Preparation of a novel foamed concrete modified with carbon fiber and graphite: Mechanical, electro-magnetic and microstructural characteristics based on X-CT.

In this paper, foam concrete is modified using graphite and carbon fiber as absorbents. The mechanical properties are analyzed in conjunction with hydration products, pore size distribution based on XCT test. Additionally, the resistivity, complex permittivity and complex permeability are tested. The results demonstrate that carbon fiber enhances the proportion of pores with diameters less than 200 µm in foam concrete, thereby significantly enhancing its flexural strength. Furthermore, incorporating graphite helps offset the initial retardation of sulfoaluminate cement hydration induced by carbon fibers, leading to an increase in the average pore size and a reduction in compressive strength. The incorporation of carbon fibers at a concentration of 0.6 wt% achieves the percolation threshold, akin to scenarios with singular fiber incorporation. Exceeding 2 wt% graphite content results in negligible influence on the conductivity. The synergistic integration of graphite and carbon fibers significantly improves the electromagnetic wave absorption performance of the composite. At a thickness of 6 mm, the material exhibits an effective bandwidth where the reflection loss is less than -10 dB, extending up to 2.5 GHz, which constitutes 52.08 % of the tested frequency spectrum.

OpenFold: retraining AlphaFold2 yields new insights into its learning mechanisms and capacity for generalization.

AlphaFold2 revolutionized structural biology with the ability to predict protein structures with exceptionally high accuracy. Its implementation, however, lacks the code and data required to train new models. These are necessary to (1) tackle new tasks, like protein-ligand complex structure prediction, (2) investigate the process by which the model learns and (3) assess the model's capacity to generalize to unseen regions of fold space. Here we report OpenFold, a fast, memory efficient and trainable implementation of AlphaFold2. We train OpenFold from scratch, matching the accuracy of AlphaFold2. Having established parity, we find that OpenFold is remarkably robust at generalizing even when the size and diversity of its training set is deliberately limited, including near-complete elisions of classes of secondary structure elements. By analyzing intermediate structures produced during training, we also gain insights into the hierarchical manner in which OpenFold learns to fold. In sum, our studies demonstrate the power and utility of OpenFold, which we believe will prove to be a crucial resource for the protein modeling community.

Rapid 3D Bioprinting of Glioblastoma Model Mimicking Native Biophysical Heterogeneity.

Glioblastoma multiforme (GBM) is the most lethal primary brain tumor characterized by high cellular and molecular heterogeneity, hypervascularization, and innate drug resistance. Cellular components and extracellular matrix (ECM) are the two primary sources of heterogeneity in GBM. Here, biomimetic tri-regional GBM models with tumor regions, acellular ECM regions, and an endothelial region with regional stiffnesses patterned corresponding to the GBM stroma, pathological or normal brain parenchyma, and brain capillaries, are developed. Patient-derived GBM cells, human endothelial cells, and hyaluronic acid derivatives are used to generate a species-matched and biochemically relevant microenvironment. This in vitro study demonstrates that biophysical cues are involved in various tumor cell behaviors and angiogenic potentials and promote different molecular subtypes of GBM. The stiff models are enriched in the mesenchymal subtype, exhibit diffuse invasion of tumor cells, and induce protruding angiogenesis and higher drug resistance to temozolomide. Meanwhile, the soft models demonstrate enrichment in the classical subtype and support expansive cell growth. The three-dimensional bioprinting technology utilized in this study enables rapid, flexible, and reproducible patient-specific GBM modeling with biophysical heterogeneity that can be employed by future studies as a tunable system to interrogate GBM disease mechanisms and screen drug compounds.