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Low- and middle-income countries bear the most significant burden of foodborne diseases, impacting their food and nutrition security, trade, and ultimately economic growth. Recent advances in digitization and artificial intelligence provide new opportunities to transform food safety systems, addressing inefficiencies through better oversight and improved decision-making. This article synthesizes current practices and developments related to food safety and digital innovation, and proposes a Digital Food Safety Transformation Framework.

The rapid advancement of Artificial Intelligence (AI) in the business sector has led to a new era of digital transformation. AI is transforming processes, functions, and practices throughout organizations creating system and process efficiencies, performing advanced data analysis, and contributing to the value creation process of the organization. However, the implementation and adoption of AI systems in the organization is not without challenges, ranging from technical issues to human-related barriers, leading to failed AI transformation efforts or lower than expected gains. We argue that while engineers and data scientists excel in handling AI and data-related tasks, they often lack insights into the nuanced human aspects critical for organizational AI success. Thus, Human Resource Management (HRM) emerges as a crucial facilitator, ensuring AI implementation and adoption are aligned with human values and organizational goals. This paper explores the critical role of HRM in harmonizing AI's technological capabilities with human-centric needs within organizations while achieving business objectives. Our positioning paper delves into HRM's multifaceted potential to contribute toward AI organizational success, including enabling digital transformation, humanizing AI usage decisions, providing strategic foresight regarding AI, and facilitating AI adoption by addressing concerns related to fears, ethics, and employee well-being. It reviews key considerations and best practices for operationalizing human-centric AI through culture, leadership, knowledge, policies, and tools. By focusing on what HRM can realistically achieve today, we emphasize its role in reshaping roles, advancing skill sets, and curating workplace dynamics to accommodate human-centric AI implementation. This repositioning involves an active HRM role in ensuring that the aspirations, rights, and needs of individuals are integral to the economic, social, and environmental policies within the organization. This study not only fills a critical gap in existing research but also provides a roadmap for organizations seeking to improve AI implementation and adoption and humanizing their digital transformation journey.

The existing multimodal image classification methods often suffer from several key limitations: difficulty in effectively balancing local detail and global topological relationships in hyperspectral image (HSI) feature extraction; insufficient multi-scale characterization of terrain features from light detection and ranging (LiDAR) elevation data; and neglect of deep inter-modal interactions in traditional fusion methods, often accompanied by high computational complexity. To address these issues, this paper proposes a comprehensive deep learning framework combining convolutional neural network (CNN), a graph convolutional network (GCN), and wavelet transform for the joint classification of HSI and LiDAR data, including several novel components: a Spectral Graph Mixer Block (SGMB), where a CNN branch captures fine-grained spectral–spatial features by multi-scale convolutions, while a parallel GCN branch models long-range contextual features through an enhanced gated graph network. This dual-path design enables simultaneous extraction of local detail and global topological features from HSI data; a Spatial Coordinate Block (SCB) to enhance spatial awareness and improve the perception of object contours and distribution patterns; a Multi-Scale Elevation Feature Extraction Block (MSFE) for capturing terrain representations across varying scales; and a Bidirectional Frequency Attention Encoder (BiFAE) to enable efficient and deep interaction between multimodal features. These modules are intricately designed to work in concert, forming a cohesive end-to-end framework, which not only achieves a more effective balance between local details and global contexts but also enables deep yet computationally efficient interaction across features, significantly strengthening the discriminability and robustness of the learned representation. To evaluate the proposed method, we conducted experiments on three multimodal remote sensing datasets: Houston2013, Augsburg, and Trento. Quantitative results demonstrate that our framework outperforms state-of-the-art methods, achieving OA values of 98.93%, 88.05%, and 99.59% on the respective datasets.

In the human neocortex, single excitatory pyramidal cells can elicit very large glutamatergic EPSPs (VLEs) in inhibitory GABAergic interneurons capable of triggering their firing with short (3-5 ms) delay. Similar strong excitatory connections between two individual neurons have not been found in nonhuman cortices, suggesting that these synapses are specific to human interneurons. The VLEs are crucial for generating neocortical complex events, observed as single pyramidal cell spike-evoked discharge of cell assemblies in the frontal and temporal cortices. However, long-term plasticity of the VLE connections and how the plasticity modulates neocortical complex events has not been studied. Using triple and dual whole-cell recordings from synaptically connected human neocortical layers 2-3 neurons, we show that VLEs in fast-spiking GABAergic interneurons exhibit robust activity-induced long-term depression (LTD). The LTD by single pyramidal cell 40 Hz spike bursts is specific to connections with VLEs, requires group I metabotropic glutamate receptors, and has a presynaptic mechanism. The LTD of VLE connections alters suprathreshold activation of interneurons in the complex events suppressing the discharge of fast-spiking GABAergic cells. The VLEs triggering the complex events may contribute to cognitive processes in the human neocortex, and their long-term plasticity can alter the discharging cortical cell assemblies by learning.

The multimodal fusion of hyperspectral images (HSI) and LiDAR data for land cover classification encounters difficulties in modeling heterogeneous data characteristics and cross-modal dependencies, leading to the loss of complementary information due to concatenation, the inadequacy of fixed fusion weights to adapt to spatially varying reliability, and the assumptions of linear separability for nonlinearly coupled patterns. We propose QIE-Mamba, integrating selective state-space models with quantum-inspired processing to enhance multimodal representation learning. The framework employs ConvNeXt encoders for hierarchical feature extraction, quantum superposition layers for complex-valued multimodal encoding with learned amplitude–phase relationships, unitary entanglement networks via skew-symmetric matrix parameterization (validated through Cayley transform and matrix exponential methods), quantum-enhanced Mamba blocks with adaptive decoherence, and confidence-weighted measurement for classification. Systematic three-phase sequential validation on Houston2013, Muufl, and Augsburg datasets achieves overall accuracies of 99.62%, 96.31%, and 96.30%. Theoretical validation confirms 35.87% mutual information improvement over classical fusion (6.9966 vs. 5.1493 bits), with ablation studies demonstrating quantum superposition contributes 82% of total performance gains. Phase information accounts for 99.6% of quantum state entropy, while gradient convergence analysis confirms training stability (zero mean/std gradient norms). The optimization framework reduces hyperparameter search complexity by 99.6% while maintaining state-of-the-art performance. These results establish quantum-inspired state-space models as effective architectures for multimodal remote sensing fusion, providing reproducible methodology for hyperspectral–LiDAR classification with linear computational complexity.

The two sialyltransferases in the ST6GALNAC subfamily (EC 2.4.99.-; CAZy family GT29), ST6GalNAc5 and ST6GalNAc6, catalyze the formation of the linkage from the sialic acid moiety to the C6 position of N -acetylgalactosamine (GalNAc) as well as to N -acetylglucosamine (GlcNAc), and are known as α-2,6-sialyltransferases. This activity is interesting for the synthesis of the disialylated oligosaccharide disialyllacto- N -tetraose (DSLNT). Human sialyltransferases ST6GalNAc5 and ST6GalNAc6 produced in HEK293 cells are commercially available at a smaller scale. In this study, we demonstrated that ST6GalNAc5 and ST6GalNAc6 can be functionally expressed in Pichia pastoris X-33. The level of ST6GalNAc5 and ST6GalNAc6 expression and activity largely depended on the type of construct, as well as on expression conditions, namely temperature, methanol feeding regime, and supplements. Insertion of a (GGGS)₂ linker peptide between the gene and the α secretion factor improved the secretion of active enzyme in P. pastoris X-33. The use of media supplemented with MgCl 2 and Casamino acids led to increased cell growth and, importantly, enhanced ST6GalNAc5 and ST6GalNAc6 production. Under optimized conditions, the P. pastoris X-33 strain could secrete up to 10 mg of active sialyltransferase protein per liter of culture. Compared to their wild-type counterparts, mutants of ST6GalNAc5 and ST6GalNAc6 devoid of N -glycosylation sites exhibited reduced enzymatic activity and stability. Apart from contributing to successful P. pastoris expression, our findings also contribute to a deeper understanding of the role of N -glycosylation in the activity and stability of sialyltransferases. Key points •  Expression of functional human ST6GalNAc5 and ST6GalNAc6 in Pichia pastoris •  Mutants devoid of N-glycosylations lack activity •  Media supplementation with MgCl2 and Casamino acids improves expression

The rise of neurotechnologies, especially in combination with artificial intelligence (AI)-based methods for brain data analytics, has given rise to concerns around the protection of mental privacy, mental integrity and cognitive liberty – often framed as “neurorights” in ethical, legal, and policy discussions. Several states are now looking at including neurorights into their constitutional legal frameworks, and international institutions and organizations, such as UNESCO and the Council of Europe, are taking an active interest in developing international policy and governance guidelines on this issue. However, in many discussions of neurorights the philosophical assumptions, ethical frames of reference and legal interpretation are either not made explicit or conflict with each other. The aim of this multidisciplinary work is to provide conceptual, ethical, and legal foundations that allow for facilitating a common minimalist conceptual understanding of mental privacy, mental integrity, and cognitive liberty to facilitate scholarly, legal, and policy discussions.

The increasing availability of brain data within and outside the biomedical field, combined with the application of artificial intelligence (AI) to brain data analysis, poses a challenge for ethics and governance. We identify distinctive ethical implications of brain data acquisition and processing, and outline a multi-level governance framework. This framework is aimed at maximizing the benefits of facilitated brain data collection and further processing for science and medicine whilst minimizing risks and preventing harmful use. The framework consists of four primary areas of regulatory intervention: binding regulation, ethics and soft law, responsible innovation, and human rights.

Latecomer political science communities have faced multiple challenges in the past decades, including the very establishment of their professional identities. Based on the case study of Hungary, this article argues that publication performance is a substantial component of the identity of the political science profession. Hungary is a notable example among Central and East European (CEE) political science academia in the sense that both the initial take-off of the profession and then its increasing challenges are typical to the CEE region. In an inclusive approach, which encompasses all authors published in the field between 1990 and 2018, as well as their publication record, the analysis demonstrates that political science has undergone major expansion, quality growth and internationalisation but these performance qualities are unevenly spread. These reflect important aspects of the profession’s identity. This agency and performance-based approach to identity formation might well be used to build up identity features elsewhere and also in a comparative manner.

Background Rare Disease research has seen tremendous advancements over the last decades, with the development of new technologies, various global collaborative efforts and improved data sharing. To maximize the impact of and to further build on these developments, there is a need for model consent clauses for rare diseases research, in order to improve data interoperability, to meet the informational needs of participants, and to ensure proper ethical and legal use of data sources and participants’ overall protection. Methods A global Task Force was set up to develop model consent clauses specific to rare diseases research, that are comprehensive, harmonized, readily accessible, and internationally applicable, facilitating the recruitment and consent of rare disease research participants around the world. Existing consent forms and notices of consent were analyzed and classified under different consent themes, which were used as background to develop the model consent clauses. Results The IRDiRC-GA4GH MCC Task Force met in September 2018, to discuss and design model consent clauses. Based on analyzed consent forms, they listed generic core elements and designed the following rare disease research specific core elements; Rare Disease Research Introductory Clause, Familial Participation, Audio/Visual Imaging, Collecting, storing, sharing of rare disease data, Recontact for matching, Data Linkage, Return of Results to Family Members, Incapacity/Death, and Benefits. Conclusion The model consent clauses presented in this article have been drafted to highlight consent elements that bear in mind the trends in rare disease research, while providing a tool to help foster harmonization and collaborative efforts.