Detailed Structure Research Articles

Take a prior from other tasks for severe blur removal

Recovering clear structures from severely blurry inputs is a huge challenge due to the detail loss and ambiguous semantics. Although segmentation maps can help deblur facial images, their effectiveness is limited in complex natural scenes because they ignore the detailed structures necessary for deblurring. Furthermore, direct segmentation of blurry images may introduce error propagation. To alleviate the semantic confusion and avoid error propagation, we propose utilizing high-level vision tasks, such as classification, to learn a comprehensive prior for severe blur removal. We propose a feature learning strategy based on knowledge distillation, which aims to learn the priors with global contexts and sharp local structures. To integrate the priors effectively, we propose a semantic prior embedding layer with multi-level aggregation and semantic attention. We validate our method on natural image deblurring benchmarks by introducing the priors to various models, including UNet and mainstream deblurring baselines, to demonstrate its effectiveness and generalization ability. The results show that our approach outperforms existing methods on severe blur removal with our plug-and-play semantic priors.

Unveiling the Distinct Structure of the Upper Mantle Beneath the Canary and Madeira Hotspots, as Depicted by the 660, 410, and X Discontinuities

AbstractThe Canary and Madeira Islands are two distinct hotspots in the Central‐East Atlantic that are close to each other. Their volcanism is generally attributed to underlying mantle plumes, but the detailed structure of these plumes is still not well understood. The thermal and compositional structure of the plume introduces complexities in the phase transitions of the mantle, which impact the depth and magnitude of seismic discontinuities. We use 1,268 high‐quality receiver functions from stations located at the two hotspots to detect P‐to‐s converted phases through a common‐conversion point stacking approach and conduct a detailed analysis of mantle seismic discontinuities. The results show that both hotspots are characterized by a thin mantle transition zone (MTZ), with sharp 410 and 660 discontinuities at depths of 428–421 km and 647–664 km, beneath the Canaries and Madeira respectively. The results indicate that the Canary plume crosses the MTZ, whereas the Madeira plume mainly influences the upper portion of the MTZ. Furthermore, we find reliable detections of a sharp X discontinuity beneath the Canaries at 287 km. Its presence suggests the accumulation of silica‐rich recycled eclogite at these depths. We also use the amplitudes of P410s and PXs to derive velocity jumps at corresponding discontinuities. Based on these measurements, we estimate that the basalt proportion is 60%–80%, with accumulation being more significant in the Canaries than in Madeira. The MTZ thickness, the presence of the X discontinuity, and the high basalt proportion provide compelling evidence for a deep‐rooted thermochemical plume beneath the study area.

Modeling the phase boundaries in Cu(100)-(2√2 × √2)R45°-O missing row reconstruction (MRR) structure

Although the structure of Cu(100)-(22×2)R45°-O missing row reconstruction (MRR) has been well-established for decades, the detailed structure of its various boundaries remains an untilled area due to the difficulties in obtaining atomically resolved images. Herein, atomic arrangement of the phase boundaries existing in MRR structure was modeled on the basis of scanning tunneling microscopy (STM) investigations. By determining the periodicity and unit structure of MRR in STM images and extending them to boundary region, several types of phase boundaries were identified, resulted respectively from: (1) the mismatch between c(2 × 2)-O patches, (2) the regulation by step edges, and (3) the mismatch between Cu missing rows (MRs). With the modeled structure, it was revealed that the types of the c(2 × 2)-O mismatch induced phase boundaries (OMIPBs) are mainly dominated by the oxygen exposure and in-diffusion barrier. The step edge regulated phase boundaries (SERPBs) are always terminated with Cu-O chain and may represent an intermediate growth stage to larger MRR structure. Comparatively, Cu MRs mismatch is often reconciled by the differently oriented domains between them. As a result, the Cu MRs mismatch induced phase boundaries (CMRMIPBs) are only occasionally observed as Cu-O chains between mismatched Cu MRs that encounter shoulder-to-shoulder. For all studied boundaries, the surrounding MRR domains exhibit obvious orientation preference through inclined packing along the SP direction with the degree closely related with the width of the boundaries.

준거기반내용분석(CBCA)의 변별력

Criteria-Based Content Analysis (CBCA) is part of Statement Validity Analysis (SVA), a process that checks for the presence of 19 criteria that are known to be frequently found in the statements of truth-tellers. This study examined how well CBCA scores predict trial outcomes (guilty or not guilty) and which conventions are associated with higher conviction rates in order to validate the analysis of adult sexual assault victim statements. The results showed that the CBCA total score was not a reliable predictor of guilt or innocence. Next, we examined the truth discriminatory power of CBCA subcategories: logical structure and superfluous details were significantly higher in the guilty group and self-deprecation was significantly higher in the innocent group. The logical structure of the statement was found to be of significant value in assessing credibility, while the other cues were found to be of varying value depending on the characteristics and psychological state of the individual interviewee and the nature of the case, and thus need to be judged in a case-specific manner. These findings suggest that CBCA is a useful tool for analyzing adult sexual assault victim statements, but it is only valuable when used as part of an SVA and cannot be used as a stand-alone authenticity tool.

Sustainable synthesis of praseodymium tungstate: An electrochemical probe for detection of Ronidazole

Profligate consumption of antibiotics in animal and livestock husbandry indulged in antibiotic pollution. This collapses the natural balance of the ecosystem and results in various environmental health crises. Herein, we have developed an ultra-sensitive praseodymium tungstate (PWO) based electrochemical sensor to detect one such serious antibiotic, Ronidazole (RD). A facile synthesis strategy was applied to prepare PWO nanomaterial and their structural, functional, and elemental details were thoroughly examined with a wide range of spectroscopic measurements. The electrochemical properties were determined by performing impedance and voltammetry experiments. The high catalytic efficiency of the designed sensor was achieved by optimizing its performance under different parameters. Based on the outcomes, the designed PWO-based sensor offered an extremely low level of detection (0.3 nM) and exhibited a wide range of linearity (0.001 µM to 20.4 µM), high sensitivity (7.72 µA µM−1 cm−2), and good selectivity. Further, the performance of the electrochemical sensor was validated with good repeatability, reproducibility, and stability results. The practical applicability of the sensor was investigated by monitoring the RD in aquatic, biological, and pharmaceutical samples. The obtained results affirmed that the PWO sensor is an efficient platform for the real-time monitoring of RD.

An efficient Fusion-Purification Network for Cervical pap-smear image classification

Background and Objectives:In cervical cell diagnostics, autonomous screening technology constitutes the foundation of automated diagnostic systems. Currently, numerous deep learning-based classification techniques have been successfully implemented in the analysis of cervical cell images, yielding favorable outcomes. Nevertheless, efficient discrimination of cervical cells continues to be challenging due to large intra-class and small inter-class variations. The key to dealing with this problem is to capture localized informative differences from cervical cell images and to represent discriminative features efficiently. Existing methods neglect the importance of global morphological information, resulting in inadequate feature representation capability. Methods:To address this limitation, we propose a novel cervical cell classification model that focuses on purified fusion information. Specifically, we first integrate the detailed texture information and morphological structure features, named cervical pathology information fusion. Second, in order to enhance the discrimination of cervical cell features and address the data redundancy and bias inherent after fusion, we design a cervical purification bottleneck module. This model strikes a balance between leveraging purified features and facilitating high-efficiency discrimination. Furthermore, we intend to unveil a more intricate cervical cell dataset: Cervical Cytopathology Image Dataset (CCID). Results:Extensive experiments on two real-world datasets show that our proposed model outperforms state-of-the-art cervical cell classification models. Conclusions:The results show that our method can well help pathologists to accurately evaluate cervical smears.

Structural, Optical, and Electronic Properties of Epitaxial β‐(AlxGa1‐x)2O3 Films for Optoelectronic Devices

AbstractBandgap engineering in monoclinic gallium oxide (β‐Ga2O3) is a powerful strategy for designing semiconductor optoelectronic devices with specific functionalities. In this work, aluminum doping is utilized to modulate the bandgap of Ga2O3. By growing epitaxial β‐(AlxGa1‐x)2O3 (0≤ x≤ 0.84) films on c‐plane sapphire substrates using RF magnetron sputtering, it allowed to tune the energy bandgap, achieving values as high as 6.10 eV. The increased luminescence intensity is attributed to the recombination between donor and acceptor transitions induced by Al doping, resulting in more defects. Additionally, the luminescent band experienced blueshifts due to the enhanced bandgaps. Moreover, density of functional theory (DFT) simulations confirmed the sensitivity of the bandgap to Al content, distinguishing between Ga‐dominated (x < 0.5) and Al‐dominated (x > 0.5) β‐(AlxGa1‐x)2O3. Notably, the bandgap increased more rapidly in Ga‐dominated structures compared to Al‐dominated ones. The electronic structure analysis revealed a redistribution of Ga d states from valence to conduction bands, attributed to the introduction of numerous Al p states. These combined experimental and detailed electronic structure investigations proved crucial insights for designing the structure and exploring potential applications of β‐(AlxGa1‐x)2O3 in photonic devices.

Porewater microbiomes in buried wetland soils: Synergic effects of water chemistry and redox gradients associated with hydrological processes

Abstract Interstitial water or pore water occupies the space between soil particles and provides “hotspots” and “fluvial networks” for microbial activities in floodplain soil. However, to date, we know very little about the microorganisms living in pore water and how they respond to environmental changes. This study aimed to understand microbial distribution and assemblage in riparian pore waters, and how they respond to water chemistry and redox gradients associated with hydrological processes. We analysed the annual changes of porewater microbial communities from the east and west banks of the White Clay Creek, a site at the Christina River Basin – Critical Zone Observatory, Pennsylvania, USA. Microbial abundances were quantified by epifluorescence microscopy and detailed community structures were characterised by high‐throughput sequencing. Water chemistry and redox gradients were also monitored and recorded, and their interactions with porewater microbiomes were analysed using correlations and multivariate analyses. Abundance of microbial cells increased during summer and late autumn. Wetland porewater microbiomes mainly contained Acidobacteria, Bacteroidetes, Nitrospirae and Proteobacteria, and microbiome structures were easily distinguishable from those in the underlying hyporheic gravel layer. Seasonal dynamics of bacterial community structure in the east and west wetlands were distinct, responding to floodplain topography and associated hydrological/geochemical processes. Iron (Fe)‐cycling bacteria (mainly Gallionellaceae and Rhodoferax spp.) dominated the porewater microbiome, and their relative abundance was significantly higher in the east than the west wetland. Furthermore, Fe‐oxidising bacteria (Gallionellaceae) were negatively correlated with Fe‐reducing bacteria (Rhodoferax spp.) at the east wetland. Microbial abundances (cell density) in pore waters showed similar seasonal patterns across stream banks, but microbial community structure did not. Microbiome assembly in pore water is correlated with water chemistry and redox gradients primarily associated with local hydrological processes. As a consequence of their significance for carbon (C) mineralisation and Fe reduction at terrestrial–aquatic interfaces, microbiomes in riparian pore waters and associated microbial activity play an essential role in C and mineral dynamics. These findings will inform future studies of the response of freshwater ecosystems to hydrological dynamics influenced by global climate change.

Virtual design of woven fabrics based on parametric modeling and physically based rendering

Textures of woven fabrics are usually designed and produced according to geometric laws in the 2D plane. Physically Based Rendering (PBR) can further optimize and enrich the texture effect, but its application to the more complex 3D structures has been limited. This work reports a method that uses PBR and parametric modeling to construct woven textured materials with centimeter and millimeter level 3D structures. The method can design the structures of various woven fabrics without the need for analyzing the fabric structure details and transfer the inherently iterative work of fabric design to the digital space. The design can be directly applied to mainstream 3D modeling software for virtual presentations in different applications, hence improving the efficiency of woven fabric design and the fidelity of virtual presentation of fabric materials.

Biomimetic enhanced polarization orientation method for underwater scenes

Underwater polarization information can provide reliable autonomous heading feedback for underwater scenes in the presence of satellite denial and multi-source interference. A biologically inspired highly robust polarization orientation strategy is proposed to address the degradation caused by weak polarization and noisy patterns in underwater environments with poor illumination, turbidity, and cloud recession. This approach initially develops a biomimetic algorithm that enhances weak polarization information, based on the increased perception mechanism of the visual neural pathway of syrphid fly under weak light conditions. After overcoming restricting polarization transmission, it is further optimized with a non-local sparse coding denoising part that takes into account the similarity in the detailed structure of the polarized image's effective zenith region. Relying on the stable ∞ symmetric distribution of the angle of polarization, the ±π/2 feature point distribution is traversed and assigned, and polarization information is quantitatively assigned, compensated, and regulated along the solar meridian region, excluding invalid polarized pixels. The experiments demonstrate that the methodology effectively reconstructs the symmetry of the partially damaged angle of polarization distribution, eliminates external interference, autonomously solves the fitted heading correction problem, and improves the environmental adaptability of the bionic polarization compass. The data from underwater experiments indicate the orientation accuracy: the error in weakly polarized patterns is within 2°, and the accuracy in noisy patterns can be increased by over 50%.

Multi-scale feature aggregation and fusion network with self-supervised multi-level perceptual loss for textures preserving low-dose CT denoising

Objective. The textures and detailed structures in computed tomography (CT) images are highly desirable for clinical diagnosis. This study aims to expand the current body of work on textures and details preserving convolutional neural networks for low-dose CT (LDCT) image denoising task. Approach. This study proposed a novel multi-scale feature aggregation and fusion network (MFAF-net) for LDCT image denoising. Specifically, we proposed a multi-scale residual feature aggregation module to characterize multi-scale structural information in CT images, which captures regional-specific inter-scale variations using learned weights. We further proposed a cross-level feature fusion module to integrate cross-level features, which adaptively weights the contributions of features from encoder to decoder by using a spatial pyramid attention mechanism. Moreover, we proposed a self-supervised multi-level perceptual loss module to generate multi-level auxiliary perceptual supervision for recovery of salient textures and structures of tissues and lesions in CT images, which takes advantage of abundant semantic information at various levels. We introduced parameters for the perceptual loss to adaptively weight the contributions of auxiliary features of different levels and we also introduced an automatic parameter tuning strategy for these parameters. Main results. Extensive experimental studies were performed to validate the effectiveness of the proposed method. Experimental results demonstrate that the proposed method can achieve better performance on both fine textures preservation and noise suppression for CT image denoising task compared with other competitive convolutional neural network (CNN) based methods. Significance. The proposed MFAF-net takes advantage of multi-scale receptive fields, cross-level features integration and self-supervised multi-level perceptual loss, enabling more effective recovering of fine textures and detailed structures of tissues and lesions in CT images.

Study on the biodynamic characteristics and internal vibration behaviors of a seated human body under biomechanical characteristics.

To provide reference and theoretical guidance for establishing human body dynamics models and studying biomechanical vibration behavior, this study aimed to develop and verify a computational model of a three-dimensional seated human body with detailed anatomical structure under complex biomechanical characteristics to investigate dynamic characteristics and internal vibration behaviors of the human body. Fifty modes of a seated human body were extracted by modal method. The intervertebral disc and head motions under uniaxial white noise excitation (between 0 and 20Hz at 1.0, 0.5 and 0.5m/s2 r.m.s. for vertical, fore-aft and lateral direction, respectively) were computed by random response analysis method. It was found that there were many modes of the seated human body in the low-frequency range, and the modes that had a great impact on seated human vibration were mainly distributed below 13Hz. The responses of different positions of the spine varied greatly under the fore-aft and lateral excitation, but the maximum stress was distributed in the lumbar under different excitations, which could explain why drivers were prone to lower back pain after prolonged driving. Moreover, there was a large vibration coupling between the vertical and fore-aft direction of an upright seated human body, while the vibration couplings between the lateral and other directions were very small. Overall, the study could provide new insights into not only the overall dynamic characteristics of the human body, but also the internal local motion and biomechanical characteristics under different excitations.

New Schiff Base Derived Organotin (IV) Complexes: Synthesis, Characterization, In vitro and In silico Biological Studies

Aims: The creation and testing of new Schiff base-based antibacterial organotin (IV) complexes were the objectives of this investigation. background: Due to developed resistance, antibiotics that were once often used to treat microorganisms are no longer effective against them. It is thought that organotin compounds synthesized from Schiff bases have significant pharmacological effectiveness and work well as antibacterial agents Background: Due to developed resistance, antibiotics that were once often used to treat microor-ganisms are no longer effective against them. It is thought that organotin compounds synthesized from Schiff bases have significant pharmacological effectiveness and work well as antibacterial agents. Methods: Thiocarbohydrazide and dehydroacetic acid were condensed to create the Schiff base, followed by processing with dialkyltin(IV)dichloride to synthesize the final product. Modern an-alytical techniques were used to clarify the compounds' probable structural details. The crystalline nature of the produced compounds was tested using PXRD. Results: All of the compounds were thermally stable up to 300°C. All of the synthesized com-plexes showed potent antibacterial activity in the range of 250 to 400 μg/ml. Furthermore, the computational biology research showed that, in contrast to ligands, which had a binding energy of -7.3 to -7.4 kcal/mol, complexes interacted well with dihydropteroate synthase and DNA gy-rase. Conclusion: The current study offered a unique technique for synthesizing diorganotin(IV) de-rivatives of N-substituted Schiff bases that are physiologically active. The results show that the chemicals created are promising antibacterial mediators against diseases that affect humans in the modern world. It might also open the door to future studies on drug-resistant microorganisms that could have biological uses.

Chemical shift assignment of dsRBD1 and dsRBD2 of Arabidopsis thaliana DRB3, an essential protein involved in RNAi-mediated antiviral defense.

As sessile organisms, plants need to counteract different biotic and abiotic stresses to survive. RNA interference provides natural immunity against various plant pathogens, especially against viral infections via inhibition of viral genome replication or translation. In plants, DRB3, a multi-domain protein containing two N-terminal dsRNA binding domains (dsRBD), plays a vital role in RNA-directed DNA methylation of the geminiviral genome. Additionally, DRB3 arrests the replication of the viral genome in the viral replication complex of RNA viruses through a mechanism that has yet to be fully deciphered. Therefore, as a first step towards exploring the structural details of DRB3, we present a nearly complete backbone and side chain assignment of the two N-terminal dsRBD domains.

Enhancing the Photocatalytic Activity of Halide Perovskite Cesium Bismuth Bromide/Hydrogen Titanate Heterostructures for Benzyl Alcohol Oxidation.

Halide perovskite Cs3Bi2Br9 (CBB) has excellent potential in photocatalysis due to its promising light-harvesting properties. However, its photocatalytic performance might be limited due to the unfavorable charge carrier migration and water-induced properties, which limit the stability and photocatalytic performance. Therefore, we address this constraint in this work by synthesizing a stable halide perovskite heterojunction by introducing hydrogen titanate nanosheets (H2Ti3O7-NS, HTiO-NS). Optimizing the weight % (wt%) of CBB enables synthesizing the optimal CBB/HTiO-NS, CBHTNS heterostructure. The detailed morphology and structure characterization proved that the cubic shape of CBB is anchored on the HTiO-NS surface. The 30 wt% CBB/HTiO-NS-30 (CBHTNS-30) heterojunction showed the highest BnOH photooxidation performance with 98% conversion and 75% benzoic acid (BzA) selectivity at 2 h under blue light irradiation. Detailed optical and photoelectrochemical characterization showed that the incorporating CBB and HTiO-NS widened the range of the visible-light response and improved the ability to separate the photo-induced charge carriers. The presence of HTiO-NS has increased the oxidative properties, possibly by charge separation in the heterojunction, which facilitated the generation of superoxide and hydroxyl radicals. A possible reaction pathway for the photocatalytic oxidation of BnOH to BzH and BzA was also suggested. Furthermore, through scavenger experiments, we found that the photogenerated h+, e- and •O2- play an essential role in the BnOH photooxidation, while the •OH have a minor effect on the reaction. This work may provide a strategy for using HTiO-NS-based photocatalyst to enhance the charge carrier migration and photocatalytic performance of CBB.

Growing Trend to Adopt Speckle Variance Optical Coherence Tomography for Biological Tissue Assessments in Pre-Clinical Applications.

Speckle patterns are a generic feature in coherent imaging techniques like optical coherence tomography (OCT). Although speckles are granular like noise texture, which degrades the image, they carry information that can be benefited by processing and thereby furnishing crucial information of sample structures, which can serve to provide significant important structural details of samples in in vivo longitudinal pre-clinical monitoring and assessments. Since the motions of tissue molecules are indicated through speckle patterns, speckle variance OCT (SV-OCT) can be well-utilized for quantitative assessments of speckle variance (SV) in biological tissues. SV-OCT has been acknowledged as a promising method for mapping microvasculature in transverse-directional blood vessels with high resolution in micrometers in both the transverse and depth directions. The fundamental scope of this article reviews the state-of-the-art and clinical benefits of SV-OCT to assess biological tissues for pre-clinical applications. In particular, focus on precise quantifications of in vivo vascular response, therapy assessments, and real-time temporal vascular effects of SV-OCT are primarily emphasized. Finally, SV-OCT-incorporating pre-clinical techniques with high potential are presented for future biomedical applications.

Arterial Blood Supply of the Stifle Joint in Horses.

The vascularization pattern of the equine stifle joint is insufficiently described in the literature, even though there is a growing need for knowledge of the exact blood supply, as (i) arthroscopy and endoscopic surgery techniques are increasingly performed in horses and (ii) ex vivo models of menisci need nutrient supply that mimic the in vivo situation. The aim of this study was to describe the vessels involved in the stifle joint supply and the exact branching pattern of the popliteal artery. Colored latex was injected into the arteries of nine pelvic limbs of equine cadavers (n = 6) to evaluate the occurrences, variations and approximate diameters of vessels that supplied the stifle joints. Next to a branch of the saphenous and descending genicular arteries, eleven branches of the popliteal artery could be described in horses that feed the vascular network of the stifle joint. With a focus on the blood supply of the menisci, a vascularization map was created to show the main influx to these intra-articular structures in detail. These findings are potentially of great importance to both clinicians in preparation of best-suited incisions for arthroscopy and researchers designing new approaches for meniscal studies and choosing suitable animal models.

Masked autoencoders with generalizable self-distillation for skin lesion segmentation.

In the field of skin lesion image segmentation, accurate identification and partitioning of diseased regions is of vital importance for in-depth analysis of skin cancer. Self-supervised learning, i.e., MAE, has emerged as a potent force in the medical imaging domain, which autonomously learns and extracts latent features from unlabeled data, thereby yielding pre-trained models that greatly assist downstream tasks. To encourage pre-trained models to more comprehensively learn the global structural and local detail information inherent in dermoscopy images, we introduce a Teacher-Student architecture, named TEDMAE, by incorporating a self-distillation mechanism, it learns holistic image feature information to improve the generalizable global knowledge learning of the student MAE model. To make the image features learned by the model suitable for unknown test images, two optimization strategies are, Exterior Conversion Augmentation (EC) utilizes random convolutional kernels and linear interpolation to effectively transform the input image into one with the same shape but altered intensities and textures, while Dynamic Feature Generation (DF) employs a nonlinear attention mechanism for feature merging, enhancing the expressive power of the features, are proposed to enhance the generalizability of global features learned by the teacher model, thereby improving the overall generalization capability of the pre-trained models. Experimental results from the three public skin disease datasets, ISIC2019, ISIC2017, and PH indicate that our proposed TEDMAE method outperforms several similar approaches. Specifically, TEDMAE demonstrated optimal segmentation and generalization performance on the ISIC2017 and PH datasets, with Dice scores reaching 82.1% and 91.2%, respectively. The best Jaccard values were 72.6% and 84.5%, while the optimal HD95% values were 13.0% and 8.9%, respectively.

A Cofacial Porphyrin Dimer Generated by Cooperative Zinc Ion Binding

Noncovalent cofacial porphyrin dimers have attracted interest due to their potential use as host materials or catalysts. In this study, we introduce a novel coordination‐directed cofacial porphyrin dimer from a meso‐tetraphenylporphyrin derivative modified with 2,2'bipyridine moieties at the meta positions of the phenyl groups. Spectroscopic investigation has shed light on the binding processes and the structure of the dimer in solution in detail. Initial Zn2+ ion binding to the porphyrin hinders the subsequent Zn2+ binding and the binding of the second porphyrin. However, once the dimer is formed, additional Zn2+ binding is notably enhanced, resulting in a pronounced S‐shaped binding curve. The detailed structure of the dimer in solution was established based on UV‐vis spectroscopy, various 1H NMR spectroscopy techniques and mass spectrometry. Simulations of ringcurrent‐induced chemical shift changes provided evidence for the face‐to‐face porphyrin structure with tetrahedral coordination of the bipyridine ligands. The X‐ray single crystal structure provided conclusive evidence for the structure of the face‐to‐face dimer, which remarkably resembles the model structure from the 1H NMR simulation. The stability of the dimer structure makes it promising for further exploration of this motif in developing new supramolecular structures and for their potential applications as host compounds and catalysts.

What is the best simulation approach for measuring local density fluctuations near solvo-/hydrophobes?

Measurements of local density fluctuations are crucial to characterizing the interfacial properties of equilibrium fluids. A specific case that has been well-explored involves the heightened compressibility of water near hydrophobic entities. Commonly, a spatial profile of local fluctuation strength is constructed from the measurements of the mean and variance of solvent particle number fluctuations in a set of contiguous subvolumes of the system adjacent to the solvo-/hydrophobe. An alternative measure proposed by Evans and Stewart [J. Phys.: Condens. Matter 27, 194111 (2015)] defines a local compressibility profile in terms of the chemical potential derivative of the spatial number density profile. Using Grand canonical Monte Carlo simulation, we compare and contrast the efficacy of these two approaches for a Lennard-Jones solvent at spherical and planar solvophobic interfaces and SPC/E water at a hydrophobic spherical solute. Our principal findings are as follows: (i) the local compressibility profile χ(r) of Evans and Stewart is considerably more sensitive to variations in the strength of local density fluctuations than the spatial fluctuation profile F(r) and can resolve much more detailed structure; and (ii) while the local compressibility profile is essentially independent of the choice of spatial discretization used to construct the profile, the spatial fluctuation profile exhibits a strong systematic dependence on the size of the subvolumes on which the profile is defined. We clarify the origin and nature of this finite-size effect.