep learning models mapping descriptors to properties; (iv) refining models with new insights; (v) determining required descriptors/conditions for target properties via inverse mapping; (vi) validating models by synthesizing predicted nanostructures; and (vii) enhancing models with validation data. This allows uncovering hidden growth-property connections, precisely tuning nanocarbons for desired attributes. We introduce new methodologies including exciting synergistic effects, synchronizing atomic vibrations, active screen plasma, energy-driven transformations, surface acoustic micro/nano-manipulation, doping and directed self-assembly to expose relationships and integrate insights into the inverse design flow. This research promises to accelerate discovery of next-generation low-dimensional nanocarbons with exceptional properties and applications.

The swift progress in low-dimensional nanocarbons, specifically in the realm of the authentic one-dimensional carbon chain featuring sp¹ hybridization, has unveiled exciting prospects for integrating them into cutting-edge technologies across diverse industries. To fully harness their potential, precise control over the growth process is necessary to obtain desired nanostructure and functionality. However, optimizing properties through traditional approaches has been challenging due to complex interactions. To address this, we implement a focused data-driven strategy for nanocarbon inverse design by leveraging a state-of-the-art data-driven nanocarbon genome approach (NCGA). By uncovering relationships between growth parameters and resultant traits, this serves as an expedient catalyst in engineering nanostructures with tailored attributes. We introduce an extensive array of technological approaches aimed at precisely controlling the growth process. These methods encompass stimulating and precisely adjusting synergistic effects, coordinating atomic vibrations at a collective level through the implementation of multilayer nano-interfaces, harnessing the potential of active screen plasma surface engineering, utilizing nano-patterning and allotropic phase transformations, incorporating heteroatom doping, and effectively directing self-assembly. These aim to unlock nanomaterials' latent potential and reveal novel neural pathways within the data-driven NCGA, enhancing its predictive capabilities. Specifically, triggering self-organization during growth can potentially unlock previously unexplored neural pathways. The data-driven NCGA offers a paradigm shift, accelerating discovery and design of nanocarbons with optimized, application-specific properties.