Spatial Regions Research Articles

Photothermal manipulation of the fringing field in gold nanorod dimers towards the apoptosis of cancerous cells

The possibility of coherent manipulation of optical and thermal energies in noble metal nanostructures has given birth to an enduring research arena coined by thermoplasmonics. Upon interaction with electromagnetic radiation, the energy of the produced hot electrons in metallic nanostructures is converted into heat and is transferred to the medium as a consequence of numerous relaxation processes. Gold nanorods have, often, been adopted as the classical anisotropic nanostructures owing to excellent shape-selective plasmonic tunability in the vis–NIR region. When a pair of metallic nanostructures are sufficiently close to each other to imbue electromagnetic interaction, there occurs evolution of collective plasmon modes, substantial enhancement of near field and strong squeezing of electromagnetic energy at the interparticle spatial region of the dimeric nanostructures. Recent advances in the ‘tips and tricks’ guide to assembling, even, anisotropic nanostructures in colloidal dispersions have offered the opportunity to interplay with the phenomenological plasmonic and thermal characteristics. The photothermal attributes emerging due to electromagnetic coupling of fringing fields have been explored considering parallel and perpendicular configurations of gold nanorod dimers as the prototypical systems from theoretical and experimental perspectives and their biomedical consequences have been realised in a mice model towards the photothermal apoptosis of cancerous cells.

Convolutional Neural Networks for Segmentation of Pleural Mesothelioma: Analysis of Probability Map Thresholds (CALGB 30901, Alliance).

The purpose of this study was to evaluate the impact of probability map threshold on pleural mesothelioma (PM) tumor delineations generated using a convolutional neural network (CNN). One hundred eighty-six CT scans from 48 PM patients were segmented by a VGG16/U-Net CNN. A radiologist modified the contours generated at a 0.5 probability threshold. Percent difference of tumor volume and overlap using the Dice Similarity Coefficient (DSC) were compared between the reference standard provided by the radiologist and CNN outputs for thresholds ranging from 0.001 to 0.9. CNN-derived contours consistently yielded smaller tumor volumes than radiologist contours. Reducing the probability threshold from 0.5 to 0.01 decreased the absolute percent volume difference, on average, from 42.93% to 26.60%. Median and mean DSC ranged from 0.57 to 0.59, with a peak at a threshold of 0.2; no distinct threshold was found for percent volume difference. The CNN exhibited deficiencies with specific disease presentations, such as severe pleural effusion or disease in the pleural fissure. No single output threshold in the CNN probability maps was optimal for both tumor volume and DSC. This study emphasized the importance of considering both figures of merit when evaluating deep learning-based tumor segmentations across probability thresholds. This work underscores the need to simultaneously assess tumor volume and spatial overlap when evaluating CNN performance. While automated segmentations may yield comparable tumor volumes to that of the reference standard, the spatial region delineated by the CNN at a specific threshold is equally important.

Low-current gliding DC discharge in high-speed flows

Abstract A low-current gliding discharge (current range 1–5 A) in high-speed air flows of 100–250 m s−1 was experimentally studied. A high-voltage direct current source with a maximum voltage of 4.5 kV was used to create the discharge. The average electron concentration n e ∼ 1014 cm−3 and the plasma ionization degree were determined by measuring the Stark broadening of the hydrogen H β line (λ Hβ = 486.1 nm). The estimates of the electric field (E ∼ 100 V cm −1 ÷ 600 V cm−1) in the discharge positive column were found using time-synchronized high-speed video recordings and oscillograms. The gas rotational temperature T g = 7000–9500 K and the vibrational temperature T v = 7000–11 000 K were estimated using optical emission spectroscopy. Time-resolved spectroscopy is used to investigate the effective plasma channel spatial regions from which the N, NH, N2 +, O and OH molecules radiate. The difference of the obtained radii indicates the presence of a radial temperature gradient and inhomogeneous plasma composition in the discharge cross section. The possibility of using of gliding discharge to ignite hydrocarbon-air mixtures in the ramjet engines combustors has been experimentally demonstrated.

Spatially hybrid control of entanglement between atom and photon

In this study, we introduced a model utilizing a plasmonic nanostructure to modulate the entanglement between atoms and photons within a V-type atomic medium. This system is influenced by composite optical vortex light (COVL) and two incoherent pumping fields. The plasmonic nanostructure comprises dielectric nanospheres, which have been thoroughly investigated (Phys. Rev. B 106, 035419, 2022). By adjusting the orbital angular momentum (OAM) of COVL, we demonstrate that the nanostructure induces significant spatial variations in the degree of entanglement (DEM). Additionally, incorporating a weak incoherent pumping field affects the DEM profile within the hybrid system. Our findings reveal that the OAM of structured light can govern the spatial dependence of DEM, particularly when quantum interference from the nanostructure and pumping fields surpasses a specific threshold. Furthermore, our results suggest that analyzing the DEM profile's spatial regions can be employed to determine the OAM number of the structured light.

Facile and controllable hybrid-nanoengineering of MWCNTs/Au@ZIF-8 and AuPt@CeO2 based sandwich electrochemical aptasensor for AFB1 determination in foods and herbs

Herein, a sandwich electrochemical sensing strategy for aflatoxin b1 (AFB1) detection based on hybrid-nanoengineering was presented. First, Au nanoparticle was doped into zeolitic imidazolate framework-8 (ZIF-8) to form Au@ZIF-8 by in-situ growth method, followed by multi-walled carbon nanotubes (MWCNTs) addition to synthesize MWCNTs/Au@ZIF-8 via self-assembly. The structural “confinement effect” of ZIF-8 afforded a microenvironment for Au nanoparticles and SMCNTs in a certain spatial region, giving MWCNTs/Au@ZIF-8 excellent electrochemical property as the substrate material. In addition, Au-Pt bimetallic nanoparticle, which exhibited excellent stability and catalytic activity was loaded on the hollow cerium oxide (CeO2) to form AuPt@CeO2 nanoparticle through one-step aqueous phase reduction. Owning to its high surface-to-volume ratio, satisfied electron transfer efficiency and biocompatibility, massive toluidine blue (TB) and AFB1 antibody (Ab) could be modified on the AuPt@CeO2 to form AuPt@CeO2-Ab-TB, which acted as signal tag for the ultrasensitive assay of AFB1. The proposed electrochemical sensing system exhibited wide detection range (2 × 10-5 − 20 ng/mL) and low detection limit (2.13 fg/mL), which has been successfully applied to AFB1 determination in four real samples. The hybrid nanoengineering presented in this work is an active attempt to prepare high-performance substrate material and signal tag, which provides a new insight for the development of highly sensitive and specific electrochemical sensing systems.

Entropy production-based nonlinear optimal perturbation for subsonic flows around an airfoil

The extraction and time evolution of optimal perturbation (OP) offers abundant physical insights in fluid dynamics. Nonlinear OP (NLOP) analysis provides an approach for obtaining the trajectory to induce the maximum changes in the flow field. In an extension into unsteady flow field, we tracked the changes of trajectory by an application of initial perturbation field in the compressible Navier–Stokes equation, and we focused on the entropy production (EP) to characterize the trajectory. We proposed entropy production-based NLOP (EP-NLOP) analysis for compressible flows and investigated the effect of evaluation function on the extracted Ops using the subsonic flow around an airfoil. Compared with the conventional disturbance energy (DE-) based NLOP (DE-NLOP) analysis, we demonstrated that the OPs with different spatial wavelength and concentration regions were successfully extracted due to the different spatial sensitivity of evaluation function. In the EP-NLOP analysis, the spatial distribution of OP extracted the larger energy dissipation upstream of the separation points for the short evaluation time. For the long evaluation time, EP-NLOP analysis extracted the transient-time evolution of interacting separation vortices, attributing the multiple wavelengths of OPs. These differences in the OPs offer promising insights into fluid dynamics.

Using computer vision to determine nutrient deficiency in winter crops

Winter wheat and winter rye often suffer from nutrient deficiencies due to variations in fertilizer usage, soil conditions, and other environmental factors. The timely and precise detection of these deficiencies, traditionally conducted through manual field inspection and soil testing, lacks scalability and often delays necessary interventions. To overcome these limitations, I conducted original research exploring the use of a Vision Transformer neural network, specifically the Swin Transformer V2, to classify crops into one of seven nutrient categories from UAV-based imagery. Two distinct, annotated datasets consisting of winter wheat (WW2020) and winter rye (WR2021) were used. Each dataset was subjected to an independent model training to account for visual differences in nutrient deficiencies between the crops. This approach leverages the advantages of the Vision Transformer, which has the capability to discern intricate patterns over expansive spatial regions. Top-1 accuracy was used to evaluate model predictions. A train-validation split yielded 67.7% accuracy for WW2020 and 68.2% for WR2021, while without a split, accuracy improved to 78.0% for WW2020 and 82.3% for WR2021. The Swin Transformer V2 shows promise for detecting nutrient deficiencies in winter crops. However, further fine-tuning and data collection are required to improve performance.

A Study of Classroom Behavior Recognition Incorporating Super-Resolution and Target Detection.

With the development of educational technology, machine learning and deep learning provide technical support for traditional classroom observation assessment. However, in real classroom scenarios, the technique faces challenges such as lack of clarity of raw images, complexity of datasets, multi-target detection errors, and complexity of character interactions. Based on the above problems, a student classroom behavior recognition network incorporating super-resolution and target detection is proposed. To cope with the problem of unclear original images in the classroom scenario, SRGAN (Super Resolution Generative Adversarial Network for Images) is used to improve the image resolution and thus the recognition accuracy. To address the dataset complexity and multi-targeting problems, feature extraction is optimized, and multi-scale feature recognition is enhanced by introducing AKConv and LASK attention mechanisms into the Backbone module of the YOLOv8s algorithm. To improve the character interaction complexity problem, the CBAM attention mechanism is integrated to enhance the recognition of important feature channels and spatial regions. Experiments show that it can detect six behaviors of students-raising their hands, reading, writing, playing on their cell phones, looking down, and leaning on the table-in high-definition images. And the accuracy and robustness of this network is verified. Compared with small-object detection algorithms such as Faster R-CNN, YOLOv5, and YOLOv8s, this network demonstrates good detection performance on low-resolution small objects, complex datasets with numerous targets, occlusion, and overlapping students.

An explainable weakly supervised model for multi-disease detection and localization from thoracic X-rays

Thoracic diseases are a major source of mortality, often requiring diagnosis from plain chest X-rays. However, differentiating between complex conditions based on subtle radiographic patterns poses challenges even for experts. Recently, deep learning methods have shown promise in automating thoracic disease detection from chest radiographs. Many existing approaches focus on the diseased organs in the radiographs by utilizing spatial regions that contribute significantly to the model’s prediction. Expert radiologists, on the other hand, first identify the prominent region before determining whether those regions are abnormal or not. Therefore, incorporating localization information through deep learning models could result in significant improvements in automatic disease classification. Motivated by this, we have proposed a generalized weakly supervised Confidence-Aware Probabilistic Class Activation Map (CAPCAM) classification model that localizes anomalies for thoracic disease. The CAPCAM used CX-Ultranet as the backbone with the combination of Confidence Aware Network (CAN) and Anomaly Detection Network (ADN) without having any localization labeling. This learning from the backbone helps the model to utilize all components of the feature extracted and, therefore eliminating the need to train them individually reducing the time taken. We have experimentally shown that the proposed CAPCAM method sets a new state-of-the-art benchmark by achieving accuracy in terms of Intersection of bounding box (IoBB) in the range of 85 % - 94 %, and Dice scores in the range of 88 %-90 % for all thirteen diseases on two publicly available large-scale CXR datasets–NIH, Stanford and CheXpert. Testing across different noise levels and different levels of blurred level assessed real-world viability. We have also added a layer of explainability to show how the image is processed. This study demonstrates deep learning’s potential to augment radiologists’ decision-making by providing fast, accurate automated aids for thoracic disease diagnosis. The proposed CAPCAM model could be readily translatable to improve clinical workflows.

Large field of view and spatial region of interest transcriptomics in fixed tissue

Expression profiling in spatially defined regions is crucial for systematically understanding tissue complexity. Here, we report a method of photo-irradiation for in-situ barcoding hybridization and ligation sequencing, named PBHL-seq, which allows targeted expression profiling from the photo-irradiated region of interest in intact fresh frozen and formalin fixation and paraffin embedding (FFPE) tissue samples. PBHL-seq uses photo-caged oligodeoxynucleotides for in situ reverse transcription followed by spatially targeted barcoding of cDNAs to create spatially indexed transcriptomes of photo-illuminated regions. We recover thousands of differentially enriched transcripts from different regions by applying PBHL-seq to OCT-embedded tissue (E14.5 mouse embryo and mouse brain) and FFPE mouse embryo (E15.5). We also apply PBHL-seq to the subcellular microstructures (cytoplasm and nucleus, respectively) and detect thousands of differential expression genes. Thus, PBHL-seq provides an accessible workflow for expression profiles from the region of interest in frozen and FFPE tissue at subcellular resolution with areas expandable to centimeter scale, while preserving the sample intact for downstream analysis to promote the development of transcriptomics.

Enhancing triboelectrification via multiscale roughness dependent thermal dissipation

Polymer-based triboelectric nanogenerators (TENGs) have held promise due to their excellent interfacial conformity and ease of fabrication. However, the role of surface roughness in triboelectricity requires further study. In this study, we have manipulated the nano-/micro-scale roughness configuration in polydimethylsiloxane (PDMS) over a wide range of extents using various sandpaper-based templates. According to the power spectral density analysis, the spatial frequency of template-free PDMS exhibits several distinct bandwidth regions each with different fractal dimensions significantly higher than 2, despite having the lowest roughness value. In contrast, most template-based PDMS shows an entire spatial frequency region that scales nearly with a single power factor corresponding to a fractal dimension as low as 2, despite slight increases in roughness values. Consequently, the surface temperature gradient and output performance of TENG increased, following the trend of fractal dimension and roughness, but the surface potentials have remained almost invariant. However, excessive increases in the surface roughness cause the spatial frequency to be divided once again into several different bandwidth regions with different cutoffs and higher fractal dimensions. These results suggest that the performance of TENG can be controlled by tuning both surface roughness and self-affine properties over multiple scales. Specifically, adhesive interaction becomes dominant on surfaces with lower fractality, enhancing TENG performance due to the expanded contact area. This study sheds light on the relationship among triboelectricity, thermal dissipation, and topography.

Cascaded partitioned phase modulation for cross-connection of orbital angular momentum mode and polarization multiplexing channels.

Multi-dimensional orbital angular momentum (OAM) mode multiplexing provides a promising route for enlarging communication capacity and establishing comprehensive networks. While multi-dimensional multiplexing has gained advancements, the cross-connection of these multiplexed channels, especially involving modes and polarizations, remains challenging due to the needs for multi-mode interconversion and on-demand polarization control. Herein, we propose an OAM mode-polarization cross-transformation solution via cascaded partitioned phase modulation, which enables the divergently separated OAM modes to be independently phase-imposed within distinct spatial regions, leading to the synergistic conversion operation of mode and polarization channels. In demonstrations, we implemented the cross-connection of three OAM modes and two polarization multiplexed channels, achieving the mode purity that exceeds 0.951 and polarization contrast up to 0.947. The measured mode insertion losses and polarization conversion losses are below 3.42 and 3.54 dB, respectively. Consequently, 1.2 Tbit/s quadrature phase shift keying signals were successfully exchanged, yielding the bit-error-rates close to 10-6. Incorporating with increased partitioned phase treatments, this approach shows promise in accommodating massive mode-polarization multiplexed channels, which hold the potential to augment networking capability of large-scale OAM mode multiplexing communication networks.

Topo‐MSJ: Search for groups of POIs with qualitative processing of spatial regions

Topo‐<scp>MSJ</scp>: Search for groups of <scp>POIs</scp> with qualitative processing of spatial regions

Advances in Automated Pigment Mapping for 15th-Century Manuscript Illuminations Using 1-D Convolutional Neural Networks and Hyperspectral Reflectance Image Cubes

Reflectance imaging spectroscopy (RIS) is invaluable in mapping and identifying artists’ materials in paintings. The analysis of the RIS image cube first involves classifying the cube into spatial regions, each having a unique reflectance spectrum (endmember). Second, endmember spectra are analyzed for spectral features useful to identify the pigments present to create labeled classes. The analysis process for paintings remains semi-automated because of the complex diffuse reflectance spectra due to the use of intimate pigment mixtures and optically thin paint layers by the artist. As a result, even when a group of related paintings are analyzed, each RIS cube is analyzed individually, which is time consuming. There is a need for new approaches to more efficiently analyze RIS cubes of related paintings to address the growing interest in the study of related paintings within a group of artists or artistic schools. This work builds upon prior investigations of 1-D spectral convolutional neural networks (CNNs) to address this need in two ways. First, an expanded training set was used—ten illuminated manuscripts created by artists stylistically grouped under the notname “Master of the Cypresses” (15th century Seville, Spain). Second, two 1-D CNN models were trained from the RIS cubes: reflectance and the first derivative. The results showed that the first derivative-trained CNN generally performed better than the reflectance-trained CNN in creating accurate labeled material maps for these illuminated manuscripts.

Exploration of diverse solutions for the calibration of imperfect climate models

Abstract. The calibration of Earth system model parameters is subject to data, time, and computational constraints. The high dimensionality of this calibration problem, combined with errors arising from model structural assumptions, makes it impossible to find model versions fully consistent with historical observations. Therefore, the potential for multiple plausible configurations presenting different trade-offs between skills in various variables and spatial regions remains usually untested. In this study, we lay out a formalism for making different assumptions about how ensemble variability in a perturbed physics ensemble relates to model error, proposing an empirical but practical solution for finding diverse near-optimal solutions. A meta-model is used to predict the outputs of a climate model reduced through principal component analysis. Then, a subset of input parameter values yielding results similar to a reference simulation is identified. We argue that the effective degrees of freedom in the model performance response to parameter input (the “parametric component”) are, in fact, relatively small, illustrating why manual calibration is often able to find near-optimal solutions. The results explore the potential for comparably performing parameter configurations that have different trade-offs in model errors. These model candidates can inform model development and could potentially lead to significantly different future climate evolution.

The 4.2 ka event is not remarkable in the context of Holocene climate variability

The “4.2 ka event” is a commonly described abrupt climate excursion that occurred about 4200 years ago. However, the extent to which this event is coherent across regional and larger scales is unclear. To objectively assess climate excursions in the Holocene we compile 1142 paleoclimate datasets that span all continents and oceans and include a wide variety of archive and proxy types. We analyze these data to determine the timing, significance and spatial imprint of climate excursions using an objective method that quantifies local, regional and global significance. Site-level excursions in temperature and hydroclimate are common throughout the Holocene, but significant global-scale excursions are rare. The most prominent excursion occurred 8200 years ago, when cold and dry conditions formed a large, significant excursion centered in the North Atlantic. We find additional significant excursions between 1600 and 1000 years ago, which agree with tree-ring data and annual-scale paleoclimate reconstructions, adding confidence and context to our findings. In contrast, although some datasets show significant climate excursions 4200 years ago, they do not occur in large, coherent spatial regions. Consequently, like most other periods in the Holocene, the “4.2 ka event” is not a globally significant climate excursion.

Abstract We024: Spatial Transcriptomics Identifies a Regeneration-Permissive Microenvironment in the Neonatal Mammalian Heart

Ischemic heart disease is the leading cause of death globally, largely driven by inability of the adult heart to restore tissue structure and function after injury. However, the neonatal mouse heart is able to regenerate up to one week after birth, circumventing scarring and functional impairment. Neonatal heart regeneration is a spatially and temporally regulated process, involving coordinated communication of different cardiac cell types. Our previous work identified a regenerative cardiomyocyte population (CM4) that proliferates after myocardial infarction (MI), but the intercellular communication that induces their regenerative response is not fully understood. To study the cellular environment of CM4 cells during neonatal heart regeneration, we profiled spatial transcriptomes of regenerative P1 hearts at 3 days and 7 days after MI and compared them to nonregenerative P8 hearts. We identified unique spatial regions with distinct gene expression signatures, that we refer to as “molecular niches”, in the ischemic and border zones (IZ, BZ) of the P1 heart post-MI compared to P8 hearts. Cell type deconvolution of molecular niches showed a distinct temporal change of fibroblast and macrophage abundance in the IZ of P1 hearts compared to P8 hearts and also showed the localization of CM4 cells to the BZ of P1 hearts at 7 days post-MI. To identify intercellular communication that contributes to the differential injury response in P1 and P8 hearts, we analyzed ligand-receptor pairs that were expressed at higher levels in the IZ and BZ of P1 compared to P8 hearts. Among the nearly 50 signaling pairs identified, OSM and SLIT signaling have been previously implicated in heart regeneration. Notably, many of these signaling factors, including cytokines, chemokines and growth factors, have receptors expressed in multiple cardiac cell types. Thus, ongoing studies are systematically distilling the effect of each secreted factor on individual cell types in the heart using a cardiac cell co-culture and multiplexed single-cell RNA sequencing approach. In summary, our study provides insight into the spatial organization and tissue microenvironment of neonatal heart regeneration and identifies a number of candidate signaling pathways that potentially mediate critical intercellular communication during neonatal heart regeneration.

Shock-Layer Measurements in T5 Shock Tunnel Hypersonic Flows Around a Cylinder Model

We report on near-body measurements of temperature and nitric oxide (NO) concentration in the hypersonic flows around a cylindrical test article in the Caltech T5 reflected shock tunnel. Flow measurements were made at 50 kHz using tunable diode laser absorption spectroscopy, deploying six lasers to probe an array of quantum state-specific transitions. Laser beams were positioned both in the freestream and behind the bow shock at specific locations deemed pertinent to computational fluid dynamics comparison and kinetic model evaluation. The fractions of laser beam pathlengths behind the shock in different spatial regions were also discerned, thus providing a measurement of shock location. This study consists of six total experiments (“shots”) across two Mach ∼5 conditions, characterized by total enthalpies of 8 and 16 MJ/kg and freestream velocities of 3.5 and 5 km/s, respectively. Freestream measurements generally concur with prior works in the T5, but with some non-trivial differences. Shock-layer measurements span from 2000 to 6000 K and feature noteworthy and expected variety among different zones within the post-shock region. Thermal equilibrium is generally held throughout the flowfield, but chemical nonequilibrium is commonly observed. NO is the primary spectroscopic target, but measurements of carbon monoxide, carbon dioxide, water, and atomic oxygen provide supplementary insights.

Quantitative Method for Calculating Spatial Release Region for Laser-Guided Bomb

Quantitative Method for Calculating Spatial Release Region for Laser-Guided Bomb

Complex network analysis of climate and landscape satellite data to explore spatio-temporal patterns in urban environment: the case of Athens

Motivated by the significance and complexity of exploring spatiotemporal patterns - regions within an urban environment, particularly in the context of extreme heat events- this research analyzes meteorological time series through complex network analysis. The data collected for the examination area is focused on Athens, Greece, and covers sections of the city’s urban landscape. The data was obtained from the Copernicus observation component of the European Union. Initially, the time series are transformed into networks using correlation network methodology, followed by examination of the discriminative capability of the topological measures of networks degree and modularity as community - region detection methods. Of particular interest is that our findings suggest that the proposed complex network analysis can lead to the extraction of spatial urban regions closely linked to land use and building heights in corresponding areas. These results may help investigate the spatial variability of heat in the urban environment and inform urban planning and management strategies in policy decision-making regarding the intensity of urban heat throughout the city and the planning of climate change adaptation strategies.