In the era of rapidly expanding human genomics in research and healthcare, efficient data reuse is essential to maximise benefits for society. In response, Federated EGA was launched in 2022, and as of 2024, the FEGA Network is composed of seven national nodes. Here we describe the complexities, challenges, and achievements of FEGA, unravelling the dynamic interplay between regulatory frameworks, technical challenges, and the shared vision of advancing genomic research.

Voltammetry is widely used to detect and quantify oxidizable or reducible species in complex environments. The neurotransmitter serotonin epitomizes an analyte that is challenging to detect in situ due to low concentrations and co-existing similarly structured analytes and interferents. We developed rapid-pulse voltammetry for brain neurotransmitter monitoring due to the high information content elicited from voltage pulses. Generally, the design of voltammetry waveforms remains challenging due to prohibitively large combinatorial search spaces and a lack of design principles. Here, we illustrate how Bayesian optimization can be used to hone searches for optimized rapid pulse waveforms. Our machine-learning-guided workflow (SeroOpt) outperformed random and human-guided waveform designs and is tunable a priori to enable selective analyte detection. We interpreted the black box optimizer and found that the logic of machine-learning-guided waveform design reflected domain knowledge. Our approach is straightforward and generalizable for all single and multi-analyte problems requiring optimized electrochemical waveform solutions. Overall, SeroOpt enables data-driven exploration of the waveform design space and a new paradigm in electroanalytical method development.

Abstract BACKGROUND Schizophrenia (SZ), a chronic and widespread brain disorder, presents with complex etiology and pathogenesis that remain inadequately understood. Despite the absence of a universally recognized endophenotype, peripheral blood mononuclear cells (PBMCs) serve as a robust model for investigating intracellular alterations linked to SZ. AIM To preliminarily investigate potential pathogenic mechanisms and identify novel biomarkers for SZ. METHODS PBMCs from SZ patients were subjected to integrative transcriptomic and proteomic analyses to uncover differentially expressed genes (DEGs) and differentially expressed proteins while mapping putative disease-associated signaling pathways. Key findings were validated using western blot (WB) and real-time fluorescence quantitative PCR (RT-qPCR). RNAi-lentivirus was employed to transfect rat hippocampal CA1 neurons in vitro, with subsequent verification of target gene expression via RT-qPCR. The levels of neuronal conduction proteins, including caMKII, CREB, and BDNF, were assessed through WB. Apoptosis was quantified by flow cytometry, while cell proliferation and viability were evaluated using the Cell Counting Kit-8 assay. RESULTS The integration of transcriptomic and proteomic analyses identified 6079 co-expressed genes, among which 25 DEGs were significantly altered between the SZ group and healthy controls. Notably, HP, LTF, and SERPING1 exhibited marked upregulation. KEGG pathway enrichment analysis implicated neuroactive ligand-receptor interaction pathways in disease pathogenesis. Clinical sample validation demonstrated elevated protein and mRNA levels of HP, LTF, and SERPING1 in the SZ group compared to controls. WB analysis of all clinical samples further corroborated the significant upregulation of SERPING1. In hippocampal CA1 neurons transfected with lentivirus, reduced SERPING1 expression was accompanied by increased levels of CaMKII, CREB, and BDNF, enhanced cell viability, and reduced apoptosis. CONCLUSION SERPING1 may suppress neural cell proliferation in SZ patients via modulation of the CaMKII-CREB-BDNF signaling pathway.

We present a method to significantly reduce the computational scaling of the post-Hartree- Fock (post-HF) component in Density Matrix Embedding Theory (DMET) calculations by exploiting the exponential decay properties of both the mean-field density matrix and the or- bital transformation matrix. Additionally, we extend this reduced-scaling approach to calculate the coupled-cluster CCSD(T) density matrix, facilitating DMET-CCSD(T) energy evaluations through a back-transformed energy formula. The accuracy of relative electronic energies is benchmarked using the all-electron solver, Lowdin-partitioned fragments, and fragments de- rived from Intrinsic Atomic Orbital and Projected Atomic Orbital (IAO+PAO) partitioning schemes. Our results demonstrate that, with appropriate utilization of the decay of one particle density matrix (1-PDM), the evaluation of the post-HF energy can achieve asymptotically linear scaling. Furthermore, for relative electronic energies calculations, Lowdin partitioning 1 performs well in weakly interacting systems, such as water clusters. This study underscores the potential of reduced-scaling techniques to improve computational efficiency and the efficacy of CCSD(T) solvers in delivering accurate thermochemical predictions in weakly interacting systems.

Abstract Background Preeclampsia (PE) is currently one of the major causes threatening the health and leading to death of pregnant women and fetuses. The onset of PE is attributed to cellular biological dysfunction resulting from the disruption of the molecular regulatory network in the trophoblast cells. We discovered that FOXO1 was downregulated in the placenta of preeclampsia. Methods In order to delve deeper into the involvement of FOXO1 in the development of preeclampsia, trophoblast cell lines were generated with manipulated levels of FOXO1, either through overexpression or knockdown, to elucidate its biological function and underlying mechanisms. Results The expression level of FOXO1 is positively correlated with the invasive, migratory, and proliferative abilities of trophoblast cells. Transcriptome sequencing analysis revealed DUSP9 as a potential target gene of FOXO1. The suppression of DUSP9 expression has been shown to markedly diminish the invasive, migratory, and proliferative abilities of trophoblast cells. Silencing DUSP9 in trophoblast cells that exhibit elevated levels of FOXO1 can attenuate their physiological functions. We found that overexpression/inhibition of FOXO1 can correspondingly suppress/activate the p38/JNK signaling pathway. Notably, the inhibition of DUSP9 in the context of FOXO1 overexpression can activate the p38/JNK signaling pathway. Conclusions FOXO1 modulates the biofunctions of trophoblast cells in preeclampsia via the DUSP9/p38/JNK signaling pathway.

The digitization of quality assurance systems in higher education is a critical innovation that enhances efficiency, transparency, and accuracy in ensuring educational quality. This study investigates the implementation and impact of a digitized quality assurance system at the Professional Education and Certification Institute (LPSP) of the State University of Surabaya. The research utilizes a mixed-method approach, combining surveys, semi-structured interviews, document analysis, and observations to gather comprehensive data from 120 respondents, including leaders, staff, faculty, students, and university administrators. Findings reveal that digitization significantly improved efficiency by streamlining workflows and reducing task completion times by up to 70%. The accuracy of data handling increased, with a 30% reduction in errors, while the transparency of processes was enhanced through real-time analytics and shared access to quality assurance metrics. Despite these advancements, challenges such as initial user adaptation, infrastructure limitations, and data privacy concerns were identified. Additionally, some faculty members expressed concerns about reduced personalization in decision-making due to increased reliance on automation. This study highlights the transformative potential of digital quality assurance systems in addressing inefficiencies and enhancing accountability in higher education. Recommendations for improvement include enhanced training programs, infrastructure upgrades, and the development of mobile-friendly interfaces to increase accessibility. The findings contribute to the growing body of knowledge on digital transformation in education and provide actionable insights for institutions seeking to implement similar systems. By balancing automation with human oversight, higher education institutions can ensure sustainable improvements in quality assurance processes.

This study explores the use of lateral flow assays (LFAs), recognized for their simplicity and ease-of-use, as a tool for characterizing nanoparticles functionalized with various biomolecules (e.g., proteins, antibodies and nucleic acids). A half-strip model system was developed using ovalbumin (OVA) conjugated to gold nanoparticles (AuNPs). The characterization results obtained with LFAs were compared to those from traditional methods such as infrared spectroscopy and fluorescence labelling. The advantages of LFAs in characterizing such conjugated nanosystems were clearly demonstrated. The use of half-strip assays could not only confirm the presence of OVA on AuNPs but also enable the quantification of OVA bound per nanoparticle, offering a rapid and quantitative characterization method. Additionally, the assay showcased its versatility, as it was successfully applied to optimize the covalent coupling conditions of OVA on AuNPs, as well as to differentiate between covalently bound and adsorbed proteins. Furthermore, LFAs were employed to detect antibodies on functionalized nanoparticles, optimize their coupling to a newly developed organic coating, and confirm both the grafting of nucleic acids onto the surface and their pairing with complementary strands. These findings underscore the remarkable adaptability of LFAs for characterizing diverse nanoconjugates. Overall, LFAs stand out as a versatile and accessible tool for characterizing complex bioconjugated nanosystems, making them highly suitable for rapid Quality Control (QC) analysis and bioconjugation optimization.

Abstract Smallholder Eucalyptus plantations (EP) in Madagascar Central Highlands (MCH) address substantial fuelwood demand and reduce pressure on natural forests. However, their sustainability is challenged by low soil fertility and inadequate management. While fertilization increases tree growth, high mineral fertilizer costs limit its use by smallholder farmers. Both biomass estimating equation and impact of fertilization on smallholder EP in MCH remain poorly documented. This study aimed to evaluate the impact of low starter mineral fertilization (15 kg.ha-1 N, 12 kg.ha-1 P, and 17 kg.ha-1 K) and weed competition on smallholder Eucalyptus robusta growth and aboveground biomass (AGB) while providing allometric equations for AGB estimation. Dendrometric data were collected from six stands aged 2 to 6 years, with fertilized and non-fertilized parts. AGB of 16 trees per stand (8 fertilized, 8 non-fertilized) were destructively measured. AGB of trees were estimated by regression based on tree circumference, height and their combinations. The results indicated circumference as the best single variable predictor (R2 > 0.90) for all tree compartments and stands Fertilization significantly improved global tree survival by 7% and increased height by 3.1 m and circumference by 8.3 cm in 6-year-old stands AGB per hectare globally doubled with fertilization, reaching 55.3 Mg.ha-1at 6 yo compared to 29.2 Mg.ha-1 in non-fertilized plots. Weed cover and biomass had significant negative linear relationships with AGB and survival rate. This study provides robust allometric equations for biomass estimation and highlights that even low fertilizer application combined with effective weed control can significantly enhance AGB production in smallholder EP.