Binary Map Research Articles

A New SDM-Based Approach for Assessing Climate Change Effects on Plant–Pollinator Networks

Current methods for studying the effects of climate change on plants and pollinators can be grouped into two main categories. The first category involves using species distribution models (SDMs) to generate habitat suitability maps, followed by applying climate change scenarios to predict the future distribution of plants and pollinators separately. The second category involves constructing interaction matrices between plants and pollinators and then either randomly removing species or selectively removing generalist or specialist species, as a way to estimate how climate change might affect the plant–pollinator network. The primary limitation of the first approach is that it examines plant and pollinator distributions separately, without considering their interactions within the context of a pollination network. The main weakness of the second approach is that it does not accurately predict climate change impacts, as it arbitrarily selects species to remove without knowing which species will truly shift, decline, or increase in distribution due to climate change. Therefore, a new approach is needed to bridge the gap between these two methods while avoiding their specific limitations. In this context, we introduced an innovative approach that first requires the creation of binary climate suitability maps for plants and pollinators, based on SDMs, for both the current and future periods. This step aligns with the first category of methods mentioned earlier. To assess the effects of climate change within a network framework, we consider species co-overlapping in a geographic matrix. For this purpose, we developed a Python program that overlays the binary distribution maps of plants and pollinators, generating interaction matrices. These matrices represent potential plant–pollinator interactions, with a ‘0’ indicating no overlap and a ‘1’ where both species coincide in the same cell. As a result, for each cell within the study area, we can construct interaction matrices for both the present and future periods. This means that for each cell, we can analyze at least two pollination networks based on species co-overlap. By comparing the topology of these matrices over time, we can infer how climate change might affect plant–pollinator interactions at a fine spatial scale. We applied our methodology to Chile as a case study, generating climate suitability maps for 187 plant species and 171 pollinator species, resulting in 2906 pollination networks. We then evaluated how climate change could affect the network topology across Chile on a cell-by-cell basis. Our findings indicated that the primary effect of climate change on pollination networks is likely to manifest more significantly through network extinctions, rather than major changes in network topology.

Damage Scene Change Detection Based on Infrared Polarization Imaging and Fast-PCANet

Change detection based on optical image processing plays a crucial role in the field of damage assessment. Although existing damage scene change detection methods have achieved some good results, they are faced with challenges, such as low accuracy and slow speed in optical image change detection. To solve these problems, an image change detection approach that combines infrared polarization imaging with a fast principal component analysis network (Fast-PCANet) is proposed. Firstly, the acquired infrared polarization images are analyzed, and pixel image blocks are extracted and filtered to obtain the candidate change points. Then, the Fast-PCANet network framework is established, and the candidate pixel image blocks are sent to the network to detect the change pixel points. Finally, the false-detection points predicted by the Fast-PCANet are further corrected by region filling and filtering to obtain the final binary change map of the damage scene. Comparisons with typical PCANet-based change detection algorithms are made on a dataset of infrared-polarized images. The experimental results show that the proposed Fast-PCANet method improves the PCC and the Kappa coefficient of infrared polarization images over infrared intensity images by 6.77% and 13.67%, respectively. Meanwhile, the inference speed can be more than seven times faster. The results verify that the proposed approach is effective and efficient for the change detection task with infrared polarization imaging. The study can be applied to the damage assessment field and has great potential for object recognition, material classification, and polarization remote sensing.

A general unified consistency framework of reciprocal preference relations over bipolar Abelian quasi-ordered monoids

Diverse categories of reciprocal preference relations (RPRs) have been put forward and extensively employed to quantify preference information. Consistency frameworks, which are profoundly interrelated with algebraic structures of RPRs, play a critical role in decision-making systems with RPRs. This paper introduces a novel concept of bipolar Abelian quasi-ordered monoids (BAQO-monoids) and regards any kind of RPRs as preference matrices over a given BAQO-monoid. Six BAQO-monoids are established to characterize algebraic structures of six types of imprecision-based RPRs: interval multiplicative RPRs, interval additive RPRs, triangular fuzzy multiplicative RPRs, triangular fuzzy additive RPRs, trapezoidal fuzzy multiplicative RPRs and trapezoidal fuzzy additive RPRs. Based on a general BAQO-monoid, a transitivity equation system with the binary mapping and parametric elements is developed. These parametric elements are then identified to propose a general unified consistency framework for RPRs over the BAQO-monoid. A distance-based computational formula is built to acquire inconsistency indices of RPRs over the general BAQO-monoid. The proposed general unified consistency framework and inconsistency measuring model are applied to set up consistency models and to calculate inconsistency indices for the six types of imprecision-based RPRs. Six numerical examples and important properties are provided to validate the proposed consistency frameworks and inconsistency measuring models.

Reporting radiographers' interaction with Artificial Intelligence-How do different forms of AI feedback impact trust and decision switching?

Artificial Intelligence (AI) has been increasingly integrated into healthcare settings, including the radiology department to aid radiographic image interpretation, including reporting by radiographers. Trust has been cited as a barrier to effective clinical implementation of AI. Appropriating trust will be important in the future with AI to ensure the ethical use of these systems for the benefit of the patient, clinician and health services. Means of explainable AI, such as heatmaps have been proposed to increase AI transparency and trust by elucidating which parts of image the AI 'focussed on' when making its decision. The aim of this novel study was to quantify the impact of different forms of AI feedback on the expert clinicians' trust. Whilst this study was conducted in the UK, it has potential international application and impact for AI interface design, either globally or in countries with similar cultural and/or economic status to the UK. A convolutional neural network was built for this study; trained, validated and tested on a publicly available dataset of MUsculoskeletal RAdiographs (MURA), with binary diagnoses and Gradient Class Activation Maps (GradCAM) as outputs. Reporting radiographers (n = 12) were recruited to this study from all four regions of the UK. Qualtrics was used to present each participant with a total of 18 complete examinations from the MURA test dataset (each examination contained more than one radiographic image). Participants were presented with the images first, images with heatmaps next and finally an AI binary diagnosis in a sequential order. Perception of trust in the AI systems was obtained following the presentation of each heatmap and binary feedback. The participants were asked to indicate whether they would change their mind (or decision switch) in response to the AI feedback. Participants disagreed with the AI heatmaps for the abnormal examinations 45.8% of the time and agreed with binary feedback on 86.7% of examinations (26/30 presentations).'Only two participants indicated that they would decision switch in response to all AI feedback (GradCAM and binary) (0.7%, n = 2) across all datasets. 22.2% (n = 32) of participants agreed with the localisation of pathology on the heatmap. The level of agreement with the GradCAM and binary diagnosis was found to be correlated with trust (GradCAM:-.515;-.584, significant large negative correlation at 0.01 level (p = < .01 and-.309;-.369, significant medium negative correlation at .01 level (p = < .01) for GradCAM and binary diagnosis respectively). This study shows that the extent of agreement with both AI binary diagnosis and heatmap is correlated with trust in AI for the participants in this study, where greater agreement with the form of AI feedback is associated with greater trust in AI, in particular in the heatmap form of AI feedback. Forms of explainable AI should be developed with cognisance of the need for precision and accuracy in localisation to promote appropriate trust in clinical end users.

Real-time path planning for autonomous vehicle off-road driving.

Autonomous driving is a growing research area that brings benefits in science, economy, and society. Although there are several studies in this area, currently there is no a fully autonomous vehicle, particularly, for off-road navigation. Autonomous vehicle (AV) navigation is a complex process based on application of multiple technologies and algorithms for data acquisition, management and understanding. Particularly, a self-driving assistance system supports key functionalities such as sensing and terrain perception, real time vehicle mapping and localization, path prediction and actuation, communication and safety measures, among others. In this work, an original approach for vehicle autonomous driving in off-road environments that combines semantic segmentation of video frames and subsequent real-time route planning is proposed. To check the relevance of the proposal, a modular framework for assistive driving in off-road scenarios oriented to resource-constrained devices has been designed. In the scene perception module, a deep neural network is used to segment Red-Green-Blue (RGB) images obtained from camera. The second traversability module fuses Light Detection And Ranging (LiDAR) point clouds with the results of segmentation to create a binary occupancy grid map to provide scene understanding during autonomous navigation. Finally, the last module, based on the Rapidly-exploring Random Tree (RRT) algorithm, predicts a path. The Freiburg Forest Dataset (FFD) and RELLIS-3D dataset were used to assess the performance of the proposed approach. The theoretical contributions of this article consist of the original approach for image semantic segmentation fitted to off-road driving scenarios, as well as adapting the shortest route searching A* and RRT algorithms to AV path planning. The reported results are very promising and show several advantages compared to previously reported solutions. The segmentation precision achieves 85.9% for FFD and 79.5% for RELLIS-3D including the most frequent semantic classes. While compared to other approaches, the proposed approach is faster regarding computational time for path planning.

AdSegNet: a deep network to localize billboard in outdoor scenes

Over the past decade, there has been a remarkable expansion in the popularity of online videos, due to the variety of content that has become accessible on the internet. This expansion presents an opportunity for advertising and marketing agencies to take advantage of targeted advertisements. Targeted advertisements can be accomplished by replacing an existing advertisement within an image frame with a new one. There is a limited amount of research on the general task of localizing billboard or advertisement board in outdoor scenes. Therefore, in this study, we proposed a deep neural network that uses a fusion of VGG16 and SegNet architecture to accurately identify the location of an advertisement in an image frame. To evaluate the effectiveness of our proposed method, we compare our proposed method to other semantic segmentation algorithms using a publicly available dataset of outdoor scenes annotated with binary maps of billboards. Our experimental results show that the proposed method achieves 98.58% training accuracy for billboard localization, while testing results gave 96.43% testing accuracy. Additionally, the low RMSE score of our AdSegNet model suggests that it can accurately determine the four corners of the billboard. Therefore, our approach could be beneficial to advertising and marketing agencies that seek to utilize targeted advertisements

Sequence comparison of spoiled gradient echo and balanced steady-state free precession for pulmonary free-breathing proton MRI in patients and healthy volunteers: Correspondence, repeatability, and validation with dynamic contrast-enhanced MRI.

Phase-resolved functional lung (PREFUL) MRI is a proton-based, contrast agent-free technique derived from the Fourier decomposition approach to measure regional ventilation and perfusion dynamics during free-breathing. Besides the necessity of extensive PREFUL postprocessing, the utilized MRI sequence must fulfill specific requirements. This study investigates the impact of sequence selection on PREFUL-MRI-derived functional parameters by comparing the standard spoiled gradient echo (SPGRE) sequence with a lung-optimized balanced steady-state free precession (bSSFP) sequence, thereby facilitating PREFULs clinical application in pulmonary disease assessment. This study comprised a prospective dataset of healthy volunteers and a retrospective dataset of patients with suspected chronic thromboembolic pulmonary hypertension. Both cohorts underwent PREFUL-MRI with both sequences to assess the correspondence of PREFUL ventilation and perfusion parameters (A). Additionally, healthy subjects were scanned a second time to evaluate repeatability (B), whereas patients received dynamic contrast-enhanced (DCE)-MRI, considered the perfusion gold standard for comparison with PREFUL-MRI (C). Signal-to-noise ratio (SNR), calculated from the unprocessed images, was compared alongside median differences of PREFUL-MRI-derived parameters using a paired Wilcoxon signed rank test. Further evaluations included calculation of the Pearson correlation, intraclass-correlation coefficient for repeatability assessment, and spatial overlap (SO) for regional comparison of PREFUL-MRI and DCE-MRI. bSSFP showed a clear SNR advantage over SPGRE (median: 23 vs. 9, p< 0.001). (A) Despite significant differences, parameter values were strongly correlated (r≥0.75). After thresholding, binary maps showed high healthy overlap across both cohorts (SOHealthy > 86%) and high defect overlap in the patient cohort (SODefect≥48%). (B) bSSFP demonstrated slightly higher repeatability across most parameters. (C) Both sequences demonstrated comparable correspondence to DCE-MRI, with SPGRE excelling in absolute quantification and bSSFP in spatial agreement. Although bSSFP showed superior SNR results, both sequences displayed spatial defect concordance and highly correlated PREFUL parameters with deviations regarding repeatability and alignment with DCE-MRI.

Event risks and scenarios for the 1999 Paddington accident

The accident near London in October 1999 didn't just shocks the UK. The collision of the two trains showed the obvious inadequacy of the existing models of train control on the Great Western main line of the UK railways. Specialists from a wide variety of fields of knowledge repeatedly return to this problem, trying to find key ways to minimize risks in this very difficult transportation industry. In this paper, an attempt is made to create a model NS neural network, which makes it possible to predict the possible consequences of a variety of technologically programmed actions, and safe dispatching. The five-sync model is based on all known risk-forming events that contributed to the occurrence of an accident in one way or another. As a function of synapse neuron activation, the bifurcation dependence of the doubled accounting period was chosen due to the number and uncertainty of the content of the accounting indicators that affect the final result. This made it possible to take into account the uncertainties that accompany the occurrence of any intersynapse channels from each input event to the resulting protocol, which assumes only a binary mapping of the expected risk. Thanks to this model, potential and hidden sources of potential risks for any system of events equivalent to the Paddington system are identified, taking into account some of its drawbacks. It is shown that the prevention of such accidents using neural network models should is reduced to automatically tracking images of potential risk-forming events embedded in the trained NS and alerting at least one or two of the monitored ones. It should be noted that such an NS model can be useful not only for the conditions of the Great Western Main Line of the UK railways, but for other systems that are subject to the same operating rules as in our example

A temporal difference matrix for historical cumulative change detection in time series PolSAR data

To detect historical cumulative changes in land cover, this study introduces a novel time series PolSAR change detection method. The objective is to address the inefficiency of traditional detection methods that use multiple pairwise comparisons, as well as to alleviate false positives and false negatives caused by the insufficient utilization of polarization and spatial context information in previous methods. The proposed method initially constructs a temporal difference matrix for time series PolSAR images and computes the difference image (DI) using the maximum eigenvalue of this matrix to depict the cumulative changes over time. Subsequently, the DI is segmented using an MRF-based image segmentation method with a limited amount of supervised information, yielding the cumulative change binary map. The efficacy of the proposed method is validated using three time series datasets covering different scenes acquired by different sensors, namely GaoFen3, Radarsat2 and Sentinel-1. The experimental results demonstrate that the method effectively characterizes cumulative changes in the time series while simultaneously considering disparities in radar backscatter intensity and polarization correlation. As a result, it significantly reduces erroneous and missed change detections and surpasses the existing methods by achieving the highest Recall, F1-score, IOU, Kappa, and overall accuracy. Furthermore, the DI calculation method serves as a versatile change descriptor applicable not only for capturing cumulative changes in time series but also for describing changes between dual temporal images.

VESCL: an open source 2D vessel contouring library.

VESCL (pronounced 'vessel') is a novel vessel contouring library for computer-assisted 2D vessel contouring and segmentation. VESCL facilitates manual vessel segmentation in 2D medical images to generate gold-standard datasets for training, testing, and validating automatic vessel segmentation. VESCL is an open-source C++ library designed for easy integration into medical image processing systems. VESCL provides an intuitive interface for drawing variable-width parametric curves along vessels in 2D images. It includes highly optimized localized filtering to automatically fit drawn curves to the nearest vessel centerline and automatically determine the varying vessel width along each curve. To support a variety of segmentation paradigms, VESCL can export multiple segmentation representations including binary segmentations, occupancy maps, and distance fields. VESCL provides sub-pixel resolution for vessel centerlines and vessel widths. It is optimized to segment small vessels with single- or sub-pixel widths that are visible to the human eye but hard to segment automatically via conventional filters. When tested on neurovascular digital subtraction angiography (DSA), VESCL's intuitive hand-drawn input with automatic curve fitting increased the speed of fully manual segmentation by 22× over conventional methods and by 3× over the best publicly available computer-assisted manual segmentation method. Accuracy was shown to be within the range of inter-operator variability of gold standard manually segmented data from a publicly available dataset of neurovascular DSA images as measured using Dice scores. Preliminary tests showed similar improvements for segmenting DSA of coronary arteries and RGB images of retinal arteries. VESCL is an open-source C++ library for contouring vessels in 2D images which can be used to reduce the tedious, labor-intensive process of manually generating gold-standard segmentations for training, testing, and comparing automatic segmentation methods.

CIBENet: A channel interaction and bridging-enhanced change detection network for optical and SAR remote sensing images

Effectively utilizing the complementary characteristics between optical and SAR remote sensing images to accurately identify change information is of great practical significance. Direct pixel comparisons are challenging since they come from sensors with different imaging mechanisms. Therefore, in this paper, a novel domain transformation that incorporates channel interaction and a bridging-enhanced (CIBENet) heterogeneous change detection network is proposed, where twin-depthwise separable fused gated channel transformation is the convolutional block designed for channel information interaction. And samples without changed semantic information are used to train the deep transformation model, which effectively solves the interference of semantic information on NiceGAN. A different perspective is provided for the heterogeneous change detection task. The backend change detection network takes UNet++ with twin-depthwise separable convolution as the baseline, introduces the gated channel transformation and bridging-enhanced decoder, and models the feature relationship between channels to strengthen the channel information interaction while suppressing the expression of nonchanged information. In addition, the bridging-enhanced decoder can efficiently solve localized holes and discontinuities in binary maps by bridging identical pixels. CIBENet is supervised and experimented on three heterogeneous change detection datasets, Gloucester, Shuguang, and Italy, also compared with the classical unsupervised methods CGAN, SCCN, INLPG and advanced supervised methods DTCDN, DACDT. The network model proposed in this paper significantly improved F1 and recall, and the overall accuracies were 96.60 %, 98.23 %, and 96.29 %, respectively. The experiments validated the reliability of the proposed model.

Integrating deep learning, satellite image processing, and spatial-temporal analysis for urban flood prediction

Urban flooding is escalating worldwide due to the increasing impervious surfaces from urban developments and frequency of extreme rainfall events by climate change. Traditional flood extent prediction and mapping methods based on physical-based hydrological principles often face limitations due to model complexity and computational burden. In response to these challenges, there has been a notable shift toward satellite image processing and Artificial Intelligence (AI) based approaches, such as Deep Learning (DL) models, including architectures like Convolutional Neural Networks (CNN). The objective of this research is to predict near real-time (NRT) flood extents within urban areas. This research integrated CNN (U-Net) with Sentinel-1 satellite imagery, Digital Elevation Model (DEM), hydrologic soil group (HSG), imperviousness, and rainfall data to create a flood extent prediction model. To detect flooded areas, a binary raster map was created using calibrated backscatter values derived from the VV (vertical transmit and vertical receive) polarization mode of Sentinel-1 imagery, which was highlighted as having a significant impact on backscatter behavior and prediction results. Application of the model was demonstrated in urban areas of Miami-Dade County, Florida. The results demonstrated the capability of the model to provide rapid and accurate flood extent predictions at a spatial resolution of 10 m, with an overall accuracy of 97.05 %, F-1 Score of 92.49 %, and AUC of 93 % in the study area. The U-Net model’s flood predictions were compared with historical floodplain data and then using GIS overlay analysis, resulting in a Ground Truth Index of 84.05 % that shows the accuracy of the model in identifying flooded areas. The research incorporated crucial flood-influencing data (including rainfall) to the flood extent prediction models and expanded the focus models beyond major rainfall events only to encompass a wider range of flood events. The presented NRT flood extent mapping model has a broad range of applicability, including, but not limited to, the continuous monitoring of flood events and their potential impacts on civil infrastructure assets (e.g., construction, operation, and maintenance of roads and bridges), early warning systems for timely evacuation and preparedness measures, and insurance risk assessment.

A deep learning model for generating [18F]FDG PET Images from early-phase [18F]Florbetapir and [18F]Flutemetamol PET images

IntroductionAmyloid-β (Aβ) plaques is a significant hallmark of Alzheimer's disease (AD), detectable via amyloid-PET imaging. The Fluorine-18-Fluorodeoxyglucose ([18F]FDG) PET scan tracks cerebral glucose metabolism, correlated with synaptic dysfunction and disease progression and is complementary for AD diagnosis. Dual-scan acquisitions of amyloid PET allows the possibility to use early-phase amyloid-PET as a biomarker for neurodegeneration, proven to have a good correlation to [18F]FDG PET. The aim of this study was to evaluate the added value of synthesizing the later from the former through deep learning (DL), aiming at reducing the number of PET scans, radiation dose, and discomfort to patients.MethodsA total of 166 subjects including cognitively unimpaired individuals (N = 72), subjects with mild cognitive impairment (N = 73) and dementia (N = 21) were included in this study. All underwent T1-weighted MRI, dual-phase amyloid PET scans using either Fluorine-18 Florbetapir ([18F]FBP) or Fluorine-18 Flutemetamol ([18F]FMM), and an [18F]FDG PET scan. Two transformer-based DL models called SwinUNETR were trained separately to synthesize the [18F]FDG from early phase [18F]FBP and [18F]FMM (eFBP/eFMM). A clinical similarity score (1: no similarity to 3: similar) was assessed to compare the imaging information obtained by synthesized [18F]FDG as well as eFBP/eFMM to actual [18F]FDG. Quantitative evaluations include region wise correlation and single-subject voxel-wise analyses in comparison with a reference [18F]FDG PET healthy control database. Dice coefficients were calculated to quantify the whole-brain spatial overlap between hypometabolic ([18F]FDG PET) and hypoperfused (eFBP/eFMM) binary maps at the single-subject level as well as between [18F]FDG PET and synthetic [18F]FDG PET hypometabolic binary maps.ResultsThe clinical evaluation showed that, in comparison to eFBP/eFMM (average of clinical similarity score (CSS) = 1.53), the synthetic [18F]FDG images are quite similar to the actual [18F]FDG images (average of CSS = 2.7) in terms of preserving clinically relevant uptake patterns. The single-subject voxel-wise analyses showed that at the group level, the Dice scores improved by around 13% and 5% when using the DL approach for eFBP and eFMM, respectively. The correlation analysis results indicated a relatively strong correlation between eFBP/eFMM and [18F]FDG (eFBP: slope = 0.77, R2 = 0.61, P-value < 0.0001); eFMM: slope = 0.77, R2 = 0.61, P-value < 0.0001). This correlation improved for synthetic [18F]FDG (synthetic [18F]FDG generated from eFBP (slope = 1.00, R2 = 0.68, P-value < 0.0001), eFMM (slope = 0.93, R2 = 0.72, P-value < 0.0001)).ConclusionWe proposed a DL model for generating the [18F]FDG from eFBP/eFMM PET images. This method may be used as an alternative for multiple radiotracer scanning in research and clinical settings allowing to adopt the currently validated [18F]FDG PET normal reference databases for data analysis.

Modeling of fabric sewing break detection based on U-Net network

To solve the problem of false positives and false negatives in the manual detection of fabric sewing breaks, a method of fabric sewing break detection based on the U-Net network is proposed. By detecting the adjacent distance between the characteristic contours of adjacent sewing stitches, the distribution uniformity of sewing stitches in sewing patterns is calculated, and the abnormal detection and traceability of fabric sewing broken threads are realized. First, the U-Net network sewing feature extraction model was trained using sewing images and their corresponding stitching feature annotation maps. Then, the trained network model was used to process sewing image samples to obtain binary stitching feature maps. Second, the stitching feature maps were processed using a closing operation to eliminate residual image noise. On this basis, the template matching algorithm was used to extract the stitching feature contours. Finally, according to the distance between adjacent feature contours, the fabric sewing break detection and abnormality tracing model was constructed. The model is validated by examples, and the results show that the abnormal samples of stitching lines are detected, and the corresponding break positions are given. The overall detection accuracy of the model is 95.75%, indicating that the constructed fabric sewing break detection model is effective.

A deep learning based relative clarity classification method for infrared and visible image fusion

Infrared and visible image fused(IVIF) results normally suffer from detail loss, noise occurrence, low contrast, and blurred edges. In this paper, a new method is proposed to address the detail loss, low contrast, and blurring issue in IVIF. Specifically, visible images are enhanced by guided filter and high dynamic range compression. Infrared images are normalized by a linear transformation. Then we use blur and clear discrimination to detect salient pixels between infrared and visible images. A fully weight shared multi-path residual neural network is proposed for blur discrimination between infrared and visible image pixels in the same position. Clear pixels are treated as salient pixels, which contribute more to fused images than blur pixels. The output of our proposed network is a binary classification map for blur and clear discrimination, which is treated as our fusion weight map in the fusion stage. To deal with the discontinuous problem, we compute the two distance-transformed maps of the binary classification map and its complementary map. The two distance-transformed maps are used as weight maps to fuse the enhanced infrared and visible images. Finally, we use single scale retinex (SSR) to further enhance our fused images. The experimental results in public IVIF datasets demonstrate the superior performance of our proposed approach over other state-of-the-art methods in terms of both subjective visual quality and objective metrics. The source code is available in https://github.com/eyob12/Multi_path_residual_neural_network_based_IVIF

Comparative analysis of ultrasonographic fetal lung texture in twin and singleton fetuses.

Increased fetal lung heterogeneity has been associated with term fetal lungs in singleton gestations. Theobjective of this study was to determine if fetal lung heterogeneity index (HI) differs between twin and singleton fetuses in the late second and third trimesters. Prospective cohort study of women with singleton and twin gestations with medically-indicated ultrasound examinations at 24weeks of gestation onward. Grayscale transverse fetal lung images were obtained at thelevel of the four-chamber heart. A region of interest wasselected in each fetal lung image. Fetal lung HI was determined with MATLAB software using a dithering technique with ultrasound image pixels transformed into a binary map form from which a dynamic range value was determined. HI averages and standard deviations were generated for twin and singleton fetuses from 24weeks gestation onward. Two sample t-tests were used to compare the mean HI at each gestational week between singleton and twin fetuses. In total, 388 singleton and 478 twin images were analyzed. From 35 through 38weeks of gestation a statistically significant divergence in mean HI was observed withhigher means in singleton compared to twin fetuses. At24weeks of gestation there was a significantly higher HI in twin fetuses compared to singletons. No differences in fetal lung HI were observed between 25 and 34weeks gestational age. Differences in fetal lung HI were observed when comparing twin and singleton fetuses. Further investigation is required to determine the potential clinical significance of these findings.

Identification and Validation of Quantitative Trait Loci Associated with Fruit Puffiness in a Processing Tomato Population.

Physiological disorders impact the yield and quality of marketable fruit in tomato. Puffy fruit caused by cavities inside the locule can be problematic for processing and fresh market quality. In this paper, we used a recombinant inbred line (RIL) and three derived processing tomato populations to map and validate quantitative trait loci (QTLs) for fruit puffiness across environments. Binary interval mapping was used for mapping the incidence of fruit puffiness, and non-parametric interval mapping and parametric composite interval mapping were used for mapping severity. Marker-trait regressions were carried out to validate putative QTLs in subsequent crosses. QTLs were detected on chromosome (Chr) 1, 2, and 4. Only the QTL on Chr 1 was validated in progeny from subsequent crosses. This QTL explained up to 22.5% of the variance in the percentage of puffy fruit, with a significant interaction between loci on Chr 2 and 4, increasing the percentage of puffy fruit by an additional 15%. The allele responsible for puffy fruit on Chr 1 was inherited from parent FG02-188 and was dominant towards increased incidence and severity. Marker-assisted selection (MAS) for the QTL on Chr 1 was as efficient as genomic selection (GS) in reducing the incidence and severity of puffy fruit, despite the potential contribution of other loci.

MANGLEE: A Tool for Mapping and Monitoring MANgrove Ecosystem on Google Earth Engine—A Case Study in Ecuador

Mangroves, integral to ecological balance and socioeconomic well-being, are facing a concerning decline worldwide. Remote sensing is essential for monitoring their evolution, yet its effectiveness is hindered in developing countries by economic and technical constraints. In addressing this issue, this paper introduces MANGLEE (Mangrove Mapping and Monitoring Tool in Google Earth Engine), an accessible, adaptable, and multipurpose tool designed to address the challenges associated with sustainable mangrove management. Leveraging remote sensing data, machine learning techniques (Random Forest), and change detection methods, MANGLEE consists of three independent modules. The first module acquires, processes, and calculates indices of optical and Synthetic Aperture Radar (SAR) data, enhancing tracking capabilities in the presence of atmospheric interferences. The second module employs Random Forest to classify mangrove and non-mangrove areas, providing accurate binary maps. The third module identifies changes between two-time mangrove maps, categorizing alterations as losses or gains. To validate MANGLEE’s effectiveness, we conducted a case study in the mangroves of Guayas, Ecuador, a region historically threatened by shrimp farming. Utilizing data from 2018 to 2022, our findings reveal a significant loss of over 2900 hectares, with 46% occurring in legally protected areas. This loss corresponds to the rapid expansion of Ecuador’s shrimp industry, confirming the tool’s efficacy in monitoring mangroves despite cloud cover challenges. MANGLEE demonstrates its potential as a valuable tool for mangrove monitoring, offering insights essential for conservation, management plans, and decision-making processes. Remarkably, it facilitates equal access and the optimal utilization of resources, contributing significantly to the preservation of coastal ecosystems.

Assessment of land use and land cover change detection and prediction using deep learning techniques for the southwestern coastal region, Goa, India.

Understanding the connections between human activities and the natural environment depends heavily on information about land use and land cover (LULC) in the form of accurate LULC maps. Environmental monitoring using deep learning (DL) is rapidly growing to preserve a sustainable environment in the long term. For establishing effective policies, regulations, and implementation, DL can be a valuable tool for assessing environmental conditions and natural resources that will positively impact the ecosystem. This paper presents the assessment of land use and land cover change detection (LULCCD) and prediction using DL techniques for the southwestern coastal region, Goa, also known as the tourist destination of India. It consists of three components: (i) change detection (CD), (ii) quantification of LULC changes, and (iii) prediction. A new CD assessment framework, Spatio-Temporal Encoder-Decoder Self Attention Network (STEDSAN), is proposed for the LULCCD process. A dual branch encoder-decoder network is constructed using strided convolution with downsampling for the encoder and transpose convolution with upsampling for the decoder to assess the bitemporal images spatially. The self-attention (SA) mechanism captures the complex global spatial-temporal (ST) interactions between individual pixels over space-time to produce more distinct features. Each branch accepts the LULC map of 2 years as one of its inputs to determine binary and multiclass changes among the bitemporal images. The STEDSAN model determines the patterns, trends, and conversion from one LULC type to another for the assessment period from 2005 to 2018. The binary change maps were also compared with the existing state of the art (SOTA) CD methods, with STEDSAN having an overall accuracy of 94.93%. The prediction was made using an recurrent neural network (RNN) known as long short term memory network (LSTM) for the year 2025. Experiments were conducted to determine area-wise changes in several LULC classes, such as built-up (BU), crops (kharif crop (KC), rabi crop (RC), zaid crop (ZC), double/triple (D/T C)), current fallow (CF), plantation (PL), forests (evergreen forest (EF), deciduous forest (DF), degraded/scurb forest (D/SF) ), littoral swamp (LS), grassland (GL), wasteland (WL), waterbodies max (Wmx), and waterbodies min (Wmn). As per the analysis, over the period of 13 years, there has been a net increase in the amount of BU (1.25%), RC (1.17%), and D/TC( 2.42%) and a net decrease in DF (3.29%) and WL(1.44%) being the most dominant classes being changed. These findings will offer a thorough description of identifying trends in coastal areas that may incorporate methodological hints for future studies. This study will also promote handling the spatial and temporal complexity of remotely sensed data employed in categorizing the coastal LULC of a heterogeneous landscape.

Using an ensemble modeling to predict the potential distribution and habitat suitability of caracal (Caracal caracal) in southwestern Iran

Anthropogenic activities and climate change are degrading wildlife habitats, making it crucial to assess suitability of remaining habitat for effective conservation and management of threatened wildlife. This study aimed to determine the variables influencing habitat selection and spatial distribution of caracal (Caracal caracal) in southwestern Iran from 2019 to 2020. Ensembles of Small Models (ESM) were used based on eight species distribution models that included a combination of statistical and machine learning methods (GLM, CTA, FDA, GBM, ANN, MARS, RF, and MaxEnt). The outputs of two ensemble models were hybridized by simple averaging. In ESM 1, distance to water resources was used as a representative of environmental variables and in ESM 2, annual precipitation was used as a representative of bioclimatic variables. In both ESMs, these variables were combined with ecological variables such as the distribution of wild goat (Capra aegagrus) and livestock. The average importance of the model variables for ESM 1 and ESM 2 showed that the distribution of wild goat and livestock were the most important factors in predicting suitable habitat for caracals, respectively. Habitat suitability was higher close to the distribution of wild goat and livestock, near water sources and during periods of higher precipitation. Probabilistic predictions of caracal presence based on the hybridization of ESM 1 and ESM 2 suggest that only 1.3 % (206 ha) of the study area was highly suitable habitat, while 58.4 % (9570 ha) was unsuitable. Based on the ensemble of binary presence-absence maps, caracals were certainly absent in 76.2 % (12,490 ha) of the area, while their presence was uncertain in 21.6 % (3536 ha). The presence of caracals was certain in 2.2 % (363 ha) of the study area. Awareness of the habitat and distribution of caracal can help to make better decisions towards conservation and management and also reduce its conflict with humans.