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Brain metastases represent a major indication for stereotactic radiosurgery (SRS), but further study is needed regarding repeat SRS (SRS2) after local or marginal recurrence after prior SRS (SRS1). We report local tumor control (LC) after SRS2 and identify predictors of radiation necrosis (RN) and symptomatic RN (SRN).
In this work, we have controlled locally the magnetic anisotropy of PdCo multilayers using ion beam modification. First, we have used techniques with different probing depths to confirm that cobalt is partially oxidized in the Pd/Co multilayers exhibiting PMA fabricated by sputtering, presumably due to the diffusion of atmospheric oxygen migrated through the Pd capping layer. Then, low energy ion bombardment has been employed to induce a structural disorder in the upper layers of the multilayer, gradually reducing the PMA at local scale with increasing ion doses. Medium ion doses lead to a marked decline in PMA, resulting in equivalent magnetic anisotropy directions and the loss of characteristic labyrinthine magnetic domain morphology. High ion doses completely suppress PMA, yielding a typical in-plane shape anisotropy. Furthermore, by combining controlled PMA reduction via lithographic techniques, we create elongated structures displaying distinct magnetic responses; bombarded regions exhibit reduced PMA, whereas adjacent regions maintain their PMA. Notably, regions with reduced PMA do not disrupt the distribution of perpendicular magnetic domains in neighboring PMA regions, even at widths as narrow as 500 nm, demonstrating the robustness of the PMA domain structure.
The local crystal structure of Y 3 NbO 7 was probed using neutron total scattering. Five different structural models were fit to the experimental pair distribution function of this material up to 15 Å. Fits of defect fluorite (Fm$\overline{3}$m), pyrochlore (Fd$\overline{3}$m), and orthorhombic C weberite models (C222 1 ) were attempted based on prior structural investigations of Y 3 NbO 7 and related fluorite-based materials. Orthorhombic P weberite (P222 1 ) and monoclinic weberite models (P112 1 ) were derived via symmetry reduction of the orthorhombic C weberite structure. All models except monoclinic weberite failed to reproduce the experimental pair distribution function. Misfits were clearly visible for peaks arising from atom pairs within the first coordination shell (defect fluorite and pyrochlore) and beyond (orthorhombic C and P weberite). By contrast, monoclinic weberite (γ ≈ 91.8°) provided an excellent fit, with no major misfits observed. This unit cell features distorted YO 8 dodecahedra, YO 7 monocapped octahedra, and NbO 6 octahedra as building blocks. Comparison between the topologies of the cationic and anionic substructures in monoclinic weberite and its parent orthorhombic C structure revealed displacements of the metal atoms along the [100] direction and a less compact spatial distribution of oxygen atoms along the a axis. In conclusion, the monoclinic weberite model presented herein may serve as a starting point to revisit structural modeling of fluorite-based materials with challenging local structures.
The high solidification rates during additive manufacturing cause highly localized thermal and strain gradients. The effect of these gradients on the evolution of local orientation misorientations within a grain is not well understood. In this study, stainless steel 316H parts were fabricated via laser powder bed fusion using three different energy densities: 43, 71, and 135 J/mm 3 . Electron backscatter diffraction showed that the maximum misorientations of the grains can be up to 25° along the build direction. Misorientation gradients (RM g ) within grains are process-dependent and can change from 0.036°/μm to 0.015°/μm with increased volumetric energy densities. In conclusion, the characterized misorientation gradients are an indication of the level of dislocations and, to an extent, the plastic deformation resulting from the rapid solidification during laser powder bed fusion.
Neutron total scattering measurements and multi-edge X-ray absorption analysis were performed on a 0.5Ba(Zr 0.2 Ti 0.8 )O 3 -0.5(Ba 0.7 Ca 0.3 )TiO 3 powder sample. The TiIII-II edges showed that titanium is distorted in the rhombohedral [111] direction (in a BaTiO 3 -like chemical and bonding environment). Extended X-ray fine structure analysis (EXAFS) was performed on the zirconium K-edge and it was found that zirconium resides in the center of the ZrO 6 octahedra in cubic symmetry. Furthermore, the local structure was initially modelled with the orthorhombic Amm2 space group with unsatisfactory results. To better model the local structure, the results from the EXAFS analysis were incorporated into the small-box pair distribution function (PDF) refinements and the best fit of the observed data was a combination of barium calcium titanate (BCT) and barium calcium zirconate (BCZ) phases.
The mesoscopic phase behavior of decylmethylimidazolium (C 10 mim) ionic liquids (ILs) bearing three monovalent anions—thiocyanate (SCN − ), nitrate (NO 3 − ), and chloride (Cl − )—mixed with water at relatively high IL contents (50–95 wt%) was investigated. Small-angle and wide-angle X-ray scattering (SAXS/WAXS) were employed to follow the evolution of both local and mesoscale structures across this composition range. In the absence of water, C 10 mimSCN and C 10 mimCl behaved as disordered liquids, whereas C 10 mimNO 3 spontaneously formed a hexagonally ordered cylindrical mesophase and displayed a sticky-solid macroscopic appearance. Upon addition of water, C 10 mimSCN remained a viscous liquid and only weakly ordered lamellar domains were observed. This limited ordering is attributed to the lack of hydrogen bonding and weak interaction energy between SCN − anions and C 10 mim + cations. In contrast, the trigonal-planar NO 3 − and point-like Cl − anions promoted the formation of well-defined hexagonal mesophases up to 35–45 wt% water. The formation of hydrogen bonding of the two anions with imidazolium ring protons likely enabled the creation of compact ion clusters that effectively exclude water molecules from the immediate vicinity of the IL aggregates. These findings demonstrate that the interaction energy between ion pairs dictate IL-water interactions and therefore control the transition from disordered liquids to ordered mesophases in IL/water mixtures with high IL contents. In conclusion, the combined SAXS/WAXS analysis reveals a correlation between local intermolecular structure and the emergence of mesoscopic order, providing a systematic framework for tailoring mesoscale structures in alkylimidazolium-based IL/water systems.
Carbon-based perovskite solar cells (C-PSCs) present a low-cost route to efficient photovoltaic technology. Gas quenching is an essential process commonly used for solvent extraction in scalable module fabrication using solution processes, but film quality remains highly sensitive to the localized environmental processing temperature, especially during intermediate phase transitions before perovskite film high temperature annealing. In this study, we introduce a cold air quench strategy to precisely control the local crystallization temperature that simultaneously promotes uniform crystallization and facilitates strain relaxation in fully air-processed C-PSCs. It is found that implementing dry air at 10 °C as the quenching gas yields a champion power conversion efficiency (PCE) of 20.52 %, with preferential (110) orientation and reduced, homogenized in-plane strain. This newly developed technique of a cost-effective low temperature air quenching process also enhances carrier lifetime, reduces interface recombination, and improves charge extraction. Furthermore, this work presents cold air quenching as a scalable, economic method to control perovskite crystallization and strain in C-PSC fabrication, advancing the industrial viability of high-performance perovskite modules.
When learning, the brain modifies individual synaptic connections to reach a desired behavior. Animal and human brains have been shown to be incredibly capable of learning complex and varied functions across a wide variety of tasks. In recent years, artificial neural networks, inspired by human and animal brains, have shown great capabilities in learning a wide variety of difficult tasks. However, artificial neural networks primarily teach themselves through the use of backpropagation, a learning method which has no clear analogue within the brain. Additionally, Artificial Neural Networks primarily use continuous activation functions, which differ significantly from the spiking neuronal behavior present in the brain. In this paper, we discuss and demonstrate a biologically plausible learning method that approximates backpropagation through two techniques on Spiking Neural Networks. First, we show that the local temporal derivatives that are necessary for backpropagation can be approximately recovered through reconstruction using spike timings. Second, we show that through learning during a sleep phase, inspired by neuroscience research into memory replay, the localized parallel feedback path can learn to approximate the derivative through the forward path weight matrix, thus solving the weight transport problem. Lastly, we demonstrate that the combination of these two methods can approach or exceed the accuracy of backpropagation-based methods for a variety of neuromorphic vision tasks while maintaining biological plausibility.
In this paper, we apply a new model to account for varying background radiation in radiological source localization. We present a hierarchical Bayesian model that simultaneously infers background and source location parameters without requiring separate estimation of the background radiation at each detector location. We employ a simplified photon transport model to reduce the computational expense of Bayesian model calibration. Furthermore, we demonstrate the model accuracy by localizing a cesium-137 source in a simulated city block, and we analyze experimental field measurements with varying background. In both cases, the model provides sufficient fidelity that we can locate the source while simultaneously estimating background radiation.
Here we have improved the AC orbit bump technique of local kick factor measurement. This technique, developed at NSLS-II a few years ago, is based on local sine-wave excitation of the electron beam by four fast correctors adjacent to the impedance location. By implementing reference bunches and the time gate function of beam position monitors, we are now able to measure the AC orbits of bunches with different intensities simultaneously. This improvement allows us to obtain the impedance-driven orbit distortion in a single measurement, significantly improving the measurement accuracy and reducing the measurement time.
Three factors govern adiabatic shear localization: strain hardening (or softening), strain-rate hardening, and thermal softening. It is typically associated with large shear strains (>1), high strain rates (10 3 –10 7 s –1 ), and high temperatures (40–100% of melting point), all of which happen within narrow regions with widths of about 1–200 μm. It is often an undesirable phenomenon, leading to failure, but there are situations where it is desirable, e. g., the generation of machining chips. Here, we review the development of both theoretical and experimental achievements, from the initiation of shear bands to their propagation with emphasis on three aspects: novel experimental techniques, novel materials, and nano/microstructural effects. The principal characteristics of adiabatic shear bands in metallic materials at the nano- and micro-scale are described. Bands that were formerly identified as transformed actually consist of nanocrystalline/ultrafine grains. These grains result from the breakup of the microstructure by a rotational recrystallization process. The evolution of the microstructure inside shear bands and their interactions for hcp, bcc, and fcc alloys, high-entropy alloys, nanocrystalline alloys, and metallic glasses are analyzed mechanistically. In conclusion, the gaps in the field and opportunities for future research are identified. Modern experimental characterization and computational techniques enable a more profound and predictive understanding of adiabatic shear localization and its avoidance in advanced materials.
The topic of microbial hotspots in soil requires not only visualizing their spatial distribution and biochemical analyses, but also statistical approaches to identify these hotspots and separate them from the surrounding activities (background). We hypothesized that each hotspot type (e.g. enzyme activities in the rhizosphere, root exudation, localization of herbicide accumulation) is a result of local process driven by biotic and/or abiotic factors, and the process rates in the hotspots are much faster than those in the soil background. We further hypothesized that the background and hotspot activities in soil belong to different statistical distributions. Consequently, hotspot determination should be based on statistical separation of activities significantly higher than the background. We analyzed for the statistical distributions of grey values on three groups of published images: 1) 14 C images of carbon input by roots into the rhizosphere, 2) 14 C glyphosate accumulation in the plant, and 3) zymogram of leucine aminopeptidase activity in rooted soil. The two Gaussian distributions were fit (the first representing the background, the second the hotspots) to the distribution of grey values in the images, the parameters (means and standard deviations, SD) of the fitted distributions were calculated, and the background was removed. Thus, we identified hotspots as areas outside of the Mean+2SD image intensity (corresponding to the upper ~ 2.5% of activity, being over 97.5% of background values) and finally, visualized images of solely hotspot locations. Finally, these results were compared with previously used decisions on hotspot intensity thresholding (i.e. Top-25% and 17 standard thresholding approaches in ImageJ) and discussed the advantages of the Mean+2SD as well as Mean+3SD approaches. These advantages include: i) simple unification of the thresholding approach for several imaging methods with various principles of activity distribution, ii) identification of hotspots with various activity levels, iii) analysis of “time-specific” hotspots in temporal sequences of images. Compared with 17 standard thresholding methods, we concluded that objectively elucidating and separating the hotspots should be based on statistical distribution analysis, e.g. using the Mean+2SD or Mean+3SD approaches. Furthermore, this simple Mean+2SD approach delivered suitable results for three groups of images and so, helps to understand the processes responsible for the highest activities and elucidate hotspots.
Amorphous grain boundary complexions act as toughening features within a microstructure because they can absorb dislocations more efficiently than traditional grain boundaries. This toughening effect should be a strong function of the local internal structure of the complexion, which has recently been shown to be determined by grain boundary crystallography. To test this hypothesis, molecular dynamics are used here to simulate dislocation absorption and damage nucleation for complexions with different distributions of structural short-range order. Here, the complexion with a more disordered structure away from the dislocation absorption site is actually found to better resist crack nucleation, as damage tolerance requires delocalized deformation and the operation of shear-transformation zones through the complexion thickness. The more damage tolerant complexion accommodates plastic strain efficiently within the entire complexion, providing the key mechanistic insight that local patterning and asymmetry of structural short-range order controls the toughening effect of amorphous complexions.
The transition to active distribution grids with real-time monitoring and control depends on the proliferation of advanced communication networks and devices. This paradigm shift towards a cyber-physical architecture also introduces new vulnerabilities for adversaries to exploit and launch sophisticated cyber-physical attacks targeting grid observability. Current research highlights the challenges in distinguishing attacks on voltage phasor or nodal injection measurements and isolating multi-source attack locations in a multiphase distribution grid. The attack detection and localization methods in literature face accuracy issues, applications across diverse attack scenarios, or scalability limits. Here, to bridge these gaps, this paper proposes a distributed Voltage Inference framework for real-time detection and localization of cyber-physical attacks, addressing scalability, adaptability, and accuracy challenges in state-of-the-art methods. The proposed methodology leverages the distributed nature of the Voltage Inference framework through a two-step process of prediction and correction, together with a tractable graph partitioning approach, providing a reliable solution to identify compromised measurement sources and facilitate isolation. Extensive testing on IEEE 13 and 123-node distribution feeders underscores the algorithm’s efficacy, enhancing the security and resilience of active distribution grids against evolving cyber threats. Additionally, Hardware-in-the-Loop (HIL) implementation validates the proposed strategy’s practical applicability in real-world scenarios.
Understanding and controlling the impact that electrostatic interactions have on the transport of injected charge carriers is important for the utilization of pi-conjugated semiconductors in opto-electronic applications. Here, we explore the impact of dopant chemical and electronic structure on the doping efficacy and charge carrier transport in semiconducting single-walled carbon nanotube (s-SWCNT) networks using molecular charge-transfer dopants based on functionalized icosahedral dodecaborane (DDB) clusters. Calculations indicate that localization of electron density on the DDB core reduces the coulombic interactions that contribute to hole localization in the s-SWCNTs, thereby improving charge carrier transport. The enhanced delocalization produces an increase in the electrical conductivity and thermopower at lower charge carrier densities, yielding enhanced thermoelectric transport and a thermoelectric power factor that surpasses the previous best in class for enriched s-SWCNT thin-film networks. This strategy can be applied broadly across pi-conjugated semiconductors to tune and enhance performance in a variety of energy harvesting devices.
Kidney Ankyrin Repeat-containing Proteins (KANKs) comprise a family of four evolutionary conserved proteins (KANK1 to 4) that localize to the belt of mature focal adhesions (FAs) where they regulate integrin-mediated adhesion, actomyosin contractility, and link FAs to the cortical microtubule stabilization complex (CMSC). The human KANK proteins were first identified in kidney and have been associated with kidney cancer and nephrotic syndrome. Here, we report the distributions and subcellular localizations of the four Kank mRNAs and proteins in mouse tissues. We found that the KANK family members display distinct and rarely overlapping expression patterns. Whereas KANK1 is expressed at the basal side of epithelial cells of all tissues tested, KANK2 expression is mainly observed at the plasma membrane and/or cytoplasm of mesenchymal cells and KANK3 exclusively in vascular and lymphatic endothelial cells. KANK4 shows the least widespread expression pattern and when present, overlaps with KANK2 in contractile cells, such as smooth muscle cells and pericytes. Our findings show that KANKs are widely expressed in a cell type-specific manner, which suggests that they have cell- and tissue-specific functions.
Highlights: • HPRT is heterogeneously expressed in prostate cancer cells, with surface HPRT being expressed in a p53-dependent fashion. • HPRT is localized to the surface of DU145 prostate cancer cells and is targeted specifically to induce anti-tumor efficacy. • Reductions in p53 expression correspond to reductions in HPRT internal and surface expression within mutant p53 cell lines. We chose to evaluate Hypoxanthine Guanine Phosphoribosyltransferase (HPRT) as a possible biomarker for prostate cancer due to its involvement in nucleotide synthesis and cell cycle progression. We utilized two prostate cancer cell lines (PC3 and DU145) along with patient tissue and knockdowns to evaluate overall HPRT expression. The surface localization of HPRT was determined utilizing flow cytometry, confocal microscopy, and scanning electron microscopy followed by ADCC to evaluate targeting potential. We found significant upregulation of HPRT within malignant samples with approximately 47% of patients had elevated levels of HPRT compared to normal controls. We also observed a significant association between HPRT and the plasma membrane of DU145 cells (p = 0.0004), but found no presence on PC3 cells (p = 0.14). This was confirmed with scanning electron microscopy and confocal microscopy. ADCC experiments were performed to determine whether HPRT could be used as a target antigen for selective cell-mediated killing. We found that DU145 cells treated with HPRT antibodies had a significantly higher incidence of cell death than both isotype treated samples and PC3 cells treated with the same concentrations of HPRT antibody. Finally, we determined that p53 had a significant impact on HPRT expression both internally and on the surface of cancer cells. These results suggest HPRT as a possible biomarker target for the treatment of patients with prostate cancer.
The local electric field strength is thought to affect the rate of water dissociation (WD) in bipolar membranes (BPMs) at the catalyst–nanoparticle surfaces. Here, we study core–shell nanoparticles, where the core is metallic, semiconducting, or insulating, to understand this effect. The nanoparticle cores were coated with a WD catalyst layer (TiO2 or HfO2) via atomic layer deposition (ALD), and the morphology was imaged with transmission electron microscopy. Irrespective of the core material, these core–shell catalysts displayed comparable WD overpotentials at optimal mass loading, despite the hypothesized differences in the electric field strength across the catalyst particle suggested by continuum electrostatic simulations. Substantial atomic interdiffusion between the core and shell was ruled out by X-ray absorption spectroscopy, X-ray photoelectron spectroscopy, and diffuse reflectance optical measurements. However, the optimal mass loading of catalyst was roughly 1 order of magnitude higher for the conductive and high dielectric core materials than for the low dielectric insulating cores. These findings are consistent with the hypothesis that electric field screening within the core material focuses the electric field drop between particles such that larger film thicknesses can be tolerated. Collectively, these data support the idea that it is the local electric field at the molecular level that controls proton-transfer rates and that the metal core/dielectric-shell constructs introduced here modulate that field. Further materials and synthetic design may enable optimization of the electric field strength across the proton-transfer trajectory at the material surface.