Temporal Images Research Articles

A three-dimensional left atrial motion estimation from retrospective gated computed tomography: application in heart failure patients with atrial fibrillation.

A reduced left atrial (LA) strain correlates with the presence of atrial fibrillation (AF). Conventional atrial strain analysis uses two-dimensional (2D) imaging, which is, however, limited by atrial foreshortening and an underestimation of through-plane motion. Retrospective gated computed tomography (RGCT) produces high-fidelity three-dimensional (3D) images of the cardiac anatomy throughout the cardiac cycle that can be used for estimating 3D mechanics. Its feasibility for LA strain measurement, however, is understudied. The aim of this study is to develop and apply a novel workflow to estimate 3D LA motion and calculate the strain from RGCT imaging. The utility of global and regional strains to separate heart failure in patients with reduced ejection fraction (HFrEF) with and without AF is investigated. A cohort of 30 HFrEF patients with (n = 9) and without (n = 21) AF underwent RGCT prior to cardiac resynchronisation therapy. The temporal sparse free form deformation image registration method was optimised for LA feature tracking in RGCT images and used to estimate 3D LA endocardial motion. The area and fibre reservoir strains were calculated over the LA body. Universal atrial coordinates and a human atrial fibre atlas enabled the regional strain calculation and the fibre strain calculation along the local myofibre orientation, respectively. It was found that global reservoir strains were significantly reduced in the HFrEF + AF group patients compared with the HFrEF-only group patients (area strain: 11.2 ± 4.8% vs. 25.3 ± 12.6%, P = 0.001; fibre strain: 4.5 ± 2.0% vs. 15.2 ± 8.8%, P = 0.001), with HFrEF + AF patients having a greater regional reservoir strain dyssynchrony. All regional reservoir strains were reduced in the HFrEF + AF patient group, in whom the inferior wall strains exhibited the most significant differences. The global reservoir fibre strain and LA volume + posterior wall reservoir fibre strain exceeded LA volume alone and 2D global longitudinal strain (GLS) for AF classification (area-under-the-curve: global reservoir fibre strain: 0.94 ± 0.02, LA volume + posterior wall reservoir fibre strain: 0.95 ± 0.02, LA volume: 0.89 ± 0.03, 2D GLS: 0.90 ± 0.03). RGCT enables 3D LA motion estimation and strain calculation that outperforms 2D strain metrics and LA enlargement for AF classification. Differences in regional LA strain could reflect regional myocardial properties such as atrial fibrosis burden.

Temporal analysis of the state of the Gbele Resource Reserve in the Upper West Region, Ghana

IntroductionThis paper assessed the changes in the forest cover of the Gbele Resource Reserve from 1990 to 2020. This provides a basis for strengthening management decisions to protect the resources in the Gbele Resource Reserve effectively.MethodsLandsat images for 1990, 2000, 2010 and 2020 were obtained from the United States Geological Service site. They were processed and classified in the System for Earth Observation Data Access, Processing, & Analysis for Land Monitoring (SEPAL), a web-based cloud computing platform. The accuracy of the images was assessed using 50 ground-truth points obtained from the 3-5 meter spatial and near-daily temporal resolution planet satellite images from Norway’s International Climate and Forest Initiative (NICFI). Post classification change detection was used to analyse the changes in land cover from 1990 to 2000, 2000 to 2010 and from 2010 to 2020.ResultsThe analysis revealed that the total forest area was 55273.2 ha. In 1990, 74.9 % of the reserve was open forest and 24.6 % was shrubs/grass. The open forest declined to 65.8 % in 2000 and further to 62.4 % in 2010 while the shrubs/grass cover increased to 35.7 in 2010. As of 2020, the forest increased to 73.6 % while the shrub/grass cover declined to 25.8 %.DiscussionThese changes could be attributed partly to widespread charcoal production in the fringe districts and rose wood harvesting in the early 2000s. charcoal production and rosewood logging have been livelihood sources for fringe communities. The ban on the harvest and exportation of rosewood after 2010 could partly explain the sharp increase in the open forest cover from 2010 to 2020. The changes in the extent of the reserve from 1990 to 2020 revealed that the reserve can vulnerable to excessive exploitation and can also be resilient if deliberate efforts are made to protect it. It is recommended that the fringe district and municipal Assemblies should strengthen the enforcement of the ban on the logging of the rosewood and trees in the reserve for the production of charcoal.

Crop Type Mapping and Winter Wheat Yield Prediction Utilizing Sentinel-2: A Case Study from Upper Thracian Lowland, Bulgaria

The aim of this study is to predict and map winter wheat yield in the Parvomay municipality, situated in the Upper Thracian Lowland of Bulgaria, utilizing satellite data from Sentinel-2. The main crops grown in the research area are winter wheat, rapeseed, sunflower, and maize. To distinguish winter wheat fields accurately, we evaluated classification methods such as Support Vector Machines (SVM) and Random Forest (RF). These methods were applied to satellite multispectral data acquired by the Sentinel-2 satellites during the growing season of 2020–2021. In accordance with their development cycles, temporal image composites were developed to identify suitable moments when each crop is most accurately distinguished from others. Ground truth data obtained from the integrated administration and control system (IACS) were used for training the classifiers and assessing the accuracy of the final maps. Winter wheat fields were masked using the crop mask created from the best-performing classification algorithm. Yields were predicted with regression models calibrated with in situ data collected in the Parvomay study area. Both SVM and RF algorithms performed well in classifying winter wheat fields, with SVM slightly outperforming RF. The produced crop maps enable the application of crop-specific yield models on a regional scale. The best predictor of yield was the green NDVI index (GNDVI) from the April monthly composite image.

SEEK+: Securing vehicle GPS via a sequential dashcam-based vehicle localization framework

Nowadays, the Global Positioning System (GPS) plays an critical role in providing navigational services for transportation and a variety of other location-dependent applications. However, the emergent threat of GPS spoofing attacks compromises the safety and reliability of these systems. In response, this study introduces a cutting-edge computer vision-based methodology, the SEquential dashcam-based vEhicle localization frameworK Plus (SEEK+), designed to counteract GPS spoofing. By analyzing dashcam footage to ascertain a vehicle’s actual location, SEEK+ scrutinizes the authenticity of reported GPS data, effectively identifying spoofing incidents. The application of dashcam imagery for localization, however, presents inherent obstacles, such as adverse lighting and weather conditions, seasonal and temporal image variations, obstructions within the camera’s field of view, and fluctuating vehicle velocities. To overcome these issues, SEEK+ integrates innovative strategies within its framework, demonstrating superior efficacy over existing approaches with a notable detection accuracy rate of up to 94%.

Pore-scale physics of ice melting within unconsolidated porous media revealed by non-destructive magnetic resonance characterization

Melting of ice in porous media widely exists in energy and environment applications as well as extraterrestrial water resource utilization. In order to characterize the ice-water phase transition within complicated opaque porous media, we employ the nuclear magnetic resonance (NMR) and imaging (MRI) approaches. Transient distributions of transverse relaxation time T2 from NMR enable us to reveal the substantial role of inherent throat and pore confinements in ice melting among porous media. More importantly, the increase in minimum T2 provides new findings on how the confinement between ice crystal and particle surface evolves inside the pore. For porous media with negligible gravity effect, both the changes in NMR-determined melting rate and our theoretical analysis of melting front confirm that conduction is the dominant heat transfer mode. The evolution of mushy melting front and 3D spatial distribution of water content are directly visualized by a stack of temporal cross-section images from MRI, in consistency with the corresponding NMR results. For heterogeneous porous media like lunar regolith simulant, the T2 distribution shows two distinct pore size distributions with different pore-scale melting dynamics, and its maximum T2 keeps increasing till the end of melting process instead of reaching steady in homogeneous porous media.

Темпоральные образы в путешествиях «массового сентиментализма»: от личного к идиллическому

The article analyzes the system of temporal images in the travel texts of “mass sentimentalism” (using the works by V. V. Izmailov, P. I. Shalikov and P. I. Sumarokov as an example) in the context of the paradigm “social – cultural – artistic time”. The aim of the research is to substantiate the thesis about the image of time as a characteristic of the way the subject perceives a certain event / chain of events in quasi-historical retrospect, in connection with which three levels of perceptual time (time of the subject / personal time; historical time, idyllic time) were identified and defined. The scientific novelty lies in determining the ways of expressing these components in the travelogues of “mass sentimentalism”, which have not been studied in this aspect before, adding to the ideas about the specifics of the literary process in Russia at the turn of the 18th and 19th centuries, including the explication of temporal representations in travel prose. It is concluded that the image of the epoch in the travels of “mass sentimentalism” is reconstructed on the basis of spatial impressions. The core of the temporal-image structure is personal time (in the dynamics of private impressions, including accurately recorded ones), while the system of images of various historical epochs (from Kievan Rus’ to the 18th century) and variant idyllic constructions form the periphery.

MTFN: multi-temporal feature fusing network with co-attention for DCE-MRI synthesis.

Dynamic Contrast Enhanced Magnetic Resonance Imaging (DCE-MRI) plays an important role in the diagnosis and treatment of breast cancer. However, obtaining complete eight temporal images of DCE-MRI requires a long scanning time, which causes patients' discomfort in the scanning process. Therefore, to reduce the time, the multi temporal feature fusing neural network with Co-attention (MTFN) is proposed to generate the eighth temporal images of DCE-MRI, which enables the acquisition of DCE-MRI images without scanning. In order to reduce the time, multi-temporal feature fusion cooperative attention mechanism neural network (MTFN) is proposed to generate the eighth temporal images of DCE-MRI, which enables DCE-MRI image acquisition without scanning. In this paper, we propose multi temporal feature fusing neural network with Co-attention (MTFN) for DCE-MRI Synthesis, in which the Co-attention module can fully fuse the features of the first and third temporal image to obtain the hybrid features. The Co-attention explore long-range dependencies, not just relationships between pixels. Therefore, the hybrid features are more helpful to generate the eighth temporal images. We conduct experiments on the private breast DCE-MRI dataset from hospitals and the multi modal Brain Tumor Segmentation Challenge2018 dataset (BraTs2018). Compared with existing methods, the experimental results of our method show the improvement and our method can generate more realistic images. In the meanwhile, we also use synthetic images to classify the molecular typing of breast cancer that the accuracy on the original eighth time-series images and the generated images are 89.53% and 92.46%, which have been improved by about 3%, and the classification results verify the practicability of the synthetic images. The results of subjective evaluation and objective image quality evaluation indicators show the effectiveness of our method, which can obtain comprehensive and useful information. The improvement of classification accuracy proves that the images generated by our method are practical.

Temporal CT Evaluation of the Relationships between Basic Anatomical Structures and the Round Window: Importance for the Cochlear Implant Surgery

Abstract Objectives In the present study, we investigated the round window (RW) and neighboring anatomical structures using temporal computed tomography (CT) which are important for cochlear implant (CI) electrodes. Methods In this retrospective study, the temporal CT images of 112 adult patients (45 males and 67 females) were evaluated. We classified mastoid pneumatization, and measured RW diameter, RW–carotid canal (CC) distance, RW–facial nerve mastoid segment (FNMS) distance, RW–pyramidal eminence distance, RW–jugular bulb (JB) distance, and RW–internal acoustic canal (IAC) distance. Additionally, RW–cochlea angle and RW–facial nerve angle were also measured. Results RW diameters in males were significantly higher than those in females bilaterally (p < 0.05). RW–CC distance and RW–JB distance were both smaller than 10 mm. RW–IAC distance was 2.54 to 2.68 mm, and RW–FNMS distance was 4.20 to 4.40 mm. RW–cochlea angle ranged from 39.62 to 41.91 degrees and RW–FN angle ranged from 17.28 to 18.40 degrees. Males showed better mastoid pneumatization values (p < 0.05). In higher RW diameters, RW–JB distance decreased, and in pneumatized mastoids, RW–JB distance increased. RW–JB distance and RW–CC distance were detected to increase together (p < 0.05). Conclusion RW is crucial anatomic structure for CI surgeries. RW diameters are between 1.21 and 1.35 mm and lower in the females. Males exhibited better mastoid pneumatization values than the females, and CC and JB distances from RW were farther in well-pneumatized mastoids. Future studies should include comprehensive clinical and surgical findings.

Ciliary muscle and anterior segment characteristics in pre-presbyopic adults with Down syndrome.

Previous research has shown that accommodation deficits are common in individuals with Down syndrome (DS), but the origin and mechanisms behind these deficits are still unknown. The aim of this study was to investigate the characteristics of different ocular structures involved in accommodation, in particular the ciliary muscle (CM), in a population of individuals with DS to further understand this deficit and its mechanisms. Thirty-two volunteer participants of pre-presbyopic age with (n = 16) and without DS (n = 16) were recruited. Temporal and nasal images of the CM were acquired using anterior segment optical coherence tomography (AS-OCT) while participants fixated an eccentrically located target. Analysis of CM parameters was undertaken using validated semi-automated software. Axial length, anterior chamber depth, lens thickness and corneal curvature were obtained with the Topcon Aladdin Optical Biometer and Corneal Topographer. Non-cycloplegic refractive error and accommodative ability were obtained with an open-field autorefractor and dynamic retinoscopy, respectively. Independent t-tests were conducted to determine differences in CM and other anterior segment parameters between participants with and without DS. No significant differences were found in the CM parameters studied between participants with and without DS (p > 0.05). In contrast, significant differences were found in visual acuity (p < 0.001), accommodative response (p < 0.001) and corneal curvature (K1 p = 0.003 and K2 p < 0.001) between participants with and without DS. Despite having poorer accommodation, pre-presbyopic adults with DS do not have a different CM morphology to that found in typically developing adults. These findings suggest that the accommodative deficit found in this population is not due to a mechanical deficit of the CM.

Multi-Temporal Sentinel-1 and Sentinel-2 Data for Orchards Discrimination in Khairpur District, Pakistan Using Spectral Separability Analysis and Machine Learning Classification

Generating orchards spatial distribution maps within a heterogeneous landscape is challenging and requires fine spatial and temporal resolution images. This study examines the effectiveness of Sentinel-1 (S1) and Sentinel-2 (S2) satellite data of relatively high spatial and temporal resolutions for discriminating major orchards in the Khairpur district of the Sindh province, Pakistan using machine learning methods such as random forest (RF) and a support vector machine. A Multicollinearity test (MCT) was performed among the multi-temporal S1 and S2 variables to remove those with high correlations. Six different feature combination schemes were tested, with the fusion of multi-temporal S1 and S2 (scheme-6) outperforming all other combination schemes. The spectral separability between orchards pairs was assessed using Jeffries-Matusita (JM) distance, revealing that orchard pairs were completely separable in the multi-temporal fusion of both sensors, especially the indistinguishable pair of dates-mango. The performance difference between RF and SVM was not significant, SVM showed a slightly higher accuracy, except for scheme-4 where RF performed better. This study concludes that multi-temporal fusion of S1 and S2 data, coupled with robust ML methods, offers a reliable approach for orchard classification. Prospectively, these findings will be helpful for orchard monitoring, improvement of yield estimation and precision based agricultural practices.

Multi-temporal change detection of asbestos roofing: A hybrid object-based deep learning framework with post-classification structure

The detection of changes in asbestos roofing is vital due to the significant health risks associated with asbestos exposure. Utilising remote sensing for this task is promising, yet it encounters challenges like misregistration and variations in illumination within temporal images. This study presents a novel object-based deep learning framework with a post-classification structure designed specifically to detect changes in asbestos roofing. The method employs a hybrid deep learning model that utilises DenseNet121 to extract spatial features and integrates with either LSTM (DenseNet-LSTM) or conv1D (DenseNet-Conv1D) to capture temporal features. Using VHR aerial imagery, image patch sequences were generated for each roofing object across seven temporal points. After training the model, labels were assigned to each image patch in the sequence through binary classification. In post-processing, sequence alignment based on Hamming distance was used to enhance label assignments by aligning them with a pre-determined set of valid label sequences. Finally, the temporal change map was generated by comparing label transitions across temporal points. The results highlighted the model's robust capability to address inconsistencies across temporal points without relying on preprocessing techniques to manage such temporal discrepancies. The DenseNet-LSTM and DenseNet-Conv1D models demonstrated comparable classification performance in asbestos roofing detection, achieving average overall accuracies of 95.8% and 96.0%, respectively. Furthermore, the model's generalisability was underscored by a test on an independent dataset, which produced up to 94% detection accuracy for asbestos class. While the framework has limitation in detecting painted asbestos roofing and partial changes, it offers a reproducible approach for change detection tasks that target a specific roofing type, rather than detecting changes in all roofing types within remote sensing imagery.

Perkembangan Lahan Terbangun Kota Pekanbaru Tahun 2000-2020

This research aims describe the development of built-up area and identify trends the development of built-up area in Pekanbaru City in 2000-2020. The approach in research is descriptive quantitative with stages of temporal landsat image interpretation, field verification, and spatial analysis. The study population is the entire pixel data in satellite imagery with sampling techniques for the image interpretation process. Supervised classification is used in the interpretation process, kappa index with 22 sample points is used to test the accuracy of the interpretation results, and map overlays are used in spatial analysis. The results of the analysis show that in the last two decades (2000-2020) the built-up area in Pekanbaru City increased by 13,45% (85,99 km2) with an average increase of 0.67% (4,30 km2) annually. Spatially, the distribution of development of built up area is unevenly, but forms a patterns and directions that tends to be the same, namely the pattern of spreading from the city center to the fringe area with development trends in the south and southwest following the main road route.

Super Resolution Dual-Energy Cone-Beam CT Imaging With Dual-Layer Flat-Panel Detector.

In flat-panel detector (FPD) based cone-beam computed tomography (CBCT) imaging, the native receptor array is usually binned into a smaller matrix size. By doing so, the signal readout speed could be increased by 4-9 times at the expense of a spatial resolution loss of 50%-67%. Clearly, such manipulation poses a key bottleneck in generating high spatial and high temporal resolution CBCT images at the same time. In addition, the conventional FPD is also difficult in generating dual-energy CBCT images. In this paper, we propose an innovative super resolution dual-energy CBCT imaging method, named as suRi, based on dual-layer FPD (DL-FPD) to overcome these aforementioned difficulties at once. With suRi, specifically, a 1D or 2D sub-pixel (half pixel in this study) shifted binning is applied instead of the conventionally aligned binning to double the spatial sampling rate during the dual-energy data acquisition. As a result, the suRi approach provides a new strategy to enable high spatial resolution CBCT imaging while at high readout speed. Moreover, a penalized likelihood material decomposition algorithm is developed to directly reconstruct the high resolution bases from these dual-energy CBCT projections containing sub-pixel shifts. Numerical and physical experiments are performed to validate this newly developed suRi method with phantoms and biological specimen. Results demonstrate that suRi can significantly improve the spatial resolution of the CBCT image. We believe this developed suRi method would greatly enhance the imaging performance of the DL-FPD based dual-energy CBCT systems in future.

Abstract A021: Evolutionary immunotherapy in NSCLC: An integrated approach

Abstract Purpose: Our study examines tumor growth dynamics and immunosuppression under the presence of immune attack to identify optimal immune pulsing treatment strategies. Motivation: Checkpoint inhibitors disrupting the PD-1/PD-L1 axis are used in multiple cancer subtypes. However, not all patients respond, and predictive biomarkers are needed to better stratify patients. Therapy for PD-L1, the most studied biomarker, has given mixed results in non-small cell lung cancer (NSCLC). The complexity of the immune response and plastic nature of PD-L1/PD-1 expression on tumor and immune cells contributes to the heterogeneity of success. IFN-γ, a cytokine released by activated immune cells, induces PD-L1 expression in some tumor cells. Chronic IFN-γ results in long-term PD-L1 expression. However, relaxation dynamics following removal of IFN-γ remains unexplored. In this study, we investigate these complex PD-L1 dynamics through a unique combination of in vitro studies and mathematical modeling. METHODS: In vitro experimental data: We have collected in vitro and temporal confocal microscopy images of PD-L1 expression on NSCLC cells, which were either untreated or treated for 48 hours with high dose IFN-γ followed by chronic low dose IFN-γ for 21 days.Agent-based model: To reinforce our in vitro experiments and findings, we developed a hybrid agent-based model (ABM) to study PD-L1 dynamics. The agents in the ABM are tumor cells having variable PD-L1 expression, and immune cells that secrete IFN-γ. Tumor cells can be randomly seeded or seeded as a cluster of cells. IFN-γ is modeled as either a binary well-mixed pulse (to simulate the in vitro experimental setup), or as a diffusible via a reaction-diffusion process (to simulate the tumor-immune cell interactions in an in-vivo spatial manner). This allows us to investigate the effects of various pulsing strategies of immunotherapy on tumor evolution where each pulse can be a variable dose of immune cells, given at regular or irregular intervals. RESULTS: Our in vitro experimental data and microscopy images confirm that PD-L1 expression continues to increase within the cells after a 48-hour IFN-γ exposure followed by low levels of IFN-γ. Our ABM has been calibrated to capture in vitro data dynamics for different conditions. Our initial results indicate that pulsing immunotherapy may lead to better overall immune efficacy than continuous immunotherapy, by preventing sustained suppression that increases immune cell exhaustion, despite the potential for tumor regrowth between pulses. We also hypothesize that pulsing immunotherapy will have different immunosuppression effects when tumors are clustered or randomly distributed. CONCLUSION: We have quantified the temporal dynamics of PD-L1 expression in NSCLC cells under different conditions of IFN-γ, and built an ABM to study tumor-immune evolution under various pulsing strategies. The results of this work could lead to improved therapy schedules in patients responsive to checkpoint inhibitors that exploit the complex dynamics of immune response and suppression. Citation Format: Sandhya Prabhakaran, Taylor M. Bursell, Kimberly Luddy, Julian Pineiro, Rafael Bravo, Jeffrey West, Megan Johnson, Mark Robertson-Tessi, Amer A. Beg, Jhanelle E. Gray, Scott Antonia, Robert A. Gatenby, Alexander R. A. Anderson. Evolutionary immunotherapy in NSCLC: An integrated approach [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Translating Cancer Evolution and Data Science: The Next Frontier; 2023 Dec 3-6; Boston, Massachusetts. Philadelphia (PA): AACR; Cancer Res 2024;84(3 Suppl_2):Abstract nr A021.

Retracted: Multisensor Data and Cross-Validation Technique for Merging Temporal Images for the Agricultural Performance Monitoring System

Retracted: Multisensor Data and Cross-Validation Technique for Merging Temporal Images for the Agricultural Performance Monitoring System

Long-term land cover changes assessment in the Jiului Valley mining basin in Romania

Introduction: Highlighting and assessing land cover changes in a heterogeneous landscape, such as those with surface mining activities, allows for understanding the dynamics and status of the analyzed area. This paper focuses on the long-term land cover changes in the Jiului Valley, the largest mining basin in Romania, using Landsat temporal image series from 1988 to 2017.Methods: The images were classified using the supervised Support Vector Machine (SVM) algorithm incorporating four kernel functions and two common algorithms (Maximum Likelihood Classification - MLC) and (Minimum Distance - MD). Seven major land cover classes have been identified: forest, pasture, agricultural land, built-up areas, mined areas, dump sites, and water bodies. The accuracy of every classification algorithm was evaluated through independent validation, and the differences in accuracy were subsequently analyzed. Using the best-performing SVM-RBF algorithm, classified maps of the study area were developed and used for assessing land cover changes by post-classification comparison (PCC).Results and discussions: All three algorithms displayed an overall accuracy, ranging from 76.56% to 90.68%. The SVM algorithms outperformed MLC by 4.87%–8.80% and MD by 6.82%–10.67%. During the studied period, changes occurred within analyzed classes, both directly and indirectly: forest, built-up areas, mined areas, and water bodies experienced increases, whereas pasture, agricultural land, and dump areas saw declines. The most notable changes between 1988 and 2017 were observed in built-up and dump areas: the built-up areas increased by 110.7%, while the dump sites decreased by 53.0%. The mined class showed an average growth of 6.5%. By highlighting and mapping long-term land cover changes in this area, along with their underlying causes, it became possible to analyze the impact of land management and usage on sustainable development and conservation effort over time.

4D Light-sheet Imaging of Zebrafish Cardiac Contraction.

Zebrafish is an intriguing model organism known for its remarkable cardiac regeneration capacity. Studying the contracting heart in vivo is essential for gaining insights into structural and functional changes in response to injuries. However, obtaining high-resolution and high-speed 4-dimensional (4D, 3D spatial + 1D temporal) images of the zebrafish heart to assess cardiac architecture and contractility remains challenging. In this context, an in-house light-sheet microscope (LSM) and customized computational analysis areused to overcome these technical limitations. This strategy, involving LSM system construction, retrospective synchronization, single cell tracking, and user-directed analysis, enables one to investigate the micro-structure and contractile function across the entire heart at the single-cell resolution in the transgenic Tg(myl7:nucGFP) zebrafish larvae. Additionally, we are able to further incorporate microinjection of small molecule compounds to induce cardiac injury in a precise and controlled manner. Overall, this framework allows one to track physiological and pathophysiological changes, as well as the regional mechanics at the single-cell level during cardiac morphogenesis and regeneration.

The Implication of Urban Spatial Expansion on the Socio-Economic Development in Obala, Cameroon

The rapid population increase in world cities, not excluding Africa and sub-Saharan African cities has had a considerable effect both on the space occupation and on the inhabitants themselves. Cameroon in general and Obala town in particular have witnessed a rapid increase in their population. Such a rapid increase has opened flood gates for land grabbing, creating problems including inadequate electricity supply, the absence of portable water, insecurity, and anarchy in construction. It is in furtherance of these issues that this paper seeks to examine the trend of urban spatial expansion and its implications for socio-economic development in the town of Obala. The research made use of secondary source data (published articles, reports, and theses). Primary source data was sourced from the administration of 116 questionnaires, some of which selected 13 quarters of the Obala urban space. Interviews and focus group discussions were organised with the objective of gathering information in Obala. To determine the rate of urban spatial expansion, a time series analysis of the years 1987, 2004, and 2020 was conducted with the use of multiple temporal Landsat satellite images. The result reveals that migration (immigration), the rehabilitation of the National Road, and the searches for employment opportunities have accelerated the urban spatial expansion in Obala. That is why urban space increased from 7.01% in 1987 to 9.01% in 2004, and in 2020, it hit 16.9%. In the social domain, the extension of electric poles and the construction of health centres and educational facilities have moved forward. In the economic domain, the emergence of commercial institutions (banks), market installations, and an increase in investment rates have increased the magnitude of economic activities in Obala.

Analysis of the photographers' quality control in the diabetic retinopathy screening at the IOBA's Reading Center

Aims/Purpose: Diabetic retinopathy (DR) is the most frequent ocular complication of diabetes and one of the leading causes of avoidable blindness. Teleophthalmology screening by taken retinal images is an effective tool in its early detection and delivering prompt treatment. IOBA's Reading Center (RC) of the University of Valladolid is in charge for the Regional Health System (SACYL) DR screening program. Retinographies are taken at Primary Care Centers, under mydriasis, following the three‐field Joslin Vision Network protocol by certified photographers. Quality control (QC) is an indispensable process in a RC to ensure an accurate retinography reading. An upper limit of 5% of photographs taken with low quality has been set as acceptable. This study aimed to analyse the quality of the retinographies taken in the DR screening at the IOBA's RC during 2021 and 2022.Methods: A retrospective study was designed analysing the quality outcomes of DR screening. Certified in image reading Optometrists determined if images received at the RC had enough quality according to seven factors (halo &gt;1/3, inadequate vessels/parenchyma sharpness, inadequate central, nasal, temporal or fundus reflex image and inadequate disk sharpness). Statistical analysis was performed using SPSS 26 (Chicago, USA). Chi‐square test was used to compare frequencies between 2021 and 2022 outcomes.Results: 4474 retinographies were taken during 2021 and 6345 during 2022. 14 photographers took part in the program (8 in both years, 2 left in 2021, and 4 joined in 2022). All analysed factors showed less than 3% of incorrect images. 6.1% of images showed at least one error in 2021 and 4.4% in 2022 (p &lt; 0.01). Every analysed factor showed a significant improvement (p &lt; 0.02) between 2021 and 2022 except for disk sharpness in both eyes (p &gt; 0.05).Conclusions: The quality of the images of the IOBA RC is adequate for the SACYL DR screening program showing values below the fixed upper limit in all the analysed factors with a significant decrease in almost all values that can be attributed to increased experience of capture personnel.

ULM-MbCNRT: In vivo Ultrafast Ultrasound Localization Microscopy by Combining Multi-branch CNN and Recursive Transformer.

Ultrasound localization microscopy (ULM) overcomes the acoustic diffraction limit by localizing tiny microbubbles (MBs), thus enabling the microvascular to be rendered at sub-wavelength resolution. Nevertheless, to obtain such superior spatial resolution, it is necessary to spend tens of seconds gathering numerous ultrasound (US) frames to accumulate MB events required, resulting in ULM imaging still suffering from trade-offs between imaging quality, data acquisition time and data processing speed. In this paper, we present a new deep learning (DL) framework combining multi-branch CNN and recursive Transformer, termed as ULM-MbCNRT, that is capable of reconstructing a super-resolution image directly from a temporal mean low-resolution image generated by averaging much fewer raw US frames, i.e., implement an ultrafast ULM imaging. To evaluate the performance of ULM-MbCNRT, a series of numerical simulations and in vivo experiments are carried out. Numerical simulation results indicate that ULM-MbCNRT achieves high-quality ULM imaging with ~10-fold reduction in data acquisition time and ~130-fold reduction in computation time compared to the previous DL method (e.g., the modified sub-pixel convolutional neural network, ULM-mSPCN). For the in vivo experiments, when comparing to the ULM-mSPCN, ULM-MbCNRT allows ~37-fold reduction in data acquisition time (~0.8 s) and ~2134-fold reduction in computation time (~0.87 s) without sacrificing spatial resolution. It implies that ultrafast ULM imaging holds promise for observing rapid biological activity in vivo, potentially improving the diagnosis and monitoring of clinical conditions.