Similar Scale Research Articles

Digitizing the USPTO patent backfile

Abstract The digitization of the US Patent and Trademark Office’s (USPTO) backfile of six million patents undertaken between 1951 and 2001 was a five-decade struggle, featuring several media transitions from print and microfilm to CD-ROMs and, finally, the Web. This mass digitization project is on a similar scale to Google Books and the Internet Archive, but it is rarely discussed within critical digitization scholarship or for its significance as a tool for knowledge production. In this article, I focus on the USPTO’s patent document’s digital and physical material form and how the current paradigm of access and storage of the digital backfile emerged. Through this case study, I build upon Ian Milligan’s distinction between the ‘text’ and ‘platform’ layers of a digitization project to demonstrate how historical decisions regarding format and metadata continue to influence how users retrieve and interpret documents, such as patents, online.

A contrast in sea ice drift and deformation between winter and spring of 2019 in the Antarctic marginal ice zone

Abstract. Two ensembles of buoys, deployed in the marginal ice zone (MIZ) of the north-eastern Weddell Sea region of the Southern Ocean, are analysed to characterise the dynamics driving sea ice drift and deformation during the winter-growth and the spring-retreat seasons of 2019. The results show that although the two buoy arrays were deployed within the same region of ice-covered ocean, their trajectory patterns were vastly different. This indicates a varied response of sea ice in each season to the local winds and currents. Analyses of the winter data showed that the Antarctic Circumpolar Current modulated the drift near the sea ice edge. This led to a highly energetic and mobile ice cover, characterised by free-drift conditions. The resulting drift and deformation were primarily driven by large-scale atmospheric forcing, with negligible contributions due to the wind-forced inertial response. For this highly advective coupled ice–ocean system, ice drift and deformation linearly depended on atmospheric forcing. We also highlight the limits of commercial floating ice velocity profilers in this regime since they may bias the estimates of sea ice drift and the ice type detection. On the other hand, the spring drift was governed by the inertial response as increased air temperatures caused the ice cover to melt and break up, promoting a counterintuitively less wind-driven ice–ocean system that was more dominated by inertial oscillations. In fact, the deformation spectra indicate a strong decoupling to large-scale atmospheric forcing. Further analyses, extended to include the deformation datasets from different regions around Antarctica, indicate that, for similar spatial scales, the magnitude of deformation varies between seasons, regions, and the proximity to the sea ice edge and the coastline. This implies the need to develop rheology descriptions that are aware of the ice types in the different regions and seasons to better represent sea ice dynamics in the MIZ.

Forecasting influenza incidence as an ordinal variable using machine learning

Background: Many mechanisms contribute to the variation in the incidence of influenza disease, such as strain evolution, the waning of immunity and changes in social mixing. Although machine learning methods have been developed for forecasting, these methods are used less commonly in influenza forecasts than statistical and mechanistic models. In this study, we applied a relatively new machine learning method, Extreme Gradient Boosting (XGBoost), to ordinal country-level influenza disease data. Methods: We developed a machine learning forecasting framework by adopting the XGBoost algorithm and training it with surveillance data for over 32 countries between 2010 and 2018 from the World Health Organisation’s FluID platform. We then used the model to predict incidence 1- to 4-week ahead. We evaluated the performance of XGBoost forecast models by comparing them with a null model and a historical average model using mean-zero error (MZE) and macro-averaged mean absolute error (mMAE). Results: The XGBoost models were consistently more accurate than the null and historical models for all forecast time horizons. For 1-week ahead predictions across test sets, the mMAE of the XGBoost model with an extending training window was reduced by 78% on average compared to the null model. Although the mMAE increased with longer prediction horizons, XGBoost models showed a 62% reduction in mMAE compared to the null model for 4-week ahead predictions. Our results highlight the potential utility of machine learning methods in forecasting infectious disease incidence when that incidence is defined as an ordinal variable. In particular, the XGBoost model can be easily extended to include more features, thus capturing complex patterns and improving forecast accuracy. Conclusion: Given that many natural extreme phenomena are often described on an ordinal scale when informing planning and response, these results motivate further investigation of using similar scales for communicating risk from infectious diseases.

Substitution therapy for patients with alcohol dependence: Mechanisms of action and efficacy.

New approaches for the treatment of alcohol dependence (AD) may improve patient outcomes. Substitution maintenance therapy is one of the most effective treatment options for opioid and nicotine use disorders. So far, there has been little attention to substitution therapy for the treatment of AD. Here, we explain the mechanistic foundations of alcohol substitution maintenance therapy. Alcohol has many primary targets in the brain (and other organs) and the physical interaction of ethanol molecules with these specific ethanol-sensitive sites on a variety of ionotropic receptors (e.g. GABA-A, NMDA, and nicotinic acetylcholine (nACh) receptors) and ion channels provides the rationale for substitution. As such, a variety of compounds can interact with those ethanol-sensitive sites and can thus substitute for some of the effects of alcohol. For some of these compounds, alcohol discrimination studies have shown their substitution potential. Accordingly, potential substitution treatments include agonists acting at GABA receptors such as sodium oxybate, baclofen and benzodiazepines, NMDA receptor antagonists such as ketamine and memantine, or nAChRs agonists such as varenicline. All these compounds are already approved for other indications and we present clinical evidence for these drugs in the treatment of alcohol withdrawal syndrome (AWS) and in the long-term treatment of AD, and outline future steps for their acceptance as substitution treatment in AD. Finally, we discuss the substitution approach of managed alcohol programs for the most severely affected homeless populations. Results showed that sodium oxybate is probably the closest to a substitution therapy for AD and is already approved for the treatment of AWS and in the long-term treatment of AD in some countries. In conclusion, we argue that better AD treatment can be provided if substitution maintenance treatments for alcohol are implemented at a similar scale as for opioid and nicotine use disorder.

Multiscale Human Activity Recognition and Anticipation Network.

Deep convolutional neural networks have been leveraged to achieve huge improvements in video understanding and human activity recognition performance in the past decade. However, most existing methods focus on activities that have similar time scales, leaving the task of action recognition on multiscale human behaviors less explored. In this study, a two-stream multiscale human activity recognition and anticipation (MS-HARA) network is proposed, which is jointly optimized using a multitask learning method. The MS-HARA network fuses the two streams of the network using an efficient temporal-channel attention (TCA)-based fusion approach to improve the model's representational ability for both temporal and spatial features. We investigate the multiscale human activities from two basic categories, namely, midterm activities and long-term activities. The network is designed to function as part of a real-time processing framework to support interaction and mutual understanding between humans and intelligent machines. It achieves state-of-the-art results on several datasets for different tasks and different application domains. The midterm and long-term action recognition and anticipation performance, as well as the network fusion, are extensively tested to show the efficiency of the proposed network. The results show that the MS-HARA network can easily be extended to different application domains.

Convection boundary layers with transpiration: Non-similar solutions and scaling

We present a new formulation and novel scaling relations for natural convection boundary layers on horizontal surfaces with weak transpiration velocities Vi, for intermediate Schmidt numbers (or Prandtl numbers Pr) 1<Sc<20. Using characteristic scales derived from integral boundary layer equations, we define a new similarity variable η=(y/x)(Rex/Grx)1/4/Sc1/5 that has Vi within it, where Rex and Grx are the local Reynolds and Grashof numbers based on Vi and the concentration difference ΔC, respectively. Using η, and the dimensionless transpiration velocity ξ=(Rex/Grx1/5)5/4 as a non-similarity variable, we obtain novel, reduced, non-similar boundary layer equations. Numerical solution of these reduced boundary layer equations using a local non-similarity approach gives us the profiles of horizontal velocity and concentration, boundary layer thicknesses, wall shear stress, and mass flux as functions of Sc, for various ξ in the range ξ<1. Using normalizing functions, we then obtain scaling relations for these parameters; we observe extended similarity where the non-similarity parameter ξ itself appears in the obtained similarity scaling relations.

Training and Testing Texture Similarity Metrics for Structurally Lossless Compression.

We present a systematic approach for training and testing structural texture similarity metrics (STSIMs) so that they can be used to exploit texture redundancy for structurally lossless image compression. The training and testing is based on a set of image distortions that reflect the characteristics of the perturbations present in natural texture images. We conduct empirical studies to determine the perceived similarity scale across all pairs of original and distorted textures. We then introduce a data-driven approach for training the Mahalanobis formulation of STSIM based on the resulting annotated texture pairs. Experimental results demonstrate that training results in significant improvements in metric performance. We also show that the performance of the trained STSIM metrics is competitive with state of the art metrics based on convolutional neural networks, at substantially lower computational cost.

Activity-induced phase transition and coarsening dynamics in dry apolar active nematics.

Using the Lebwohl-Lasher interaction for reciprocal local alignment, we present a comprehensive phase diagram for a dry, apolar, active nematic system using its stochastic off-lattice dynamics. The nematic-isotropic transition in this system is first-order and occurs alongside a fluctuation-dominated phase separation. Our phase diagram identifies three distinct regions based on activity and orientational noise relative to alignment strength: a homogeneous isotropic phase, a nematic phase with giant density fluctuations, and a coexistence region. Using mean-field analysis and hydrodynamic theory, we demonstrate that reciprocal interactions lead to a density fluctuation-induced first-order transition and derive a phase boundary consistent with numerical results. Quenching from the isotropic to nematic phase reveals coarsening dynamics where nematic ordering precedes particle clustering. Both the nematic and density fields exhibit similar scaling behaviors, exhibiting dynamic exponents zS ≈ 2.5 and zρ ≈ 2.34, consistently falling within the range of 2 and 3.

Investigating the effect of relationships among studies in the context of research methodology on predicting the results of hypotheses

This study aims to investigate the effect of relationships among 25 studies conducted in Türkiye based on research methodology on predicting the results of the hypothesis regarding the effect of service quality dimensions on satisfaction. In the selected studies, the effects of tangibility, reliability, responsiveness, assurance and empathy, which are the dimensions of service quality, on satisfaction and the effects of service quality dimensions together on satisfaction were examined. UCINET 6.0 program was used for analysis. Google Scholar database was used to access the studies. Twenty-five articles conducted in Türkiye were selected. A separate content analysis was conducted for each study accessed. It was determined that the studies considered had too many relationships with each other depending on the sample size, and this negatively affected the prediction of acceptance or rejection of the hypotheses regarding the effect of tangibility on satisfaction. Considering the analysis technique, it was seen that most studies had a high relationship with each other and that this high relationship positively affected the acceptance or rejection prediction of the study hypotheses addressing the effect of tangibility on satisfaction. Whether or not similar sample sizes, similar analyses, and similar scales were used did not affect predicting the results of the hypotheses addressing the effect of reliability, responsiveness, assurance, and empathy on satisfaction.

Measurement technologies for pipeline transport of carbon dioxide-rich mixtures for CCS

Carbon capture and storage (CCS) is necessary to achieve the long-term temperature goals of the Paris Agreement. The mass of CO₂ that will be transported for permanent storage by 2050, to meet the International Energy Agency (IEA)'s Sustainable Development Scenario, is of a similar scale to the current global market of natural gas. Such trading levels require significant and expedited technological efforts. The urgency of the climate goals requires technological innovations to build from existing technologies operating in similar environments. The present work aims to be a stepping stone towards the realisation of the CCS business, which relies on accurate metrology and tight control systems. Here, various established technologies are screened, and for each principle, their possible niches of operation within CCS are identified. Eight technologies were shortlisted and assessed against selected benchmarking criteria. The most promising solutions for the various operative scenarios encompassing bulk flow measurement, structural flow identification, composition, and leak detection are described. All surveyed technologies require further experimental verification for one or more of the operating schemes throughout the CCS value chain. A roadmap to further explore the applicability of various measurement principles in CCS is also provided.

Impedance Spectroscopy with Microscopic Reference Electrodes as a Technique to Study Electrode Kinetics in Dye-Sensitized Solar Cells

Measurements with microscopic reference electrodes in electrochemical impedance spectroscopy (µRE-EIS) were used to study electrode kinetics of dye-sensitized solar cells (DSSCs). A detailed impedance analysis of DSSCs is essential to separately optimize different rate-determining steps in these cells. Two-electrode measurements as typically used, however, often do not allow a separate analysis of charge transfer reactions at different electrodes that occur with similar time constants. Such problems, e.g., arise in DSSCs with water-based electrolytes using 2,2,6,6-tretramethylpiperidine-1-oxyl (TEMPO) as a redox mediator. The use of a quasi-reference electrode offers the possibility to separately record impedance spectra of the photoanode and the counter electrode of a DSSC under operating conditions in one experiment. With this method, electron transfer kinetics could be studied for both electrodes individually and contributions of charge carrier recombination at the photoanode could be studied separately from the reduction of the redox mediator at the counter electrode.In DSSCs, the sensitized photoanode and the counter electrode are commonly separated by no more than 60 µm, depending on the thickness of the sealing foil. Into such an arrangement, a thin platinum wire was introduced as a third electrode, serving as a microscopic quasi-reference electrode (µRE). The architecture was adjusted stepwise and the influence of the adjustments was studied to ensure that important cell properties such as solar cell parameters and diffusion resistance remained comparable to cells in a typical two-electrode architecture.Subsequently, three-electrode experiments were carried out, using the photoanode as the working electrode (WE), a counter electrode (CE), and the µRE. For µRE-EIS experiments, the potential was modulated between CE and WE and the overall cell impedance Z 0 was determined. Simultaneously, the impedance between µRE and WE was recorded (Z 1) and the impedance between µRE and CE (Z 2) was calculated from Z 0 and Z 1. Thus, one experiment yielded spectra for the full cell and both respective half-cells WE vs. µRE and CE vs. µRE.Here, we investigated DSSCs based on TiO2 photoanodes sensitized with the organic dye Y123, aqueous electrolytes with the organic redox mediators TEMPO+/0 or OH-TEMPO+/0, and poly(3,4-ethylenedioxythiphene) (PEDOT) counter electrodes. The impedance spectra revealed that there are contributions in the same frequency range in both half-cell spectra, suggesting that both charge transfer processes from the electrodes to the oxidized redox species, i.e., recombination at the photoanode and the reduction of the redox mediators at the counter electrode, respectively, occur on similar time scales. These contributions superimpose in a typical two-electrode measurement and cannot be separated. µRE-EIS, however, proved to be a powerful tool to simultaneously study the interactions of novel redox mediators with photoanodes and counter electrode materials in detail. Figure 1

Investigating the Impedance of an Iron/Iron Redox Flow Battery at Different State of Charge Conditions – a Distribution of Relaxation Times Analysis

New energy storage technologies are needed to offset the erratic power supply of renewable energy sources due to the rising global energy demand and the urgency of climate change action. The Iron/Iron redox flow battery is a viable contender for large-scale energy storage, even if there are still issues with operational parameters and efficiency. Due to the positive electrode's moderate pH and low voltage, which preserves minimum toxicity and aggressivity, iron is a very affordable active material, making it feasible to develop inexpensive energy storage systems and batteries. The Electrochemical impedance spectroscopy technique was utilized to understand the degradation of the iron/iron redox flow battery during charging and discharging operation modes. However, discriminating between processes occurring at similar time scales is difficult since they overlap in the generally used Nyquist or Bode representation. Distribution of relaxation times (DRT) analysis tackles this issue by converting the EIS data with respect to the time constants of the individual processes. Individual processes such as electrochemical reactions inside the iron/iron redox flow cell, transport processes through porous structures, and iron ion transport were investigated.The impedance measurements were carried out to evaluate the ohmic resistance during the charging and discharging phases of the iron/iron redox flow battery with varied states of charge ranging from 0-100% while charging and 100-0% while discharging. In this series, a DC offset load of 100 mA in the charging and discharging directions was used to test the resistance differential associated with the battery's state of charge. In the charging phase of the battery of three DC offset load parameters, the iron ions were transported faster in the process while the state of charge of a battery increased at lower frequencies. The impedance increased when the state of charge decreased at a higher frequency range, involving the iron ions transported through the negative half-cell in an iron/iron redox flow battery. The overall ohmic resistance in the charging phase of the battery decreased up to 6 % until the battery was fully charged, but in discharging phase, the overall ohmic resistance increased to 2 %. This indicated that the DC offset load in the charging and discharging direction were dependent on the battery's state of charge. Figure 1

SiamFCA: A new fish single object tracking method based on siamese network with coordinate attention in aquaculture

In aquaculture, tracking fish helps to obtain information about their growth, behavior, location and health, etc. However, there are some problems in the process of fish tracking. Firstly, to solve the noise problem of underwater images, we use the contrast limited adaptive histogram equalization (CLAHE) to carry out local histogram equalization in each small block, which increases the image contrast while retaining more details. This operation enhances the contrast of the image while suppressing noise to solve the problem of noisy images. Then, to solve the problems of similarity interference, occlusion and scale variation among fish, we propose a new method for fish single object tracking in aquaculture environments, called SiamFCA, by combining the coordinated attention mechanism (CA). The method is based on the siamese network framework and uses a modified AlexNet to extract the depth features of image pairs. And through the coordinate attention mechanism, the model can learn the relationship between different objects more accurately, which can improve the accuracy and robustness of the model when dealing with complex image scenes. Finally, the correlation operation between classification and regression is performed based on the region proposal network (RPN). By classifying, region suggestions can be labeled as positive or negative samples, and regression can calibrate the position and size of the region proposal to match targets more accurately. The experimental results show that the precision and success rate of the algorithm are 84.2 % and 62.4 %, respectively, which are better than the tracking methods such as SiamRPN and DaSiamRPN, etc. The SiamFCA runs at 83 fps, which meets the demand of real-time fish tracking in aquaculture.

The new injury severity score underestimates true injury severity in a resource-constrained setting

BackgroundThe new injury severity score (NISS) is widely used within trauma outcomes research. NISS is a composite anatomic severity score derived from the Abbreviated Injury Scale (AIS) protocol. It has been postulated that NISS underestimates trauma severity in resource-constrained settings, which may contribute to erroneous research conclusions. We formally compare NISS to an expert panel's assessment of injury severity in South Africa. MethodsThis was a retrospective chart review of adult trauma patients seen in a tertiary trauma center. Randomly selected medical records were reviewed by an AIS-certified rater who assigned an AIS severity score for each anatomic injury. A panel of five South African trauma experts independently reviewed the same charts and assigned consensus severity scores using a similar scale for comparability. NISS was calculated as the sum of the squares of the three highest assigned severity scores per patient. The difference in average NISS between rater and expert panel was assessed using a multivariable linear mixed effects regression adjusted for patient demographics, injury mechanism and type. ResultsOf 49 patients with 190 anatomic injuries, the majority were male (n = 38), the average age was 36 (range 18–80), with either a penetrating (n = 23) or blunt (n = 26) injury, resulting in 4 deaths. Mean NISS was 16 (SD 15) for the AIS rater compared to 28 (SD 20) for the expert panel. Adjusted for potential confounders, AIS rater NISS was on average 11 points (95 % CI: 7, 15) lower than the expert panel NISS (p < 0.001). Injury type was an effect modifier, with the difference between the AIS rater and expert panel being greater in penetrating versus blunt injury (16 vs. 7; p = 0.04). Crush injury was not well-captured by AIS protocol. ConclusionNISS may under-estimate the ‘true’ injury severity in a middle-income country trauma hospital, particularly for patients with penetrating injury.

From leaves to the whole tree: Mistletoe effects on the productivity, water relations, and demography of a Neotropical savanna tree

AbstractTrees' responses to mistletoes occur at multiple organization levels (e.g., leaf, individual, population), yet integrating these multi‐scale responses is still challenging. Here, we compared the traits of infected versus uninfected trees over multiple scales, from leaf anatomy and physiology to canopy allometries and individual growth rate and survivorship. We tested the hypotheses that mistletoes lead in the host (1) the production of leaves with a conservative resource‐use strategy, (2) more mechanically stable canopies and (3) reductions in growth and survival probability in the trees they infect. We addressed these hypotheses in the widespread savanna tree Vochysia thyrsoidea and xylem‐tapping mistletoe Psittacanthus robustus in the Brazilian Cerrado, a global biodiversity hotspot. We found that (1) mistletoe infection did not affect key traits associated with resource conservativeness, such as leaf mass per area (LMA) and carbon assimilation rates (A). Likewise, (2) hosts did not increase the mechanical safety of their trunks in response to mistletoe infection since infected and uninfected trees had a similar allometric scaling between height and crown volume against stem diameter. (3) At the population level, both the relative growth rate and survival probability decreased as the number of parasites increased. However, zero growth and a 50% chance of mortality were estimated to occur in a minority of heavily infected trees (&gt;7 and 14 parasites, respectively). Our results challenge the idea that mistletoes have a ubiquitous negative impact on their hosts. We highlight, therefore, the need for clarifying the mechanisms that allow trees to maintain their functioning even in the face of mistletoe parasitism.

Flow dynamics as Froude‐supercritical turbidity currents encounter metre‐scale slope minibasin topography

AbstractSeafloor topography can affect turbidity current dynamics on deep‐water slopes, significantly influencing the dispersal of sediment. Despite the common occurrence of topographic complexity, there are few detailed investigations of topographic interactions and their effect on downslope flow evolution in intraslope environments. In this study, the sedimentology and architecture of an Upper Cretaceous intraslope fan succession deposited within an extensional, fault‐bound minibasin are described from a rare, well‐exposed, near‐continuous, oblique depositional‐dip outcrop of the Tres Pasos Formation, Chile. The 2 to 8 m thick studied interval transitions downslope from high‐energy heterolithic strata, including metre‐scale steep‐faced scours, to non‐amalgamated thick‐bedded sandstones. Abrupt increases in sandstone percentage, sandstone bed thickness and grain size occur on the hangingwall blocks of south‐east and north‐east‐dipping normal faults that bound the minibasin. Sandstone beds are dominated by backset or wavy low‐angle stratification proximally, contain compositional banding near faults, and are characterised by increased proportions of planar laminated and structureless turbidite divisions downslope along the transect. Experimental observations of turbidity current interactions with topography are synthesised into a qualitative framework, which is used to interpret flow processes and characteristics from deposit trends. The results reconstruct the response of Froude‐supercritical, stratified turbidity currents with denser basal layers when encountering metre‐scale fault scarps. The analysis shows that metre‐scale topographic features can substantially alter the flow properties of stratified turbidity currents, and their downslope flow evolution to include the development of transitional, depositional and flow‐stripped sediment gravity currents. However, in comparison to base‐of‐slope settings, overall flow conditions are interpreted to be more uniform over slope breaks and zones of flow expansion in a partially confined intraslope environment. These findings have considerable implications for understanding flow response to similar scale morphological features on the seafloor and the potential for flow transformations in intraslope settings.

Chemical extraction of chitin from American lobster (Homarus americanus) shells optimized through response surface methodology

Chitin extraction from the shells of American lobsters (Homarus americanus) was optimized through the use of response surface methodology (RSM). The demineralization step was optimized to minimize the ash content of shell samples and the deproteination step was optimized to minimize the protein content of the chitin product. At a laboratory scale, one set of optimized conditions for the demineralization step was 7.35 % w/w acetic acid at a 40 mL/g of powdered lobster shell ratio for 15 min; this lowered the ash content from 39.62 % to 0.41 ± 0.08 %. A set of optimized conditions for the deproteination step at a similar scale was 4 % w/w sodium hydroxide at a 43 mL/g demineralized shell ratio heated to 95 °C for 83 min. These conditions were indicated to entirely remove protein from the resultant chitin. Average yields under optimized conditions were 23.43 ± 1.75 % for demineralization and 30.33 ± 0.02 % for deproteination, though a demineralization reaction with larger biomass input had a higher yield at 40.31 %.

Unraveling the Mechanisms of P-Selectin in Sickle Cell Disease Using a Microvascular-on-a-Chip Model

Individuals who suffer from Sickle Cell Disease (SCD) is known to have multifaceted complications one such being vaso-occlusive crises, which result from blockages in their blood vessels. Recently, progress has been made to get drugs to the marketplace that address this issue. Several of these drugs target p-selectin, a transmembrane protein that facilitates the adhesion of platelets, leukocytes, and activated endothelial cells. P-selectin Glycoprotein Ligand-1 (PSGL-1), a P-selectin antagonist, has been extensively researched in vitro for its ability to mediate Microvascular obstruction through leukocyte interactions, particularly leukocyte rolling. Particularly, in human patients rather than animal models, the precise role of P-selectin in the interactions between activated platelets and endothelial cells remains unclear. This study delves into this interaction using a 3D vein-on-a-chip model that can capture microvascular obstruction formation at a similar scale to the microvascular. Our data shows that P Selectin in SCD inhibition is the attenuation mainly driven by platelets with contributions from WBC rather than driven by the activated endothelium. Through the creation of a PDMS branching 32-channel microfluidic, the cell interactions and obstruction of the microvascular can be quantified. Additionally, this model serves as an analog to test the various intercellular PSGL-1 interactions. Endothelial cells are seeded into the channels and cultured under flow to create 3D structures. Once confluent, healthy, and SCD patient blood samples collected in sodium citrate can be recalcified and perfused into the endothelial network to simulate physiological conditions then endothelial activation markers like P Selectin can be tagged, imaged, and quantified. Microvascular obstruction can be imaged in live time and quantified primarily through area and accumulation. Finally, various PSGL-1 analogs can be doped into the blood before and after perfusion to simulate prophylaxis or treatment. This system allows for the study of PSGL-1's effect on the microvascular obstruction process and its effect on individual cell types through tagging [Figure 1A]. When investigating PSGL1 analogs, the system does indicate a significant reduction in microvascular obstruction, quantified by the amount of occlusion within the channels [Figure 1B &amp; 1C]. With a cohort of n=10 SS patients on Hydroxyurea, there is a downstream effect on the amount of occlusion, specifically the percentage of the area of the channel that is occluded. Each patient is compared against itself to serve as its control, and each patient is perfused into three near-identical chips of treated and three untreated [Figure 1D]. When targeting CD41 and CD45 within the model, occlusions seem to be primarily driven by activated platelets, an indication that PSGL-1's interactions may be more involved in platelets than leukocyte dynamics. Work is ongoing to target this specific interaction. PSGL1's interaction with the activated endothelium is driving microvascular obstruction to be dominated by activated platelet leukocyte interactions. Current data shows that attenuation of P Selectin decreases vascular obstruction. PSGL-1 cell interactions and PSGL-1's effect on occlusion mitigation are quantified. By quantifying PSGL-1's interactions through a vein-on-a-chip model, SCD patient-specific drug interactions can be identified. Additionally, a greater understanding of how these drugs affect vaso-occlusive crisis and SCD.

Anisotropic molecular diffusion in confinement II: A model for structurally complex particles applied to transport in thin ionic liquid films

HypothesisDiffusion in confinement is an important fundamental problem with significant implications for applications of supported liquid phases. However, resolving the spatially dependent diffusion coefficient, parallel and perpendicular to interfaces, has been a standing issue and for objects of nanometric size, which structurally fluctuate on a similar time scale as they diffuse, no methodology has been established so far. We hypothesise that the complex, coupled dynamics can be captured and analysed by using a model built on the 2-dimensional Smoluchowski equation and systematic coarse-graining. Methods and simulationsFor large, flexible species, a universal approach is offered that does not make any assumptions about the separation of time scales between translation and other degrees of freedom. The method is validated on Molecular Dynamics simulations of bulk systems of a family of ionic liquids with increasing cation sizes where internal degrees of freedom have little to major effects. FindingsAfter validation on bulk liquids, where we provide an interpretation of two diffusion constants for each species found experimentally, we clearly demonstrate the anisotropic nature of diffusion coefficients at interfaces. Spatial variations in the diffusivities relate to interface-induced structuring of the ionic liquids. Notably, the length scales in strongly confined ionic liquids vary consistently but differently at the solid–liquid and liquid–vapour interfaces.

Edge-guided two-stage feature matching for infrared and visible image registration in electric power scenes

Collaborative processing of infrared and visible (IR–VS) images is essential in identifying potential equipment failures during power inspections. However, due to significant differences in spectrum, resolution, and intensity between IR–VS images, it is still challenging to obtain an accurate spatial registration for them. To address these issues, we propose the edge-guided two-stage registration strategy for various kinds of real-world applications. We first develop a heated, hollow calibration board tailored to IR–VS cameras, thus conducting offline calibration to estimate the scale and resolution relationships. The IR–VS images are pretransformed into a similar scale to improve registration accuracy. Furthermore, we propose the edge-guided feature matching network (EGFMNet) to extract feature points and build associated descriptors end-to-end. Specifically, benefiting from the excellent description ability of the network for image intensity changes, we directly adopt multimodal edge maps to learn more consistent features for accurate registration. Meanwhile, the local–global consistent features from edge maps are extracted to eliminate the nonlinear radiation distortion. By jointly learning the repeatability and reliability information, robust feature points with distinctive descriptors can be extracted and matched for homography estimation. Comparative and ablation experiments demonstrate the effectiveness and practicality of the proposed registration strategy in real-world power inspection scenarios. Extended experiments on several public datasets further demonstrate the superiority of the proposed method.