Early Risk Indicators for DSM-IV Diagnoses in Adolescents and Young Adults with Intellectual Disabilities

Objective: To identify risk indicators at ages 6–18 years that are associated with DSM-IV diagnoses in adolescents and young adults with intellectual disabilities five years later. To assess the potential health gain and efficiency of preventive interventions targeting these risk indicators. Method: Parents reported on potential child, parental, and environmental risk indicators. Five years later, parents were interviewed using a standardised psychiatric interview schedule (DISC-IV) to assess DSM-IV diagnoses in children with ID (N = 614) at the age of 11 to 24 years. Logistic regression and linear probability models were used to test the contribution of risk indicators to the prediction of DSM-IV diagnoses. Results: Deviant levels of internalising and externalising problems, inadequate adaptive behaviour, and parental psychopathology predicted psychiatric disorder. Children/adolescents exposed to multiple risk indicators were at greater risk of developing DSM-IV disorders. Conclusions: Strategies aiming for the risk reduction of psychiatric disorders in children/adolescents with ID should focus on intervening at an early age, improving psychopathology and adaptive behaviour skills of the children/adolescents, and supporting their parents.

Shifting Prospects: Views and Strategies for Transforming Livestock and Meat Value Chains in a Dynamic Location

Rural areas characterized by resource-dependent industries often experience growth but also lock-in and transformation pressures. We ask what strategies industries and businesses pursue that successfully exploit the transformative potential of such a location and what prevents other industries and businesses from doing the same. Based on interviews with stakeholders and experts from the livestock and meat sector in a highly specialized location, we explore the will, resources, and capabilities of industries and actors to transform their businesses and entire value chains in ways that can stabilize the local growth regime. The analysis is based on a conceptual framework derived from resource-based and dynamic capability theories at the micro level and the concept of Strategic Action Fields (SAFs) at the meso level. The results suggest that incumbents from the old industrial core tend to counteract the transformation of the SAF with conservative strategies. Challengers from former support activities, in contrast, want to move away from cost competition towards new markets. Their product variation and horizontal diversification can exploit favorable cluster characteristics to develop future-proof capabilities. This should be encouraged, along with new entrepreneurial activity, even if the region is then no longer hosting the core industries of the transformed field. 

12th International Workshop on the CCN Family of Genes

Cellular Communication Network factors 1-6 (CCNs) are matricellular proteins consisting of an N-terminal secretory peptide and four multifunctional structural domains. The CCN1-6 members belonging to this family have a complex network of interacting ligands that can affect diverse signaling pathways through a multitude of mechanisms. Specifically, these proteins play crucial roles in cell proliferation, differentiation, angiogenesis, apoptosis, chondrogenesis, wound repair, and extracellular matrix (ECM) formation/remodeling. This short communication provides a brief summary of the 12th International Workshop on the CCN Family of Genes held at the Scandic Holmenkollen Park Hotel in Oslo, Norway from 20–23 June 2024.

Prospective Approaches for Ecosystem Sustainability Including Climate Mitigation

A summary, based upon foresight, futures, ideation and frontier technology studies of prospective approaches to foster ecosystem sustainability including climate mitigation at the technology and societal levels which are at scale and profitable. Approaches summarized include halophytes/salt plants grown on deserts/wastelands using saline/seawater, to address land, water, food, energy and climate, frontier energetics, nascent climate mitigation concepts, cellular agriculture, materials optimization, the virtual age, efficiency and redesigning the ecosystem for the Anthropocene. Solution/mitigation approaches are targeted at deforestation, desertification, pollution writ large (land, sea, air, space), and extensive urbanization along with soil salination, ocean acidification, mining, and water scarcity.

Solute Carrier Family 26 Member 4 (SLC26A4), A Potential Therapeutic Target for Asthma

Asthma is a prevalent respiratory condition with multifaceted pathomechanisms, presenting challenges for therapeutic development. The SLC (Solute Carrier) gene family, encompassing diverse membrane transport proteins, plays pivotal roles in various human diseases by facilitating solute movement across biological membranes. These solutes include ions, sugars, amino acids, neurotransmitters, and drugs. Mutations in these ion channels have been associated with numerous disorders, underscoring the significance of SLC gene families in physiological processes. Among these, the SLC26A4 gene encodes pendrin, an anion exchange protein involved in transmembrane transport of chloride, iodide, and bicarbonate. Mutations in SLC26A4 are associated with Pendred syndrome. Elevated SLC26A4 expression has been linked to airway inflammation, hyperreactivity, and mucus production in asthma. Here, we review novel insights from SLC gene family members into the mechanisms of substrate transport and disease associations, with specific emphasis on SLC26A4. We explore triggers inducing SLC26A4 expression and its contributions to the pathogenesis of pulmonary diseases, particularly asthma. We summarize the inhibitors of SLC26A4 that have shown promise in the treatment of different phenotypes of diseases. While SLC26A4 inhibitors present potential treatments for asthma, further research is imperative to delineate their precise role in asthma pathogenesis and develop efficacious therapeutic strategies targeting this protein.

An Integer Programming Approach to Multi-Trip Routing of Delivery Drones at Load-Dependent Flight Speed

In recent years, there has been a growing interest in utilizing drones for parcel delivery among companies, aiming to address logistical challenges. However, effective optimization of delivery routes is essential. A theoretical framework termed the Flight Speed-aware Vehicle Routing Problem (FSVRP) has emerged to address the variability in drone flight speed based on payload weight. Several approximate methods have been proposed to solve the FSVRP. Our research endeavors to optimize parcel delivery efficiency and reduce delivery times by introducing a novel delivery problem. This problem accounts for multiple deliveries while considering the variability in flight speed due to diverse payloads. Through experimentation, we evaluate the efficacy of our proposed method compared to existing approaches. Specifically, we assess total flight distance and flight time. Our findings indicate that even in cases where the payload exceeds maximum capacity, all parcels can be delivered through multiple trips. Furthermore, employing a multi-trip FSVRP approach results in an average reduction of 10% in total flight time, even when payload capacities are not exceeded.

Sulforaphane’s Nuclear Factor Erythroid 2-Related Factor 2 (Nrf2)-Dependent and -Independent Mechanism of Anti-SARS-CoV-2 Activity

It is well established that Nrf2 plays a crucial role in anti-oxidant and anti-inflammatory functions. However, its antiviral capabilities remain less explored. Despite this, several Nrf2 activators have demonstrated anti-SARS-CoV-2 properties, though the mechanisms behind these effects are not fully understood. In this study, using two mouse models of SARS-CoV-2 infection, we observed that the absence of Nrf2 significantly increased viral load and altered inflammatory responses. Additionally, we evaluated five Nrf2 modulators. Notably, epigallocatechin gallate (EGCG), sulforaphane (SFN), and dimethyl fumarate (DMF) exhibited significant antiviral effects, with SFN being the most effective. SFN did not impact viral entry but appeared to inhibit the main protease (M^Pro) of SARS-CoV-2, encoded by the Nsp5 gene, as indicated by two protease inhibition assays. Moreover, using two Nrf2 knockout cell lines, we confirmed that SFN's antiviral activity occurs independently of Nrf2 activation in vitro. Paradoxically, in vivo tests using the MA30 model showed that SFN's antiviral function was completely lost in Nrf2 knockout mice. Thus, although SFN and potentially other Nrf2 modulators can inhibit SARS-CoV-2 independently of Nrf2 activation in cell models, their Nrf2-dependent activities might be crucial for antiviral defense under physiological conditions. 

High Entropy Oxides: Next-Generation Air Electrodes for Reversible Protonic Solid Oxide Cells

Reversible protonic solid oxide cell (P-SOC) operating at intermediate-temperature exhibits excellent potential as a power generation and green hydrogen production device in fuel cell and electrolysis cell modes because of the high conversion efficiency. However, the lack of efficient air electrodes is the main challenge to obtain P-SOC with remarkable performance. Typically, air electrodes should possess high proton, oxygen ion and electron conductivity, outstanding catalytic ability for oxygen reduction reaction and H₂O splitting, and also long-term durability. Recently, high entropy oxides (HEO) have become popular due to their various potential applications in terms of outstanding properties, including catalysis ability, conductivity, thermal stability, etc. HEO air electrodes have been confirmed to show good electrochemical performance in P-SOC, but the complex compositions and structure make it difficult to study HEO by traditional experimental methods. Machine learning (ML) has been regarded as a powerful tool in materials research and can solve the drawbacks in the discovery of HEO in a traditional way. In this perspective, we not only discuss the current utilization of HEO in P-SOC but also provide a possible process to use ML to guide the development of HEO.

Green Chemical Technology: A New Journal for Green Engineering and Green Technology

Cytosine Deaminase-Assisted Mutator for Genome Evolution in Cupriavidus necator

Cupriavidus necator H16 has been intensively explored for its potential as a versatile microbial cell factory, especially for its CO₂ fixation capability over the past few decades. However, rational metabolic engineering remains challenging in the construction of microbial cell factories with complex phenotypes due to the limited understanding of its metabolic regulatory network. To overcome this obstacle, laboratory adaptive evolution emerges as an alternative. In the present study, CAM (cytosine deaminase-assisted mutator) was established for the genome evolution of C. necator, addressing the issue of low mutation rates. By fusing cytosine deaminase with single-stranded binding proteins, CAM introduced genome-wide C-to-T mutations during DNA replication. This innovative approach could boost mutation rates, thereby expediting laboratory adaptive evolution. The applications of CAM were demonstrated in improving cell factory robustness and substrate utilization, with H₂O₂ resistance and ethylene glycol utilization as illustrative case studies. This genetic tool not only facilitates the development of efficient cell factories but also opens avenues for exploring the intricate phenotype-genotype relationships in C. necator.