Navigating the decision-making landscape of AI in risk finance: Techno-accountability unveiled.

The integration of artificial intelligence (AI) systems has ushered in a profound transformation. This conversion is marked by revolutionary extrapolative capabilities, a shift toward data-centric decision-making processes, and the enhancement of tools for managing risks. However, the adoption of these AI innovations has sparked controversy due to their unpredictable and opaque disposition. This study employs the transactional stress model to empirically investigate how six technological stressors (techno-stressors) impact both techno-eustress (positive stress) and techno-distress (negative stress) experienced by finance professionals and experts. To collect data for this research, an e-survey was distributed to a diverse group of 251 participants from various sources. The findings, particularly the identification and development of techno-accountability as a significant factor, contribute to the risk analysis domain by improving the failure mode and effect analysis framework to better fit the rapidly evolving landscape of AI-driven innovations.

K27M in canonical and noncanonical H3 variants occurs in distinct oligodendroglial cell lineages in brain midline gliomas.

Canonical (H3.1/H3.2) and noncanonical (H3.3) histone 3 K27M-mutant gliomas have unique spatiotemporal distributions, partner alterations and molecular profiles. The contribution of the cell of origin to these differences has been challenging to uncouple from the oncogenic reprogramming induced by the mutation. Here, we perform an integrated analysis of 116 tumors, including single-cell transcriptome and chromatin accessibility, 3D chromatin architecture and epigenomic profiles, and show that K27M-mutant gliomas faithfully maintain chromatin configuration at developmental genes consistent with anatomically distinct oligodendrocyte precursor cells (OPCs). H3.3K27M thalamic gliomas map to prosomere 2-derived lineages. In turn, H3.1K27M ACVR1-mutant pontine gliomas uniformly mirror early ventral NKX6-1+/SHH-dependent brainstem OPCs, whereas H3.3K27M gliomas frequently resemble dorsal PAX3+/BMP-dependent progenitors. Our data suggest a context-specific vulnerability in H3.1K27M-mutant SHH-dependent ventral OPCs, which rely on acquisition of ACVR1 mutations to drive aberrant BMP signaling required for oncogenesis. The unifying action of K27M mutations is to restrict H3K27me3 at PRC2 landing sites, whereas other epigenetic changes are mainly contingent on the cell of origin chromatin state and cycling rate.

Detection and genomic analysis of BRAF fusions in Juvenile Pilocytic Astrocytoma through the combination and integration of multi-omic data.

BACKGROUND: Juvenile Pilocytic Astrocytomas (JPAs) are one of the most common pediatric brain tumors, and they are driven by aberrant activation of the mitogen-activated protein kinase (MAPK) signaling pathway. RAF-fusions are the most common genetic alterations identified in JPAs, with the prototypical KIAA1549-BRAF fusion leading to loss of BRAF's auto-inhibitory domain and subsequent constitutive kinase activation. JPAs are highly vascular and show pervasive immune infiltration, which can lead to low tumor cell purity in clinical samples. This can result in gene fusions that are difficult to detect with conventional omics approaches including RNA-Seq. METHODS: To this effect, we applied RNA-Seq as well as linked-read whole-genome sequencing and in situ Hi-C as new approaches to detect and characterize low-frequency gene fusions at the genomic, transcriptomic and spatial level. RESULTS: Integration of these datasets allowed the identification and detailed characterization of two novel BRAF fusion partners, PTPRZ1 and TOP2B, in addition to the canonical fusion with partner KIAA1549. Additionally, our Hi-C datasets enabled investigations of 3D genome architecture in JPAs which showed a high level of correlation in 3D compartment annotations between JPAs compared to other pediatric tumors, and high similarity to normal adult astrocytes. We detected interactions between BRAF and its fusion partners exclusively in tumor samples containing BRAF fusions. CONCLUSIONS: We demonstrate the power of integrating multi-omic datasets to identify low frequency fusions and characterize the JPA genome at high resolution. We suggest that linked-reads and Hi-C could be used in clinic for the detection and characterization of JPAs.