Contrast Mechanism Research Articles

Neuromelanin-sensitive MRI for mechanistic research and biomarker development in psychiatry.

Neuromelanin-sensitive MRI is a burgeoning non-invasive neuroimaging method with an increasing number of applications in psychiatric research. This MRI modality is sensitive to the concentration of neuromelanin, which is synthesized from intracellular catecholamines and accumulates in catecholaminergic nuclei including the dopaminergic substantia nigra and the noradrenergic locus coeruleus. Emerging data suggest the utility of neuromelanin-sensitive MRI as a proxy measure for variability in catecholamine metabolism and function, even in the absence of catecholaminergic cell loss. Given the importance of catecholamine function to several psychiatric disorders and their treatments, neuromelanin-sensitive MRI is ideally positioned as an informative and easy-to-acquire catecholaminergic index. In this review paper, we examine basic aspects of neuromelanin and neuromelanin-sensitive MRI and focus on its psychiatric applications in the contexts of mechanistic research and biomarker development. We discuss ongoing debates and state-of-the-art research into the mechanisms of the neuromelanin-sensitive MRI contrast, standardized protocols and optimized analytic approaches, and application of cutting-edge methods such as machine learning and artificial intelligence to enhance the feasibility and predictive power of neuromelanin-sensitive-MRI-based tools. We finally lay out important future directions to allow neuromelanin-sensitive-MRI to fulfill its potential as a key component of the research, and ultimately clinical, toolbox in psychiatry.

Investigation of a hyperspectral Scanning Laser Optical Tomography setup for label-free cell identification

The development of non-destructive, tomographic imaging systems is a current topic of research in biomedical technologies. One of these technologies is Scanning Laser Optical Tomography (SLOT), which features a highly modular setup with various contrast mechanisms. Extending this technology with new acquisition mechanisms allows us to investigate untreated and non-stained biological samples, leaving their natural biological physiology intact. To enhance the development of SLOT, we aimed to extend the density of information with a significant increase of acquisition channels. This should allow us to investigate samples with unknown emission spectra and even allow for label-fee cell identification. We developed and integrated a hyperspectral module into an existing SLOT system. The adaptations allow for the acquisition of three-dimensional datasets containing a highly increased information density. For validation, artificial test objects were made from fluorescent acrylic and acquired with the new hyperspectral setup. In addition, measurements were made on two different human cell spheroids with an unknown spectra, to test the possibilities of label-free cell identification. The validation measurements of the artificial test target show the expected results. Furthermore, the measurements of the biological cell spheroids show small variations in their tomographic spectrum that allow for label-free cell type differentiation. The results of the biological sample demonstrate the potential of label-free cell identification of the newly developed setup.

Attributed graph clustering under the contrastive mechanism with cluster-preserving augmentation

Attributed graph clustering is a fundamental task in complex network analysis. Many existing graph clustering methods utilize graph representation learning techniques to learn node representations, subsequently applying K-means for clustering. Despite the significant attention and promising outcomes of graph contrastive learning in graph representation learning, we find two essential problems that need to be solved. (1) How to achieve augmentation for contrast that preserves the cluster structure of a given graph? (2) How to design an effective contrastive learning mechanism that collaborates with clustering? Therefore, we propose an attributed graph clustering method under the contrastive mechanism with cluster-preserving augmentation, integrating node representation learning and clustering into a unified framework. Specifically, we construct a contrasting view based on a generated kNN graph and edge betweenness centrality to preserve the cluster structure in the original graph. Meanwhile, a multilevel contrast mechanism based on pseudo-label-guided negative sampling is proposed to maximize the agreement between node representations in multiple latent spaces. We jointly optimize a specific clustering objective during the contrastive process, leading to refined cluster distribution directly in training. Extensive experiments on several datasets demonstrate that our proposed model consistently outperforms existing state-of-the-art methods on clustering.

OsO2 as the Contrast-Generating Chemical Species of Osmium-Stained Biological Tissues in Electron Microscopy.

Electron imaging of biological samples stained with heavy metals has enabled visualization of subcellular structures critical in chemical-, structural-, and neuro-biology. In particular, osmium tetroxide (OsO4) has been widely adopted for selective lipid imaging. Despite the ubiquity of its use, the osmium speciation in lipid membranes and the process for contrast generation in electron microscopy (EM) have continued to be open questions, limiting efforts to improve staining protocols and therefore high-resolution nanoscale imaging of biological samples. Following our recent success using photoemission electron microscopy (PEEM) to image mouse brain tissues with synaptic resolution, we have used PEEM to determine the nanoscale electronic structure of Os-stained biological samples. Os(IV), in the form of OsO2, generates nanoaggregates in lipid membranes, leading to a strong spatial variation in the electronic structure and electron density of states. OsO2 has a metallic electronic structure that drastically increases the electron density of states near the Fermi level. Depositing metallic OsO2 in lipid membranes allows for strongly enhanced EM signals and conductivity of biological materials. The identification of the chemical species and understanding of the membrane contrast mechanism of Os-stained biological specimens provides a new opportunity for the development of staining protocols for high-resolution, high-contrast EM imaging.

Asymmetric double-winged multi-view clustering network for exploring diverse and consistent information

In unsupervised scenarios, deep contrastive multi-view clustering (DCMVC) is becoming a hot research spot, which aims to mine the potential relationships between different views. Most existing DCMVC algorithms focus on exploring the consistency information for the deep semantic features, while ignoring the diverse information on shallow features. To fill this gap, we propose a novel multi-view clustering network termed CodingNet to explore the diverse and consistent information simultaneously in this paper. Specifically, instead of utilizing the conventional auto-encoder, we design an asymmetric structure network to extract shallow and deep features separately. Then, by approximating the similarity matrix on the shallow feature to the zero matrix, we ensure the diversity for the shallow features, thus offering a better description of multi-view data. Moreover, we propose a dual contrastive mechanism that maintains consistency for deep features at both view-feature and pseudo-label levels. Our framework’s efficacy is validated through extensive experiments on six widely used benchmark datasets, outperforming most state-of-the-art multi-view clustering algorithms.

Streak artefact removal in x-ray dark-field computed tomography using a convolutional neural network.

Computed tomography (CT) relies on the attenuation of x-rays, and is, hence, of limited use for weakly attenuating organs of the body, such as the lung. X-ray dark-field (DF) imaging is a recently developed technology that utilizes x-ray optical gratings to enable small-angle scattering as an alternative contrast mechanism. The DF signal provides structural information about the micromorphology of an object, complementary to the conventional attenuation signal. A first human-scale x-ray DF CT has been developed by our group. Despite specialized processing algorithms, reconstructed images remain affected by streaking artifacts, which often hinder image interpretation. In recent years, convolutional neural networks have gained popularity in the field of CT reconstruction, amongst others for streak artefactremoval. Reducing streak artifacts is essential for the optimization of image quality in DF CT, and artefact free images are a prerequisite for potential future clinical application. The purpose of this paper is to demonstrate the feasibility of CNN post-processing for artefact reduction in x-ray DF CT and how multi-rotation scans can serve as a pathway for trainingdata. We employed a supervised deep-learning approach using a three-dimensional dual-frame UNet in order to remove streak artifacts. Required training data were obtained from the experimental x-ray DF CT prototype at our institute. Two different operating modes were used to generate input and corresponding ground truth data sets. Clinically relevant scans at dose-compatible radiation levels were used as input data, and extended scans with substantially fewer artifacts were used as ground truth data. The latter is neither dose-, nor time-compatible and, therefore, unfeasible for clinical imaging ofpatients. The trained CNN was able to greatly reduce streak artifacts in DF CT images. The network was tested against images with entirely different, previously unseen image characteristics. In all cases, CNN processing substantially increased the image quality, which was quantitatively confirmed by increased image quality metrics. Fine details are preserved during processing, despite the output images appearing smoother than the ground truthimages. Our results showcase the potential of a neural network to reduce streak artifacts in x-ray DF CT. The image quality is successfully enhanced in dose-compatible x-ray DF CT, which plays an essential role for the adoption of x-ray DF CT into modern clinicalradiology.

Enhancing Hypotension Prediction in Real-time Patient Monitoring Through Deep Learning: A Novel Application of XResNet with Contrastive Learning and Value Attention Mechanisms.

The precise prediction of hypotension is vital for advancing preemptive patient care strategies. Traditional machine learning approaches, while instrumental in this field, are hampered by their dependence on structured historical data and manual feature extraction techniques. These methods often fall short of recognizing the intricate patterns present in physiological signals. Addressing this limitation, our study introduces an innovative application of deep learning technologies, utilizing a sophisticated end-to-end architecture grounded in XResNet. This architecture is further enhanced by the integration of contrastive learning and a value attention mechanism, specifically tailored to analyze arterial blood pressure (ABP) waveform signals. Our approach improves the performance of hypotension prediction over the existing state-of-theart ABP model [7]. This research represents a step towards optimizing patient care, embodying the next generation of AI-driven healthcare solutions. Through our findings, we demonstrate the promise of deep learning in overcoming the limitations of conventional prediction models, thereby offering an avenue for enhancing patient outcomes in clinical settings.

Vibrational imaging of metabolites for improved microbial cell strains.

Biomanufacturing utilizes modified microbial systems to sustainably produce commercially important biomolecules for use in agricultural, energy, food, material, and pharmaceutical industries. However, technological challenges related to non-destructive and high-throughput metabolite screening need to be addressed to fully unlock the potential of synthetic biology and sustainable biomanufacturing. This perspective outlines current analytical screening tools used in industrial cell strain development programs and introduces label-free vibrational spectro-microscopy as an alternative contrast mechanism. We provide an overview of the analytical instrumentation currently used in the "test" portion of the design, build, test, and learn cycle of synthetic biology. We then highlight recent progress in Raman scattering and infrared absorption imaging techniques, which have enabled improved molecular specificity and sensitivity. Recent developments in high-resolution chemical imaging methods allow for greater throughput without compromising the image contrast. We provide a roadmap of future work needed to support integration with microfluidics for rapid screening at the single-cell level. Quantifying the net expression of metabolites allows for the identification of cells with metabolic pathways that result in increased biomolecule production, which is essential for improving the yield and reducing the cost of industrial biomanufacturing. Technological advancements in vibrational microscopy instrumentation will greatly benefit biofoundries as a complementary approach for non-destructive cell screening.

Contrasting mechanisms of non‐vascular and vascular plants on spatial turnover in multifunctionality in the Antarctic continent

Abstract Dominant plants play crucial roles in supporting the functioning of terrestrial ecosystems. Plants can influence the spatial heterogeneity of environmental factors, as well as the spatial turnover in the composition of soil communities (i.e. β‐diversity of soil communities). However, we still poorly understand how dominant plants drive the spatial turnover in multiple ecosystem functions (β‐multifunctionality hereafter), and to which extent the effects of dominant plants are mediated by changes in environmental heterogeneity and the β‐diversity of soil communities. Antarctica supports one of the most challenging environments on the planet including low temperature and water availability. Here, we collected soil samples under three dominant plants (lichen, moss and vascular plants) and bare ground. We measured carbon storage, nutrient availability, nutrient decomposition, microbial biomass and pathogen control to calculate β‐multifunctionality. Both non‐vascular and vascular plants were associated with increased β‐multifunctionality compared to bare ground. We further showed that lichen mainly affected β‐multifunctionality through soil temperature heterogeneity and β‐bacterial diversity. Similarly, moss mainly affected β‐multifunctionality through the spatial heterogeneity of soil water content and β‐bacterial diversity. However, vascular plants did not significantly affect environmental heterogeneity. Instead, the responses of β‐multifunctionality to vascular plants were mainly driven by the β‐diversity of soil communities. These results indicate that environmental heterogeneity is important for turnover in multiple ecosystem functions in early successional stages (dominated by non‐vascular plants), while the importance of soil communities' heterogeneity becomes more significant in late successional stages (dominated by vascular plants). Synthesis. Our findings highlight the fundamental role of dominant plants in controlling the spatial turnover in ecosystem functions, and suggest that accelerated succession under current climate warming may increase bacterial β‐diversity but decrease abiotic heterogeneity, thereby leading to both increases (e.g. regarding functions related to microbial biomass) and decreases (e.g. regarding functions related to nutrient availability) in β‐multifunctionality and hence the spatial turnover in levels of ecosystem functioning.

Multimodal 2D and 3D microscopic mapping of growth cartilage by computational imaging techniques – a short review including new research

Being able to image the microstructure of growth cartilage is important for understanding the onset and progression of diseases such as osteochondrosis and osteoarthritis, as well as for developing new treatments and implants. Studies of cartilage using conventional optical brightfield microscopy rely heavily on histological staining, where the added chemicals provide tissue-specific colours. Other microscopy contrast mechanisms include polarization, phase- and scattering contrast, enabling non-stained or ‘label-free’ imaging that significantly simplifies the sample preparation, thereby also reducing the risk of artefacts. Traditional high-performance microscopes tend to be both bulky and expensive. Computational imaging denotes a range of techniques where computers with dedicated algorithms are used as an integral part of the image formation process. Computational imaging offers many advantages like 3D measurements, aberration correction and quantitative phase contrast, often combined with comparably cheap and compact hardware. X-ray microscopy is also progressing rapidly, in certain ways trailing the development of optical microscopy. In this study, we first briefly review the structures of growth cartilage and relevant microscopy characterization techniques, with an emphasis on Fourier ptychographic microscopy (FPM) and advanced x-ray microscopies. We next demonstrate with our own results computational imaging through FPM and compare the images with hematoxylin eosin and saffron (HES)-stained histology. Zernike phase contrast, and the nonlinear optical microscopy techniques of second harmonic generation (SHG) and two-photon excitation fluorescence (TPEF) are explored. Furthermore, X-ray attenuation-, phase- and diffraction-contrast computed tomography (CT) images of the very same sample are presented for comparisons. Future perspectives on the links to artificial intelligence, dynamic studies and in vivo possibilities conclude the article.

Abstract A003: Unwinding the complexities of helicases as compelling drug targets in oncology

Abstract In recent years, helicases have moved to the forefront of research as novel drug targets for a variety of human diseases, including cancer. As critical components of the cell cycle and DNA repair, these enzymes offer an attractive point of therapeutic intervention. Recent bioinformatic endeavours such as the Cancer DepMap have further highlighted the essentiality of these proteins in the progression of numerous cancers. Inhibitors of Werner helicase (WRN) entered Phase I evaluation in 2023, after studies highlighted its role in tumours with microsatellite instability (MSI) and impaired mismatch repair (MMR). Building on these advances, helicases now represent a significant drug target class and are the subject of intense research. However, these targets present significant complexities in their prosecution. Hit finding and robust validation of these putative active compounds remains challenging, with high attrition rates and significant sensitivity to false positives. Resultant hits require thorough biological characterisation via both biochemical and biophysical methods, alongside determination of selectivity against other closely related family members, to increase confidence in their provenance before commencing a detailed drug discovery project. To exemplify these approaches, we have profiled three commercially available WRN inhibitors from two chemical series through a selection of biochemical and biophysical assays. The three compounds exhibit excellent selectivity against 6 distinct helicase/translocase targets. In Michaelis-Menten mechanism of Inhibition studies, we defined contrasting mechanisms of inhibition for the two series and were also able to identify time dependent inhibition with one compound series. Analysing binding interactions of the compounds against protein and protein:substrate complexes, via a fluorescent thermal shift assay (FTSA) and microscale thermophoresis (MST), enabled us to further understand and interrogate the mechanism of inhibition. Herein, we describe some of our experiences, learnings and best practices when prosecuting these compelling, but challenging, targets. Citation Format: Yael Mamane, Zoe Caple, Katie Chapman, Ian Henderson, Allan Jordan, Vanessa Lyne, Cynthia Okoye, Laurent Rigoreau, Ganesh Kadamur, Stuart Thomson, Chris Tomlinson, Jana Wolf. Unwinding the complexities of helicases as compelling drug targets in oncology [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Expanding and Translating Cancer Synthetic Vulnerabilities; 2024 Jun 10-13; Montreal, Quebec, Canada. Philadelphia (PA): AACR; Mol Cancer Ther 2024;23(6 Suppl):Abstract nr A003.

Machine learning assisted mid-infrared spectrochemical fibrillar collagen imaging in clinical tissues.

Label-free multimodal imaging methods that can provide complementary structural and chemical information from the same sample are critical for comprehensive tissue analyses. These methods are specifically needed to study the complex tumor-microenvironment where fibrillar collagen's architectural changes are associated with cancer progression. To address this need, we present a multimodal computational imaging method where mid-infrared spectral imaging (MIRSI) is employed with second harmonic generation (SHG) microscopy to identify fibrillar collagen in biological tissues. To demonstrate a multimodal approach where a morphology-specific contrast mechanism guides a mid-infrared spectral imaging method to detect fibrillar collagen based on its chemical signatures. We trained a supervised machine learning (ML) model using SHG images as ground truth collagen labels to classify fibrillar collagen in biological tissues based on their mid-infrared hyperspectral images. Five human pancreatic tissue samples (sizes are in the order of millimeters) were imaged by both MIRSI and SHG microscopes. In total, 2.8 million MIRSI spectra were used to train a random forest (RF) model. The remaining 68 million spectra were used to validate the collagen images generated by the RF-MIRSI model in terms of collagen segmentation, orientation, and alignment. Compared to the SHG ground truth, the generated MIRSI collagen images achieved a high average boundary F-score (0.8 at 4 pixels threshold) in the collagen distribution, high correlation (Pearson's R 0.82) in the collagen orientation, and similarly high correlation (Pearson's R 0.66) in the collagen alignment. We showed the potential of ML-aided label-free mid-infrared hyperspectral imaging for collagen fiber and tumor microenvironment analysis in tumor pathology samples.

Automatic segmentation of medial temporal lobe subregions in multi-scanner, multi-modality MRI of variable quality.

Volumetry of subregions in the medial temporal lobe (MTL) computed from automatic segmentation in MRI can track neurodegeneration in Alzheimer's disease. However, image quality may vary in MRI. Poor quality MR images can lead to unreliable segmentation of MTL subregions. Considering that different MRI contrast mechanisms and field strengths (jointly referred to as "modalities" here) offer distinct advantages in imaging different parts of the MTL, we developed a muti-modality segmentation model using both 7 tesla (7T) and 3 tesla (3T) structural MRI to obtain robust segmentation in poor-quality images. MRI modalities including 3T T1-weighted, 3T T2-weighted, 7T T1-weighted and 7T T2-weighted (7T-T2w) of 197 participants were collected from a longitudinal aging study at the Penn Alzheimer's Disease Research Center. Among them, 7T-T2w was used as the primary modality, and all other modalities were rigidly registered to the 7T-T2w. A model derived from nnU-Net took these registered modalities as input and outputted subregion segmentation in 7T-T2w space. 7T-T2w images most of which had high quality from 25 selected training participants were manually segmented to train the multi-modality model. Modality augmentation, which randomly replaced certain modalities with Gaussian noise, was applied during training to guide the model to extract information from all modalities. To compare our proposed model with a baseline single-modality model in the full dataset with mixed high/poor image quality, we evaluated the ability of derived volume/thickness measures to discriminate Amyloid+ mild cognitive impairment (A+MCI) and Amyloid- cognitively unimpaired (A-CU) groups, as well as the stability of these measurements in longitudinal data. The multi-modality model delivered good performance regardless of 7T-T2w quality, while the single-modality model under-segmented subregions in poor-quality images. The multi-modality model generally demonstrated stronger discrimination of A+MCI versus A-CU. Intra-class correlation and Bland-Altman plots demonstrate that the multi-modality model had higher longitudinal segmentation consistency in all subregions while the single-modality model had low consistency in poor-quality images. The multi-modality MRI segmentation model provides an improved biomarker for neurodegeneration in the MTL that is robust to image quality. It also provides a framework for other studies which may benefit from multimodal imaging.

Soil organic matter components and characteristics of forest soil in spruce and sycamore plantations in the temperate region

The stability of soil organic matter (SOM) that governs soil organic carbon (SOC) storage depends on its characteristics and components, but little is known about how tree species in forest ecosystems affect SOM components and characteristics. In this study, we used FTIR spectroscopy to investigate plantations of two ecologically and economically significant tree species—namely, spruce (Picea spp.) and sycamore (Acer pseudoplatanus)—in order to determine how the different litter inputs and root-microbe interactions of these two plantations affect the functional groups, components, and characteristics of their SOM. Soil samples were taken from the topsoil (0–10 cm) and subsoil (10–20 cm). In the 0–10 cm soil depth, the SOM's hydrophilic, hydrophobic, and aromatic components differ between the spruce and sycamore plantations. The hydrophobic components constitute the primary constituents of the SOM of the two forest plantations, in contrast to the expected predominance of the hydrophilic component of the SOM. Also, the high hydrophobicity (hydrophilic/hydrophobic) in the subsoil of the spruce plantations was attributed to a decrease in hydrophilic components and a subsequent increase in hydrophobic components of the SOM. The sycamore plantations exhibited a higher SOM aromaticity and a greater degree of decomposition than the spruce plantations. The aforementioned distinctions emphasise the contrasting mechanisms involved in transforming and turnover of the two-tree species' soil organic matter (SOM).

A Dual-Gene Reporter-Amplifier Architecture for Enhancing the Sensitivity of Molecular MRI by Water Exchange.

The development of genetic reporters for magnetic resonance imaging (MRI) is essential for investigating biological functions in vivo. However, current MRI reporters have low sensitivity, making it challenging to create significant contrast against the tissue background, especially when only a small fraction of cells express the reporter. To overcome this limitation, we developed an approach for amplifying the sensitivity of molecular MRI by combining a chemogenetic contrast mechanism with a biophysical approach to increase water diffusion through the co-expression of a dual-gene construct comprising an organic anion transporting polypeptide, Oatp1b3, and a water channel, Aqp1. We first show that the expression of Aqp1 amplifies MRI contrast in cultured cells engineered to express Oatp1b3. We demonstrate that the contrast amplification is caused by Aqp1-driven increase in water exchange, which provides the gadolinium ions internalized by Oatp1b3-expressing cells with access to a larger water pool compared with exchange-limited conditions. We further show that our methodology allows cells to be detected using approximately 10-fold lower concentrations of gadolinium than that in the Aqp1-free scenario. Finally, we show that our approach enables the imaging of mixed-cell cultures containing a low fraction of Oatp1b3-labeled cells that are undetectable on the basis of Oatp1b3 expression alone.

SCCAM: Supervised Contrastive Convolutional Attention Mechanism for Ante-Hoc Interpretable Fault Diagnosis With Limited Fault Samples.

In real industrial processes, fault diagnosis methods are required to learn from limited fault samples since the procedures are mainly under normal conditions and the faults rarely occur. Although attention mechanisms have become increasingly popular for the task of fault diagnosis, the existing attention-based methods are still unsatisfying for the above practical applications. First, pure attention-based architectures like transformers need a substantial quantity of fault samples to offset the lack of inductive biases thus performing poorly under limited fault samples. Moreover, the poor fault classification dilemma further leads to the failure of the existing attention-based methods to identify the root causes. To develop a solution to the aforementioned problems, we innovatively propose a supervised contrastive convolutional attention mechanism (SCCAM) with ante-hoc interpretability, which solves the root cause analysis problem under limited fault samples for the first time. First, accurate classification results are obtained under limited fault samples. More specifically, we integrate the convolutional neural network (CNN) with attention mechanisms to provide strong intrinsic inductive biases of locality and spatial invariance, thereby strengthening the representational power under limited fault samples. In addition, we ulteriorly enhance the classification capability of the SCCAM method under limited fault samples by employing the supervised contrastive learning (SCL) loss. Second, a novel ante-hoc interpretable attention-based architecture is designed to directly obtain the root causes without expert knowledge. The convolutional block attention module (CBAM) is utilized to directly provide feature contributions behind each prediction thus achieving feature-level explanations. The proposed SCCAM method is testified on a continuous stirred tank heater (CSTH) and the Tennessee Eastman (TE) industrial process benchmark. Three common fault diagnosis scenarios are covered, including a balanced scenario for additional verification and two scenarios with limited fault samples (i.e., imbalanced scenario and long-tail scenario). The effectiveness of the presented SCCAM method is evidenced by the comprehensive results that show our method outperforms the state-of-the-art methods in terms of fault classification and root cause analysis.

Contrasting physical mechanisms of yellowfin tuna fluctuations between the western and eastern Indian Ocean

Contrasting physical mechanisms of yellowfin tuna fluctuations between the western and eastern Indian Ocean

Contrasting defence mechanisms against spider mite infestation in cyanogenic and non-cyanogenic legumes

Understanding the complex interactions between plants and herbivores is essential for improving crop resistance. Aiming to expand the role of cyanogenesis in plant defence, we investigated the response of the cyanogenic Phaseolus lunatus (lima bean) and the non-cyanogenic Phaseolus vulgaris (common bean) to Tetranychus urticae (spider mite) infestation. Despite mite infesting both legumes, leaf damage infringed by this feeder was reduced in lima bean. Comparative transcriptome analyses revealed that both species exhibited substantial metabolic and transcriptional changes upon infestation, although alterations in P. lunatus were significantly more pronounced. Specific differences in amino acid homeostasis and key genes associated with the cyanogenic pathway were observed in these species, as well as the upregulation of the mandelonitrile lyase gene (PlMNL1) following T. urticae feeding. Concomitantly, the PIMNL1 activity increased. Lima bean plants also displayed an induction of β-cyanoalanine synthase (PlCYSC1), a key enzyme for cyanide detoxification, suggesting an internal regulatory mechanism to manage the toxicity of their defence responses. These findings contribute to our understanding of the legume-herbivore interactions and underscore the potential role of cyanogenesis in the elaboration of specific defensive responses, even within the same genus, which may reflect distinctive evolutionary adaptations or varying metabolic capabilities between species.

Colour Image Enhancement Model of Retinal Fundus Image for Diabetic Retinopathy Recognition

Diabetic retinopathy (DR) features are typically identified through ophthalmologist eye examinations, but these images often facechallenges like low contrast, non-uniform illumination, and colour inconsistency, affecting the diagnosis accuracy. Therefore, this studyintroduces two novel techniques to improve image quality. One is applying colour image processing techniques to original retinalfundus images, overcoming existing algorithm limitations. Firstly, a new colour correction algorithm was proposed based on TunedBrightness Controlled Single-Scale Retinex (TBCSSR) named Fuzzy TBCSSR Histogram Matching (fTBCSSRhm) to address the issue of colour inconsistency in the dataset. Secondly, based on hybrid particle swarm optimisation-contrast stretch (HPSOCS), the hybridof TBCSSR and HPSOCS named eTBCSSR-HPSOCS algorithm is introduced to tackle the limitations of the standard Particle SwarmOptimisation (PSO) algorithm in HPSOCS, which is prone to local optima and exhibits low convergence rates. This technique combinesthe L-component of the LAB colour model with an enhanced velocity mechanism in PSO and contrast stretching (lavHPSOCS). Its goalis to fine-tune parameters automatically, reduce over-enhancement, avoid unwanted artefacts, and preserve intricate details. This approach improves optimisation by balancing exploration and exploitation and refining velocity control. The proposed algorithm underwent both qualitative and quantitative evaluations. Tests on 100 retinal fundus images from primary datasets were performed to benchmark the algorithm against three existing approaches. The results show that the qualitative performance of the proposed enhancement is more favourable to ophthalmologist specialists than other images. Quantitatively, eTBCSSR-HPSOCS outperforms others with the lowest mean squared error (MSE) of 42.72859, the highest peak signal-to-noise ratio (PSNR) of 32.768, and entropy of 0.977.

LRFE-CL: A self-supervised fusion network for infrared and visible image via low redundancy feature extraction and contrastive learning

Aiming to amalgamate the distinct yet complementary attributes inherent to infrared and visible spectra, Infrared and Visible Image Fusion (IVIF) endeavors to preserve an optimal intensity distribution while curtailing the superfluous data within the resultant image. Nonetheless, it is an incontrovertible reality that the majority of extant methods are prone to engendering an undesirable redundancy of features, in addition to a marked degradation throughout the fusion process. To address the issue, we proposed a self-supervised fusion network for infrared and visible image via low redundancy feature extraction and contrastive learning, termed as LRFE-CL. Specifically, the Low Redundancy Feature Extraction Network (SSFN-LRAE) has been developed to effectively extract low-redundancy features from infrared and visible images using the self-supervised strategy and the Channel Shift Attention Module (CSAM). Subsequently, the Contrastive Fusion Decoder (CFDN) is designed to fuse multi-layer features extracted from the SSFN-LRAE. To prevent feature degradation, a novel regularized contrastive loss mechanism targeting multi-layer features has been formulated to optimize our CFDN, ensuring the fusion of comprehensive texture details and brightness information. Our comprehensive experimental results unequivocally demonstrate the exceptional performance of our method, both in terms of visual effects and fusion performance.