Visual Research Articles

Near-infrared in vivo imaging system for dynamic visualization of lung-colonizing bacteria in mouse pneumonia.

Conventional animal models of infectious diseases have traditionally relied upon average assessments involving numerous individuals, meaning they do not directly reflect changes in the pathology of an individual. Moreover, in recent years, ethical concerns have resulted in the demand to reduce the number of animals used in such models. Although in vivo imaging offers an effective approach for longitudinally evaluating the pathogenesis of infectious diseases in individual animals, a standardized method has not yet been established. To our knowledge, this study is the first to develop a highly versatile in vivo pulmonary bacterial quantification system utilizing near-infrared luminescence, plasmid-mediated expression of firefly luciferase in bacteria, and a scientific complementary metal-oxide semiconductor camera. Our research holds promise as a useful tool for assessing the efficacy of therapeutic drugs and pathogenesis of infectious diseases.

An Ultrasensitive Programmable 2D Photoelectric Synaptic Transistor

AbstractThe burgeoning advancement of information technology has engendered a discernible surge in the examination of neuromorphic devices, notably drawing broader attention to artificial vision systems endowed with sensory recognition capabilities. Current photoelectric synapse devices employed in artificial vision systems are generally well‐suited for well‐illuminated conditions, yet exhibit diminished sensitivity in weak‐light scenarios, resulting in a pronounced deterioration of recognition accuracy. Here, an ultrasensitive photoelectric synaptic transistor based on negative quantum capacitance effect resulted from the 2D semi‐metallic graphene layer that partially enclosed within the gate dielectric layer, which manifests a noteworthy reduction in device control voltage and exhibits perception and storage capabilities for weak light of 39.4 nW cm−2 with detectivity above 1016 cm Hz1/2 W−1 is demonstrated. The voltage amplification effect and the concomitant formation of an equivalent local electrostatic field induced by the negative quantum capacitance effect engenders a robust programmable synaptic plasticity for extremely weak light by modifying the control gate. These results represent the inaugural integration of the negative quantum capacitance effect into optoelectronic devices and furnish a robust hardware foundation for developing vision systems in weak‐light environments.

Bioinspired Flexible and Low-Voltage Organic Synaptic Transistors for UV Light-Driven Vision Systems.

Neuromorphic vision systems, particularly those stimulated by ultraviolet (UV) light, hold great potential applications in portable electronics, wearable technology, biological analysis, military surveillance, etc. Organic artificial synaptic devices hold immense potential in this field due to their ease of processing, flexibility, and biocompatibility. In this work, we have fabricated a flexible organic field-effect transistor (OFET) that utilizes chitosan-silver nanoparticles (AgNPs) composite material as the active dielectric material. During UV light illumination, both silver nanoparticles and the pentacene layer generate a large number of charge carriers. The photogenerated carriers lead to a more significant hole accumulation at the pentacene interface, resulting in a current rise. In the absence of light, the trapped electron in the silver nanoparticles persists for a longer duration, preventing the instant recombination with holes. This extended retention of electrons leads to the observed synaptic performance of the transistor. The use of aluminum oxide (Al2O3) as one of the dielectric layers enables the device to operate effectively at low voltage (<1 V). The device mimics various crucial synaptic properties of the brain, including short-term potentiation and long-term potentiation (STP and LTP), paired-pulse facilitation (PPF), spike-duration dependent plasticity (SDDP), spike-number dependent plasticity (SNDP), and spike-rate dependent plasticity (SRDP), etc. This work introduces an approach to develop flexible organic synaptic transistors that operate efficiently at low voltages, paving the way toward high-performance, UV light-driven neuromorphic vision systems.

How life became colourful: colour vision, aposematism, sexual selection, flowers, and fruits.

Plants and animals are often adorned with potentially conspicuous colours (e.g. red, yellow, orange, blue, purple). These include the dazzling colours of fruits and flowers, the brilliant warning colours of frogs, snakes, and invertebrates, and the spectacular sexually selected colours of insects, fish, birds, and lizards. Such signals are often thought to utilize pre-existing sensitivities in the receiver's visual systems. This raises the question: what was the initial function of conspicuous colouration and colour vision? Here, we review the origins of colour vision, fruit, flowers, and aposematic and sexually selected colouration. We find that aposematic colouration is widely distributed across animals but relatively young, evolving only in the last ~150 million years (Myr). Sexually selected colouration in animals appears confined to arthropods and chordates, and is also relatively young (generally <100 Myr). Colourful flowers likely evolved ~200 million years ago (Mya), whereas colourful fruits/seeds likely evolved ~300 Mya. Colour vision (sensu lato) appears to be substantially older, and likely originated ~400-500 Mya in both arthropods and chordates. Thus, colour vision may have evolved long before extant lineages with fruit, flowers, aposematism, and sexual colour signals. We also find that there appears to have been an explosion of colour within the last ~100 Myr, including >200 origins of aposematic colouration across nine animal phyla and >100 origins of sexually selected colouration among arthropods and chordates.

Automated Detection of Hillforts in Remote Sensing Imagery With Deep Multimodal Segmentation

ABSTRACTRecent advancements in remote sensing and artificial intelligence can potentially revolutionize the automated detection of archaeological sites. However, the challenging task of interpreting remote sensing imagery combined with the intricate shapes of archaeological sites can hinder the performance of computer vision systems. This work presents a computer vision system trained for efficient hillfort detection in remote sensing imagery. Equipped with an adapted multimodal semantic segmentation model, the system integrates LiDAR‐derived LRM images and aerial orthoimages for feature fusion, generating a binary mask pinpointing detected hillforts. Post‐processing includes margin and area filters to remove edge inferences and smaller anomalies. The resulting inferences are subjected to hard positive and negative mining, where expert archaeologists classify them to populate the training data with new samples for retraining the segmentation model. As the computer vision system is far more likely to encounter background images during its search, the training data are intentionally biased towards negative examples. This approach aims to reduce the number of false positives, typically seen when applying machine learning solutions to remote sensing imagery. Northwest Iberia experiments witnessed a drastic reduction in false positives, from 5678 to 40 after a single hard positive and negative mining iteration, yielding a 99.3% reduction, with a resulting F1 score of 66%. In England experiments, the system achieved a 59% F1 score when fine‐tuned and deployed countrywide. Its scalability to diverse archaeological sites is demonstrated by successfully detecting hillforts and other types of enclosures despite their typical complex and varied shapes. Future work will explore archaeological predictive modelling to identify regions with higher archaeological potential to focus the search, addressing processing time challenges.

Interactions Between 3D Surface Shape and Material Perception

Our visual systems are remarkably adept at deriving the shape and material properties of surfaces even when only one image of a surface is available. This ability implies that a single image of a surface contains potent information about both surface shape and material. However, from a computational perspective, the problem of deriving surface shape and material is formally ill posed. Any given image could be due to many combinations of shape, material, and illumination. Early computational models required prior knowledge about two of the three scene variables to derive the third. However, such models are biologically implausible because our visual systems are tasked with extracting all relevant scene variables from images simultaneously. This review describes recent progress in understanding how the visual system solves this problem by identifying complex forms of image structure that support its ability to simultaneously derive the shape and material properties of surfaces from images.

The Evolution of Simplifying Heuristics in Visual Cognition: Categorization, Specialization, and Visual Illusions.

Animals live in visually complex environments. As a result, visual systems have evolved mechanisms that simplify visual processing and allow animals to focus on the information that is most relevant to adaptive decision making. This review explores two key mechanisms that animals use to efficiently process visual information: categorization and specialization. Categorization occurs when an animal's perceptual system sorts continuously varying stimuli into a set of discrete categories. Specialization occurs when particular classes of stimuli are processed using distinct cognitive operations that are not used for other classes of stimuli. We also describe a nonadaptive consequence of simplifying heuristics: visual illusions, where visual perception consistently misleads the viewer about the state of the external world or objects within it. We take an explicitly comparative approach by exploring similarities and differences in visual cognition across human and nonhuman taxa. Considering areas of convergence and divergence across taxa provides insight into the evolution and function of visual systems and associated perceptual strategies.

Insights Into Myopia from Mouse Models.

Animal models are critical for understanding the initiation and progression of myopia, a refractive condition that causes blurred distance vision. The prevalence of myopia is rapidly increasing worldwide, and myopia increases the risk of developing potentially blinding diseases. Current pharmacological, optical, and environmental interventions attenuate myopia progression in children, but it is still unclear how this occurs or how these interventions can be improved to increase their protective effects. To optimize myopia interventions, directed mechanistic studies are needed. The mouse model is well-suited to these studies because of its well-characterized visual system and the genetic experimental tools available, which can be combined with pharmacological and environmental manipulations for powerful investigations of causation. This review describes aspects of the mouse visual system that support its use as a myopia model and presents genetic, pharmacological, and environmental studies that significantly contribute to our understanding of the mechanisms that underlie myopigenesis.

The Role of Chromatic Aberration in Vision.

The study of biological optics would be complicated enough if light only came in a single wavelength. However, altering the wavelength (or distribution of wavelengths) of light has multiple effects on optics, including on diffraction, scattering (of various sorts), transmission through and reflection by various media, fluorescence, and waveguiding properties, among others. In this review, we consider just one wavelength-dependent optical effect: longitudinal chromatic aberration (LCA). All vertebrate eyes that have been tested have significant LCA, with shorter (bluer) wavelengths of light focusing closer to the front of the eye than longer (redder) wavelengths. We consider the role of LCA in the visual system in terms of both how it could degrade visual acuity and how biological systems make use of it.

Deep feature fusion with computer vision driven fall detection approach for enhanced assisted living safety

Assisted living facilities cater to the demands of the elderly population, providing assistance and support with day-to-day activities. Fall detection is fundamental to ensuring their well-being and safety. Falls are frequent among older persons and might cause severe injuries and complications. Incorporating computer vision techniques into assisted living environments is revolutionary for these issues. By leveraging cameras and complicated approaches, a computer vision (CV) system can monitor residents’ movements continuously and identify any potential fall events in real time. CV, driven by deep learning (DL) techniques, allows continuous surveillance of people through cameras, investigating complicated visual information to detect potential fall risks or any instances of falls quickly. This system can learn from many visual data by leveraging DL, improving its capability to identify falls while minimalizing false alarms precisely. Incorporating CV and DL enhances the efficiency and reliability of fall detection and allows proactive intervention, considerably decreasing response times in emergencies. This study introduces a new Deep Feature Fusion with Computer Vision for Fall Detection and Classification (DFFCV-FDC) technique. The primary purpose of the DFFCV-FDC approach is to employ the CV concept for detecting fall events. Accordingly, the DFFCV-FDC approach uses the Gaussian filtering (GF) approach for noise eradication. Besides, a deep feature fusion process comprising MobileNet, DenseNet, and ResNet models is involved. To improve the performance of the DFFCV-FDC technique, improved pelican optimization algorithm (IPOA) based hyperparameter selection is performed. Finally, the detection of falls is identified using the denoising autoencoder (DAE) model. The performance analysis of the DFFCV-FDC methodology was examined on the benchmark fall database. A widespread comparative study reported the supremacy of the DFFCV-FDC approach with existing techniques.

How Shape Perception Works, in Two Dimensions and Three Dimensions

The ventral visual pathway transforms retinal images into neural representations that support object understanding, including exquisite appreciation of precise 2D pattern shape and 3D volumetric shape. We articulate a framework for understanding the goals of this transformation and how they are achieved by neural coding at successive ventral pathway stages. The critical goals are (a) radical compression to make shape information communicable across axonal bundles and storable in memory, (b) explicit coding to make shape information easily readable by the rest of the brain and thus accessible for cognition and behavioral control, and (c) representational stability to maintain consistent perception across highly variable viewing conditions. We describe how each transformational step in ventral pathway vision serves one or more of these goals. This three-goal framework unifies discoveries about ventral shape processing into a neural explanation for our remarkable experience of shape as a vivid, richly detailed aspect of the natural world.

Real-time two-dimensional visualization reveals the transport mechanisms of biochar colloids in the presence of DOM in porous media

Biochar colloids (BCs) have attracted much attention globally, and their fate and transport in the subsurface are significantly influenced by soil-dissolved organic matter (DOM) in soil. This study utilized a real-time and non-invasive visualization system to reveal the transport and retention behavior of BCs in the presence of DOM in two-dimensional porous media. Results indicated that the presence of DOM enhanced the transport of BCs, due to its increased negative charge density and increased repulsion between BCs. The change in the particle size of the porous medium was also shown to affect the BCs transport in the porous media. Additionally, the negative charge of BCs shielded by high IS, the mobility of BCs decreased by 30.37 % from 1 mM to 50 mM. When the pH was increased from 5 to 9, the oxygen-containing functional groups of BCs and DOM were dissociated, and the mobility of BCs increased by 32.41 %. Through a simplified Double-Monod model, we fitted the breakthrough curves for BCs transport in porous media (R2 > 0.94). Moreover, the mechanism of different conditions on colloid clogging behavior was further elucidated through the DLVO theory. These findings extend the understanding of the environmental behavior of BCs in the presence of DOM derived from soil, enabling us to assess better and predict their environmental risks.

The Absorption, Storage, and Transport of Ocular Carotenoids and Retinoids.

Carotenoids, yellow and red pigments found abundantly in nature, play essential roles in various aspects of human physiology. They serve as critical molecules in vision by functioning as antioxidants and as filters for blue light within the retina. Furthermore, carotenoids are the natural precursors of vitamin A, which is indispensable for the synthesis of retinaldehyde, the visual chromophore, and retinoic acid, a small molecule that regulates gene expression. Insufficient levels of carotenoids and retinoids have been linked to age-related macular degeneration and xerophthalmia, respectively. Nevertheless, the mechanisms by which the eye maintains carotenoid and retinoid homeostasis have remained a mystery. Recent breakthroughs identified the molecular players involved in this process and provided valuable biochemical insights into their functioning. Mutations in the corresponding genes disrupt the homeostasis of carotenoids and retinoids, leading to visual system pathologies. This review aims to consolidate our current understanding of these pathways, including their regulatory principles.

345 Breed differences within heart scores of fattening feedlot cattle

Abstract Feedlot heart disease (FHD) is a condition in fattening beef cattle that arises from right-side heart remodeling resulting from alveolar hypoxia. This hypoxic environment is hypothesized to result from the large body mass of fattened cattle. Sensitive cattle have increased incidence of pulmonary hypertension resulting from compensation mechanisms of the individual. The severity of this remodeling is quantified at harvest by gross examination of the heart utilizing a visual grading system of 1-5, where a score of 1 is a normal heart and a score of 5 is a severely remodeled heart. Previously, heart score was found to contain sire differences and to be moderately heritable. This led to more questions regarding other potential factors influencing heart scores, one of which was breed differences. Therefore, the objective of this study was to examine the prevalence of heart remodeling in beef and dairy cattle breeds and to investigate whether some breeds had a greater prevalence of heart remodeling. Heart scores were collected on Angus-influenced animals raised in the panhandle of Texas (1,103 m) and a Beef x Dairy group raised in western Kansas (846 m). The total number of animal observations was 2,069. Twenty-four percent of the individuals exhibited moderate to severe remodeling (score 3 and greater). Of this cohort, 1,307 animals had breed composition data (i.e., Angus, Simmental, Jersey, and Holstein) suitable for use in the analysis. Breed proportions ranged from 0% to 100% for Angus, 0 to 93.25% for Simmental, 0% to 69.22% for Jersey, and 0% to 78.06% for Holstein. Utilizing a linear regression model with heart score as the response and breed percentages and heart scorers as fixed effects, the importance of breed composition (covariate) was determined. The percentages of Angus influence tended to be associated with heart score (P = 0.0506), with a regression coefficient of -0.005. The percentage of Simmental had an association with the variation in heart score (P &amp;lt; 0.05) and had a regression coefficient of -0.008. Additionally, the percentages of Jersey and Holstein were found to reduce the incidence of heart score by a factor of -0.014 and -0.018 (P &amp;lt; 0.05), respectively. In summary, an increased percentage of either Jersey or Holstein breed resulted in reduced heart scores, while the incidence of beef breeds was found to be associated with some to little difference in the improvement of heart scores, alluding to potential breed advantages in mitigating right sided heart remodeling.

67 Precision identification and weight assessment of cattle using supervised machine learning on body surface keypoints

Abstract Computer vision systems (CVS) offer identification solutions for animals with distinct coat patterns, but are less effective for solid-colored herds. In addition, they can be used to measure critical phenotypes, such as body weight (BW). Providing both BW and identification for solid-colored animals can help farmers make decisions. This study aimed to 1) develop an automatic CVS capable of identifying using the Euclidean distance between keypoints located at specific anatomical landmarks (e.g., bony prominences), and 2) predict the BW using features extracted from these keypoints. The keypoint model was trained to identify seven keypoints per cow, located at the left and right hips, left and right pin bones, tail head, sacral, and cervical vertebrae. These locations remain constant as body condition score of a cow changes. The Euclidean distance between these keypoints was used to generate biometric features for each cow. The keypoint detection model was trained using 3,928 images over 900 epochs with a batch size of 14 and validated using 391 images. This trained model was used to predict keypoints on a different set of 41 cows over 5 d, resulting in a total of 6,944 images which correspond to the dataset for cow identification. The Random Forest (RF) algorithm for cow identification was trained with the first 4 d (5,376 images), and the last day served as the test set (1,568 images). For the cow identification, a RF algorithm was employed on the Euclidean distance of the keypoints. The hyperparameters were determined through a grid search on the training set, utilizing 5-fold cross-validation. The hyperparameters ‘mtry’ (which ranged from 1 to 10) and ‘ntree’ (which ranged from 250 to 500, in increments of 50) were optimized. For the second objective, due to the completely new environment, the keypoint model was fine-tuned using transfer learning for 240 epochs with a batch size of 14, utilizing 310 images. The model predicted keypoints in 1,593 images from different beef-on-dairy animals. This dataset was used to train the BW prediction model. The Euclidean distances from the predicted images and sex were used as features. A RF model was trained using a leave-one-animal-out cross-validation approach. The hyperparameters were determined through a grid search on the training set, using 10-fold cross-validation. The ‘mtry’ parameter ranged from 2 to 16, in increments of 2. For animal identification, the model achieved an accuracy, precision, recall, and F1-score of 92.7%, 89.0%, 90.2%, and 92.7%, respectively. To predict BW, the RF model achieved an R² of 0.86 and a root mean squared prediction error of 36.9 kg, representing 6.7% of the observed values average. These results suggest that keypoints located on the dorsal body surface can identify and weigh individual animals, even those lacking distinct coat color patterns.

A comprehensive qualitative and quantitative survey on image dehazing based on deep neural networks

Image dehazing has become a necessary area of research with the increasing popularity and demand of computer vision systems. Image dehazing is a method to remove haze from an image to improve its visual quality. Dehazing techniques are widely employed in a variety of computer vision applications to enhance their overall performance. Many techniques have been proposed by researchers in recent years to eliminate the haze from an image. However, there is a lack of available literature that provides a summary of deep learning-based state-of-the-art image dehazing methods. In this study, we provide a detailed review of recently proposed image dehazing techniques based on deep neural networks such as CNN, GAN, RNN, RCNN, and Transformer. A concise review of significant applications of image dehazing, benchmark datasets, and various performance metrics are also presented. We compare the state-of-the-art methods quantitatively using performance evaluation metrics such as SSIM and PSNR. Finally, this study discusses the fundamental difficulties associated with image dehazing approaches that need to be further explored.

80 Empowering informed choices: How computer vision can assist consumers in making decisions about meat quality

Abstract Consumer perception of meat quality is highly influenced by meat tenderness and juiciness. Artificial intelligence technology, such as computer vision systems (CVS), can be a powerful tool to predict such meat attributes in retail markets and help consumers make informed decisions. This study aimed to develop a CVS using smartphone images to classify beef steaks and pork chops tenderness (1), predicting shear-force (SF) and intramuscular fat (IMF) content (2), and performing a comparative evaluation between consumer assessments and the CVS (3). The dataset for beef steak consisted of 915 images (one image per steak), with 691 in the training set, 82 in the validation set, and 142 in the testing set. For pork, there were 514 images (one image per chop), distributed as 377 in the training set, 80 in the validation set, and 57 in the testing set. We trained deep neural networks (Xception), pretrained on the ImageNet dataset, for image classification and regression. The tenderness of the steaks was categorized based on Warner-Bratzler shear-force (N) as tender, intermediate, and tough. To achieve the third object, 1,000 pairwise comparisons were drawn from the 142 testing set images, where both consumers and the CVS performed predictions on tender and tough meat. The option with the least SF in each pair was identified as the most tender meat, with ground truth established using SF measurements. The same pairs were presented to meat consumers through a survey. For classifying beef as tender and tough, the algorithms demonstrated F1-scores of 68.1% and 70.8%, respectively. However, intermediate meat yielded worse results, with an F1-score of 36.4%. The dataset was subsequently re-categorized into two classes: tender and tough, with all intermediate steaks classified as tough, and resulted in an F1-score of 76.6% for tough meat. For classifying pork chops as tender and tough, the algorithms demonstrated F1-scores of 81.4% and 85.7%, respectively. The intermediate class in pork chops also exhibited worse results, with an F1-score of 24.0%. After re-categorizing the dataset into two classes, the F1-score for classifying pork chops as tough was 83.9%. The regression model predicted SF in beef steak and pork chops with R2 of 0.64 and 0.76, and RMSEP of 16.9 N and 9.15 N, respectively. The regression model, after analyzing with 1,000 pairwise comparisons, accurately predicted the tenderest steak with an accuracy of 76.5%. In contrast, human recognition of meat tenderness achieved an average accuracy of 46.7% for beef steak. To predict IMF in beef steaks and pork chops the model achieved R2 values of 0.61 and 0.54, and RMSEP of 2.6% and 1.22%, respectively. These findings suggest that CVS can provide a more objective method for evaluating meat tenderness and IMF before purchase, potentially enhancing consumer satisfaction.

Defect detection on RTM composite parts via robotic contact measurement system

In this work a new approach to detect and to evaluate defects typical of carbon fiber parts manufactured via resin transfer molding (RTM) is presented. The approach is based on a robotic contact measurement system, as an alternative or combined solution to vision methods. The research is meant to provide more accurate local geometry information to the global information acquired using vision systems. The system consists of a collaborative robot equipped with a force-torque sensor that scans the surface of the workpiece with a defined motion strategy. To evaluate the system, tests were conducted on a 3D printed part presenting a feature representative of the defects found on carbon fiber components. The results are compared with the CAD model and data acquired using a structured light scanner, showing that robotic contact measurement can be a viable method to enhance the reconstruction of a part with a higher accuracy. The system is then applied and validated on a real part made with RTM.

Deep learning-based sow posture classifier using colour and depth images

Assessing sow posture is essential for understanding their physiological condition and helping farmers improve herd productivity. Deep learning-based techniques have proven effective for image interpretation, offering a better alternative to traditional image processing methods. However, distinguishing transitional postures such as sitting and kneeling is challenging with only conventional top-view RGB images. This study aimed to develop and compare different deep learning-based sow posture classifiers using different architectures and image types. Using Kinect v.2 cameras, RGB and depth images were collected from 9 sows housed individually in farrowing crates. A total of 26,362 images were manually labelled by posture: “standing”, “kneeling”, “sitting”, “ventral recumbency” and “lateral recumbency”. Different deep learning algorithms were developed to detect sow postures from three types of images: colour (RGB), depth (depth image transformed into greyscale), and fused (colour-depth composite images). Results indicated that the ResNet-18 model presented the best results and that including depth information improved the performance of all models tested. Depth and fused models achieved higher accuracies than the models using only RGB images. The best model used only depth images as input and presented an accuracy of 98.3 %. The mean precision and recall values were 97.04 % and 97.32 %, respectively (F1-score = 97.2 %). The study shows improved posture classification using depth images. Future research can improve model accuracy and speed by expanding the database, exploring fused methods and computational models, considering different breeds of sows, and incorporating more postures. These models can be integrated into computer vision systems to automatically characterise sow behavior.

The Role of Population Receptive Field Sizes in Higher-Order Visual Dysfunction.

Population receptive field (pRF) modeling is an fMRI technique used to retinotopically map visual cortex, with pRF size characterizing the degree of spatial integration. In clinical populations, most pRF mapping research has focused on damage to visual system inputs. Herein, we highlight recent work using pRF modeling to study high-level visual dysfunctions. Larger pRF sizes, indicating coarser spatial processing, were observed in homonymous visual field deficits, aging, and autism spectrum disorder. Smaller pRF sizes, indicating finer processing, were observed in Alzheimer's disease and schizophrenia. In posterior cortical atrophy, a unique pattern was found in which pRF size changes depended on eccentricity. Changes to pRF properties were observed in clinical populations, even in high-order impairments, explaining visual behavior. These pRF changes likely stem from altered interactions between brain regions. Furthermore, some studies suggested that pRF sizes change as part of cortical reorganization, and they can point towards future prognosis.