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Therapeutic vulnerabilities and pan-cancer landscape of BRAF class III mutations in epithelial solid tumors

Kinase-impaired class III BRAF mutations have recently received attention as a possible prognostic factor and therapeutic target. Class III BRAF variants differ from class I and class II mutations in terms of mechanism of pathway activation and therapeutic vulnerabilities. Genomic landscape analyses of tumors in large real-world cohorts represent a great opportunity to further characterize tumor-related molecular events and treatment vulnerabilities, however, such data is not yet available for tumors with BRAF class III mutations.

Modeling the impact of structure and coverage on the reactivity of realistic heterogeneous catalysts

Adsorbates often cover the surfaces of catalysts densely as they carry out reactions, dynamically altering their structure and reactivity. Understanding adsorbate-induced phenomena and harnessing them in our broader quest for improved catalysts is a substantial challenge that is only beginning to be addressed. Here we chart a path toward a deeper understanding of such phenomena by focusing on emerging in silico modeling methodologies, which will increasingly incorporate machine learning techniques. We first examine how adsorption on catalyst surfaces can lead to local and even global structural changes spanning entire nanoparticles, and how this affects their reactivity. We then evaluate current efforts and the remaining challenges in developing robust and predictive simulations for modeling such behavior. Last, we provide our perspectives in four critical areas—integration of artificial intelligence, building robust catalysis informatics infrastructure, synergism with experimental characterization, and adaptive modeling frameworks—that we believe can help surmount the remaining challenges in rationally designing catalysts in light of these complex phenomena.

Spin-state effect on the efficiency of a post-synthetic modification reaction on a spin crossover complex

The spin state of a metal center significantly influences the catalytic activity of its complex, a phenomenon so crucial that it has led to the dedicated field of spin catalysis. Here we investigate the effect of the spin state of an iron-based metal complex on the organic reactivity of its ligands. Specifically, we examined the post-synthetic modification of the spin crossover (SCO) complex [Fe(NH2trz)3](NO3)2 with p-anisaldehyde. A series of experiments were performed to study the transformation of the amino groups depending on the spin state of the metal. Owing to the wide thermal hysteresis loop of the SCO complex, both spin states were compared under identical conditions. The results revealed that the high-spin state led to the formation of 1.34 times more imine functional groups than the low-spin state, we propose that this arises from the different interactions between the solvent and the SCO at the different spin states.

Two-tiered mutualism improves survival and competitiveness of cross-feeding soil bacteria

Metabolic cross-feeding is a pervasive microbial interaction type that affects community stability and functioning and directs carbon and energy flows. The mechanisms that underlie these interactions and their association with metal/metalloid biogeochemistry, however, remain poorly understood. Here, we identified two soil bacteria, Bacillus sp. BP-3 and Delftia sp. DT-2, that engage in a two-tiered mutualism. Strain BP-3 has low utilization ability of pyruvic acid while strain DT-2 lacks hexokinase, lacks a phosphotransferase system, and is defective in glucose utilization. When strain BP-3 is grown in isolation with glucose, it releases pyruvic acid to the environment resulting in acidification and eventual self-killing. However, when strain BP-3 is grown together with strain DT-2, strain DT-2 utilizes the released pyruvic acid to meet its energy requirements, consequently rescuing strain BP-3 from pyruvic acid-induced growth inhibition. The two bacteria further enhance their collective competitiveness against other microbes by using arsenic as a weapon. Strain DT-2 reduces relatively non-toxic methylarsenate [MAs(V)] to highly toxic methylarsenite [MAs(III)], which kills or suppresses competitors, while strain BP-3 detoxifies MAs(III) by methylation to non-toxic dimethylarsenate [DMAs(V)]. These two arsenic transformations are enhanced when strains DT-2 and BP-3 are grown together. The two strains, along with their close relatives, widely co-occur in soils and their abundances increase with the soil arsenic concentration. Our results reveal that these bacterial types employ a two-tiered mutualism to ensure their collective metabolic activity and maintain their ecological competitive against other soil microbes. These findings shed light on the intricateness of bacterial interactions and their roles in ecosystem functioning.

First-principles and machine-learning approaches for interpreting and predicting the properties of MXenes

MXenes are a versatile family of 2D inorganic materials with applications in energy storage, shielding, sensing, and catalysis. This review highlights computational studies using density functional theory and machine-learning approaches to explore their structure (stacking, functionalization, doping), properties (electronic, mechanical, magnetic), and application potential. Key advances and challenges are critically examined, offering insights into applying computational research to transition these materials from the lab to practical use.

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