IRv2-Net: A Deep Learning Framework for Enhanced Polyp Segmentation Performance Integrating InceptionResNetV2 and UNet Architecture with Test Time Augmentation Techniques.

Colorectal polyps in the colon or rectum are precancerous growths that can lead to a more severe disease called colorectal cancer. Accurate segmentation of polyps using medical imaging data is essential for effective diagnosis. However, manual segmentation by endoscopists can be time-consuming, error-prone, and expensive, leading to a high rate of missed anomalies. To solve this problem, an automated diagnostic system based on deep learning algorithms is proposed to find polyps. The proposed IRv2-Net model is developed using the UNet architecture with a pre-trained InceptionResNetV2 encoder to extract most features from the input samples. The Test Time Augmentation (TTA) technique, which utilizes the characteristics of the original, horizontal, and vertical flips, is used to gain precise boundary information and multi-scale image features. The performance of numerous state-of-the-art (SOTA) models is compared using several metrics such as accuracy, Dice Similarity Coefficients (DSC), Intersection Over Union (IoU), precision, and recall. The proposed model is tested on the Kvasir-SEG and CVC-ClinicDB datasets, demonstrating superior performance in handling unseen real-time data. It achieves the highest area coverage in the area under the Receiver Operating Characteristic (ROC-AUC) and area under Precision-Recall (AUC-PR) curves. The model exhibits excellent qualitative testing outcomes across different types of polyps, including more oversized, smaller, over-saturated, sessile, or flat polyps, within the same dataset and across different datasets. Our approach can significantly minimize the number of missed rating difficulties. Lastly, a graphical interface is developed for producing the mask in real-time. The findings of this study have potential applications in clinical colonoscopy procedures and can serve based on further research and development.

Comparative metabolomic profiling of Arabidopsis thaliana roots and leaves reveals complex response mechanisms induced by a seaweed extract.

Seaweed extracts are a prominent class of biostimulants that enhance plant health and tolerance to biotic and abiotic stresses due to their unique bioactive components. However, the mechanisms of action of biostimulants are still unknown. Here, we have used a metabolomic approach, a UHPLC-MS method, to uncover the mechanisms induced following application to Arabidopsis thaliana of a seaweed extract derived from Durvillaea potatorum and Ascophyllum nodosum. We have identified, following the application of the extract, key metabolites and systemic responses in roots and leaves across 3 timepoints (0, 3, 5 days). Significant alterations in metabolite accumulation or reduction were found for those belonging to broad groups of compounds such as lipids, amino acids, and phytohormones; and secondary metabolites such as phenylpropanoids, glucosinolates, and organic acids. Strong accumulations of TCA cycle and N-containing and defensive metabolites such as glucosinolates were also found revealing the enhancement of carbon and nitrogen metabolism and defence systems. Our study has demonstrated that application of seaweed extract dramatically altered the metabolomic profiles of Arabidopsis and revealed differences in roots and leaves that varied across the timepoints tested. We also show clear evidence of systemic responses that were initiated in the roots and resulted in metabolic alterations in the leaves. Collectively, our results suggest that this seaweed extract promotes plant growth and activates defence systems by altering various physiological processes at the individual metabolite level.

Feasibility study of Mg storage in a bilayer silicene anode via application of an external electric field.

With the goal of developing a Si-based anode for Mg-ion batteries (MIBs) that is both efficient and compatible with the current semiconductor industry, the current research utilized classical Molecular Dynamics (MD) simulation in investigating the intercalation of a Mg2+ ion under an external electric field (E-field) in a 2D bilayer silicene anode (BSA). First principles density functional theory calculations were used to validate the implemented EDIP potentials. Our simulation shows that there exists an optimum E-field value in the range of 0.2-0.4 V Å-1 for Mg2+ intercalation in BSA. To study the effect of the E-field on Mg2+ ions, an exhaustive spread of investigations was carried out under different boundary conditions, including calculations of mean square displacement (MSD), interaction energy, radial distribution function (RDF), and trajectory of ions. Our results show that the Mg2+ ions form a stable bond with Si in BSA. The effects of E-field direction and operating temperature were also investigated. In the X-Y plane in the 0°-45° range, 15° from the X-direction was found to be the optimum direction for intercalation. The results of this work also suggest that BSA does not undergo drastic structural changes during the charging cycles with the highest operating temperature being ∼300 K.

Machine Learning Approach to Delineate the Impact of Material Properties on Solar Cell Device Physics.

In this research, solar cell capacitance simulator-one-dimensional (SCAPS-1D) software was used to build and probe nontoxic Cs-based perovskite solar devices and investigate modulations of key material parameters on ultimate power conversion efficiency (PCE). The input material parameters of the absorber Cs-perovskite layer were incrementally changed, and with the various resulting combinations, 63,500 unique devices were formed and probed to produce device PCE. Versatile and well-established machine learning algorithms were thereafter utilized to train, test, and evaluate the output dataset with a focused goal to delineate and rank the input material parameters for their impact on ultimate device performance and PCE. The most impactful parameters were then tuned to showcase unique ranges that would ultimately lead to higher device PCE values. As a validation step, the predicted results were confirmed against SCAPS simulated results as well, highlighting high accuracy and low error metrics. Further optimization of intrinsic material parameters was conducted through modulation of absorber layer thickness, back contact metal, and bulk defect concentration, resulting in an improvement in the PCE of the device from 13.29 to 16.68%. Overall, the results from this investigation provide much-needed insight and guidance for researchers at large, and experimentalists in particular, toward fabricating commercially viable nontoxic inorganic perovskite alternatives for the burgeoning solar industry.

Supervised Machine Learning-Aided SCAPS-Based Quantitative Analysis for the Discovery of Optimum Bromine Doping in Methylammonium Tin-Based Perovskite (MASnI3-xBrx).

In this investigation, supervised machine learning (ML) was utilized to accurately predict the optimum bromine doping concentration in single-junction MASnI3-xBrx devices. Data-driven optimizations were carried out on 42 000 unique devices built utilizing a solar cell capacitance simulator (SCAPS). The devices were investigated through variations of bromine doping %, bandgap, electron affinity, series resistance, back-contact metal, and acceptor concentration─parameters that were specifically chosen because of their tunable nature and ability to be modified through facile experimental fabrication techniques of the device. Five different algorithms were utilized to explore feature engineering. The first step before bromine doping within the device included validation studies of a pure tin-based system, MASnI3: a power conversion efficiency (PCE) of 6.71% was achieved, having close congruence with experimental data. ML analyses for optimal bromine doping resulted in the discovery of two devices with bromine concentrations of 22.43% (Br22) and 25.63% (Br25), with the latter being a more fine-tuned value obtained through extra rigorous analysis. To understand the total and relative impact of each feature on power conversion efficiency (PCE), Br22 and Br25 were analyzed with a state-of-the-art algorithm, namely, the SHapley Additive exPlanations (SHAP) algorithm. Focusing on the two discovered devices, further device optimizations were carried out utilizing SCAPS. Modulations of absorber thickness, bulk and interfacial defect density, and choice of electron transport layer (ETL) and hole transport layer (HTL) materials were tried. Device stability was analyzed through carrier lifetime studies. Following these optimization steps, Br22 and Br25 demonstrated final high PCE values of 20.72 and 17.37%, respectively. The ML-assisted quantitative analysis of the current work provides significant confidence for optimal bromine-doped tin-based devices to be considered as viable and competitive nontoxic alternatives to traditional technologies.

Seaweed Extract-Stimulated Priming in Arabidopsis thaliana and Solanum lycopersicum.

Plant priming is an induced physiological state where plants are protected from biotic and abiotic stresses. Whether seaweed extracts promote priming is largely unknown as is the mechanism by which priming may occur. In this study, we examined the effect of a seaweed extract (SWE) on two distinct stages of plant priming (priming phase and post-challenge primed state) by characterising (i) plant gene expression responses using qRT-PCR and (ii) signal transduction responses by evaluating reactive oxygen species (ROS) production. The SWE is made from the brown algae Ascophyllum nodosum and Durvillaea potatorum. The priming phase was examined using both Arabidopsis thaliana and Solanum lycopersicum. At this stage, the SWE up-regulated key priming-related genes, such as those related to systemic acquired resistance (SAR) and activated the production of ROS. These responses were found to be temporal (lasting 3 days). The post-challenge primed state was examined using A. thaliana challenged with a root pathogen. Similarly, defence response-related genes, such as PR1 and NPR1, were up-regulated and ROS production was activated (lasting 5 days). This study found that SWE induces plant priming-like responses by (i) up-regulating genes associated with plant defence responses and (ii) increasing production of ROS associated with signalling responses.

How to Unravel the Key Functions of Cryptic Oomycete Elicitin Proteins and Their Role in Plant Disease.

Pathogens and plants are in a constant battle with one another, the result of which is either the restriction of pathogen growth via constitutive or induced plant defense responses or the pathogen colonization of plant cells and tissues that cause disease. Elicitins are a group of highly conserved proteins produced by certain oomycete species, and their sterol binding ability is recognized as an important feature in sterol-auxotrophic oomycetes. Elicitins also orchestrate other aspects of the interactions of oomycetes with their plant hosts. The function of elicitins as avirulence or virulence factors is controversial and is dependent on the host species, and despite several decades of research, the function of these proteins remains elusive. We summarize here our current understanding of elicitins as either defense-promoting or defense-suppressing agents and propose that more recent approaches such as the use of 'omics' and gene editing can be used to unravel the role of elicitins in host-pathogen interactions. A better understanding of the role of elicitins is required and deciphering their role in host-pathogen interactions will expand the strategies that can be adopted to improve disease resistance and reduce crop losses.

Phaeophyceaean (Brown Algal) Extracts Activate Plant Defense Systems in Arabidopsis thaliana Challenged With Phytophthora cinnamomi.

Seaweed extracts are important sources of plant biostimulants that boost agricultural productivity to meet current world demand. The ability of seaweed extracts based on either of the Phaeophyceaean species Ascophyllum nodosum or Durvillaea potatorum to enhance plant growth or suppress plant disease have recently been shown. However, very limited information is available on the mechanisms of suppression of plant disease by such extracts. In addition, there is no information on the ability of a combination of extracts from A. nodosum and D. potatorum to suppress a plant pathogen or to induce plant defense. The present study has explored the transcriptome, using RNA-seq, of Arabidopsis thaliana following treatment with extracts from the two species, or a mixture of both, prior to inoculation with the root pathogen Phytophthora cinnamomi. Following inoculation, five time points (0-24 h post-inoculation) that represented early stages in the interaction of the pathogen with its host were assessed for each treatment and compared with their respective water controls. Wide scale transcriptome reprogramming occurred predominantly related to phytohormone biosynthesis and signaling, changes in metabolic processes and cell wall biosynthesis, there was a broad induction of proteolysis pathways, a respiratory burst and numerous defense-related responses were induced. The induction by each seaweed extract of defense-related genes coincident with the time of inoculation showed that the plants were primed for defense prior to infection. Each seaweed extract acted differently in inducing plant defense-related genes. However, major systemic acquired resistance (SAR)-related genes as well as salicylic acid-regulated marker genes (PR1, PR5, and NPR1) and auxin associated genes were found to be commonly up-regulated compared with the controls following treatment with each seaweed extract. Moreover, each seaweed extract suppressed P. cinnamomi growth within the roots of inoculated A. thaliana by the early induction of defense pathways and likely through ROS-based signaling pathways that were linked to production of ROS. Collectively, the RNA-seq transcriptome analysis revealed the induction by seaweed extracts of suites of genes that are associated with direct or indirect plant defense in addition to responses that require cellular energy to maintain plant growth during biotic stress.

Rhamnose synthase activity is required for pathogenicity of the vascular wilt fungus Verticillium dahliae.

The initial interaction of a pathogenic fungus with its host is complex and involves numerous metabolic pathways and regulatory proteins. Considerable attention has been devoted to proteins that play a crucial role in these interactions, with an emphasis on so-called effector molecules that are secreted by the invading microbe to establish the symbiosis. However, the contribution of other types of molecules, such as glycans, is less well appreciated. Here, we present a random genetic screen that enabled us to identify 58 novel candidate genes that are involved in the pathogenic potential of the fungal pathogen Verticillium dahliae, which causes vascular wilt diseases in over 200 dicotyledonous plant species, including economically important crops. One of the candidate genes that was identified concerns a putative biosynthetic gene involved in nucleotide sugar precursor formation, as it encodes a putative nucleotide-rhamnose synthase/epimerase-reductase (NRS/ER). This enzyme has homology to bacterial enzymes involved in the biosynthesis of the nucleotide sugar deoxy-thymidine diphosphate (dTDP)-rhamnose, a precursor of L-rhamnose, which has been shown to be required for virulence in several human pathogenic bacteria. Rhamnose is known to be a minor cell wall glycan in fungi and has therefore not been suspected as a crucial molecule in fungal-host interactions. Nevertheless, our study shows that deletion of the VdNRS/ER gene from the V. dahliae genome results in complete loss of pathogenicity on tomato and Nicotiana benthamiana plants, whereas vegetative growth and sporulation are not affected. We demonstrate that VdNRS/ER is a functional enzyme in the biosynthesis of uridine diphosphate (UDP)-rhamnose, and further analysis has revealed that VdNRS/ER deletion strains are impaired in the colonization of tomato roots. Collectively, our results demonstrate that rhamnose, although only a minor cell wall component, is essential for the pathogenicity of V. dahliae.

Active defence by an Australian native host, Lomandra longifolia, provides resistance against Phytophthora cinnamomi.

Resistance is rare against the oomycete plant pathogen Phytophthora cinnamomi Rands. Only a limited number of species have been recorded as field-resistant species in Australia. However, understanding the nature of resistance of those species when grown under controlled conditions is challenging because of their slow growth and the inherent difficulties of working with a root pathogen. We assessed the Australian native species, Lomandra longifolia Labill., as a resistant species by analysing in detail the response of roots to infection by P. cinnamomi in a series of comparative tests with Lupinus angustifolius L., a highly susceptible species. Following inoculation of L. longifolia roots, lesion length and colonisation percentage were significantly less than in roots of the susceptible species. Moreover, there was no statistical difference in root growth rate, whole-plant FW and leaf relative chlorophyll content between controls and inoculated L. longifolia. We then examined three key cellular responses that are related to resistance: the production of the reactive oxygen species, H2O2, callose formation and lignin deposition in L. longifolia roots following inoculation with P. cinnamomi. The upregulation of these resistance-related components in the early hours after inoculation suggested their involvement in resistance and that this is controlled by the coordinated response of multiple components. Resistance assessment and a detailed investigation of cellular resistance components along with gene expression analysis provides a platform for further understanding of the mechanisms of resistance against this generalist pathogen and presents opportunities for manipulating susceptible species for disease resistance.