The Role of Medical Image Modalities and AI in the Early Detection, Diagnosis and Grading of Retinal Diseases: A Survey.

Traditional dilated ophthalmoscopy can reveal diseases, such as age-related macular degeneration (AMD), diabetic retinopathy (DR), diabetic macular edema (DME), retinal tear, epiretinal membrane, macular hole, retinal detachment, retinitis pigmentosa, retinal vein occlusion (RVO), and retinal artery occlusion (RAO). Among these diseases, AMD and DR are the major causes of progressive vision loss, while the latter is recognized as a world-wide epidemic. Advances in retinal imaging have improved the diagnosis and management of DR and AMD. In this review article, we focus on the variable imaging modalities for accurate diagnosis, early detection, and staging of both AMD and DR. In addition, the role of artificial intelligence (AI) in providing automated detection, diagnosis, and staging of these diseases will be surveyed. Furthermore, current works are summarized and discussed. Finally, projected future trends are outlined. The work done on this survey indicates the effective role of AI in the early detection, diagnosis, and staging of DR and/or AMD. In the future, more AI solutions will be presented that hold promise for clinical applications

Prediction Models Based on Soil Characteristics for Evaluation of the Accumulation Capacity of Nine Metals by Forage Sorghum Grown in Agricultural Soils Treated with Varying Amounts of Poultry Manure

Predictive models were generated to evaluate the degree to which nine metals (Cd, Co, Cr, Cu, Fe, Mn, Ni, Pb, and Zn) were absorbed by the leaves, stems and roots of forage sorghum in growing media comprising soil admixed with poultry manure concentrations of 0, 10, 20, 30 and 40 g/kg. The data revealed that the greatest contents of the majority of the metals were evident in the roots rather than in the stems and leaves. A bioaccumulation factor (BAF)  1. Translocation factor values were < 1 for all metals with the exception of Co, Cr and Ni, which displayed values of 1.20, 1.67 and 1.35 for the leaves, and 1.12, 1.23 and 1.24, respectively, for the stems. The soil pH had a negative association with metal tissues in plant parts. A positive relationship was observed with respect to plant metal contents, electrical conductivity and organic matter quantity. The designed models exhibited a high standard of data precision; any variations between the predicted and experimentally observed contents for the nine metals in the three plant tissue components were nonsignificant. Thus, it was concluded that the presented predictive models constitute a pragmatic tool to establish the safety from risk to human well-being with respect to growing forage sorghum when cultivating media fortified with poultry manure.The authors extend their appreciation to the Deputyship for Research and Innovation, Ministry of Education in Saudi Arabia for funding this research work through the project number IFP-KKU-2020/3.Peer reviewe

Transcriptomic analysis of the late stages of grapevine (Vitis vinifera cv. Cabernet Sauvignon) berry ripening reveals significant induction of ethylene signaling and flavor pathways in the skin

Background: Grapevine berry, a nonclimacteric fruit, has three developmental stages; the last one is when berrycolor and sugar increase. Flavors derived from terpenoid and fatty acid metabolism develop at the very end of thisripening stage. The transcriptomic response of pulp and skin of Cabernet Sauvignon berries in the late stages ofripening between 22 and 37 \ub0Brix was assessed using whole-genome micorarrays.Results: The transcript abundance of approximately 18,000 genes changed with \ub0Brix and tissue type. There were alarge number of changes in many gene ontology (GO) categories involving metabolism, signaling and abioticstress. GO categories reflecting tissue differences were overrepresented in photosynthesis, isoprenoid metabolismand pigment biosynthesis. Detailed analysis of the interaction of the skin and pulp with \ub0Brix revealed that therewere statistically significantly higher abundances of transcripts changing with \ub0Brix in the skin that were involved inethylene signaling, isoprenoid and fatty acid metabolism. Many transcripts were peaking around known optimalfruit stages for flavor production. The transcript abundance of approximately two-thirds of the AP2/ERF superfamilyof transcription factors changed during these developmental stages. The transcript abundance of a unique clade ofERF6-type transcription factors had the largest changes in the skin and clustered with genes involved in ethylene,senescence, and fruit flavor production including ACC oxidase, terpene synthases, and lipoxygenases. The transcriptabundance of important transcription factors involved in fruit ripening was also higher in the skin.Conclusions: A detailed analysis of the transcriptome dynamics during late stages of ripening of grapevine berriesrevealed that these berries went through massive transcriptional changes in gene ontology categories involvingchemical signaling and metabolism in both the pulp and skin, particularly in the skin. Changes in the transcriptabundance of genes involved in the ethylene signaling pathway of this nonclimacteric fruit were statisticallysignificant in the late stages of ripening when the production of transcripts for important flavor and aroma compoundswere at their highest. Ethylene transcription factors known to play a role in leaf senescence also appear to play a role infruit senescence. Ethylene may play a bigger role than previously thought in this non-climacteric fruit

Prediction Models Founded on Soil Characteristics for the Estimated Uptake of Nine Metals by Okra Plant, <i>Abelmoschus esculentus</i> (L.) Moench., Cultivated in Agricultural Soils Modified with Varying Sewage Sludge Concentrations

Prediction models were developed to estimate the extent to which the metals Cd, Co, Cr, Cu, Fe, Mn, Ni, Pb, and Zn were taken up by the fruits, the leaves, the stems, and the roots of the okra plant, Abelmoschus esculentus (L.) Moench., grown under greenhouse conditions in soil modified with a spectrum of sewage sludge concentrations: 0, 10, 20, 30, 40, and 50 g/kg. All the metals under investigation, apart from Cd, were more concentrated in the A. esculentus roots than in any other organ. Overall, the sum of the metal concentration (mg/kg) within the varying plant tissues can be ranked in the following order: roots (13,795.5) > leaves (1252.7) > fruits (489.3) > stems (469.6). For five of the metals (i.e., Cd, Co, Fe, Mn, and Pb), the BCF was 1, (i.e., Cr, 1.074; Cu, 1.347; Ni, 1.576; and Zn, 1.031). The metal BCFs were negatively correlated with the pH of the soil and positively correlated with soil OM content. The above-ground tissues exhibited a TF t-tests indicated that any differences between the measured and forecasted concentrations of the nine metals within the four tissue types of A. esculentus failed to reach significance. It can, therefore, be surmised that the prediction models described in the current research form a feasible method with which to determine the safety and risk to human health when cultivating the tested species in soils modified with sewage sludge

Heavy Metal Bioaccumulation, Growth Characteristics, and Yield of Pisum sativum L. Grown in Agricultural Soil-Sewage Sludge Mixtures

The application of sewage sludge (SS) in agriculture is an alternative disposal method for wastewater recycling and soil fertilization. This study evaluated heavy metal bioaccumulation, growth, and yield of Pisum sativum (pea) grown in agricultural soil amended with SS at rates of 0, 10, 20, 30, and 40 g/kg. The results show that root, shoot, pod length, biomass, and number of leaves and pods increased with SS amendments of 10 and 20 g/kg, while rates declined at 30 and 40 g/kg. SS had greater salinity and organic content than the soil. Heavy metals in the postharvest soil samples increased for all SS application rates except Fe and Mo. The significant increase in Cd content started at the lowest amendment rate 10 g/kg; for Co, Mn, and Pb, the significant increase was detected at the highest amendment rate (40 g/kg). Generally, all heavy metals increased significantly in portions of P. sativum except Cd in the shoot. At an amendment rate of 10 g/kg, Co in the shoot and root, Cr in the fruit, Cu in the root, Fe in the fruit, Mn in the shoot and fruit, Mo in the fruit, Pb in the shoot, and Zn in the fruit were elevated significantly. In contrast, the concentrations of Cd in the fruit, Cr in the root, Cu in the shoot, Fe in the shoot and root, Ni in the fruit and root, Pb in the fruit and root, and Zn in the root significantly increased only at the highest rate of 40 g/kg. The highest regression R2 was 0.927 for Mn in pods and the lowest was 0.154 for Cd in shoots. Bioaccumulation and translocation factors were &gt; 1 for Mo and the bioaccumulation of Pb was &gt;1. SS could be used for pea fertilization but only at rates below 20 g/kg to avoid environmental and health hazards

Prediction Models Founded on Soil Characteristics for the Estimated Uptake of Nine Metals by Okra Plant, Abelmoschus esculentus (L.) Moench., Cultivated in Agricultural Soils Modified with Varying Sewage Sludge Concentrations

Prediction models were developed to estimate the extent to which the metals Cd, Co, Cr, Cu, Fe, Mn, Ni, Pb, and Zn were taken up by the fruits, the leaves, the stems, and the roots of the okra plant, Abelmoschus esculentus (L.) Moench., grown under greenhouse conditions in soil modified with a spectrum of sewage sludge concentrations: 0, 10, 20, 30, 40, and 50 g/kg. All the metals under investigation, apart from Cd, were more concentrated in the A. esculentus roots than in any other organ. Overall, the sum of the metal concentration (mg/kg) within the varying plant tissues can be ranked in the following order: roots (13,795.5) &gt; leaves (1252.7) &gt; fruits (489.3) &gt; stems (469.6). For five of the metals (i.e., Cd, Co, Fe, Mn, and Pb), the BCF was &lt;1; for the remaining four metals, the BCF was &gt;1, (i.e., Cr, 1.074; Cu, 1.347; Ni, 1.576; and Zn, 1.031). The metal BCFs were negatively correlated with the pH of the soil and positively correlated with soil OM content. The above-ground tissues exhibited a TF &lt; 1 for all metals, apart from Cd with respect to the leaves (2.003) and the fruits (2.489), and with the exception of Mn in relation to the leaves (1.149). Further positive associations were demonstrated for the concentrations of all the metals in each examined plant tissue and the corresponding soil metal concentration. The tissue uptakes of the nine metals were negatively correlated with soil pH, but positively associated with the OM content in the soil. The generated models showed high performance accuracy; students’ t-tests indicated that any differences between the measured and forecasted concentrations of the nine metals within the four tissue types of A. esculentus failed to reach significance. It can, therefore, be surmised that the prediction models described in the current research form a feasible method with which to determine the safety and risk to human health when cultivating the tested species in soils modified with sewage sludge

Prediction models based on soil properties for evaluating the uptake of eight heavy metals by tomato plant (Lycopersicon esculentum Mill.) grown in agricultural soils amended with sewage sludge

The aim of this study is to design de novo prediction models in order to gauge the likely uptake of eight heavy metals (Al, Cr, Cu, Fe, Mn, Ni, Pb and Zn) by Lycopersicon esculentum, the tomato plant. Uptake was assessed within the plant’s root, stem, leaf and fruit tissues, respectively. The plant was cultivated in soil amended by different application rates of sewage sludge, i.e. 0, 10, 20, 30 and 40 g/kg. The roots exhibited markedly elevated heavy metal concentrations compared to the above-ground plant components, with the exception of the quantity of Ni in the leaves. Apart from Al, Fe and Mn, a bioconcentration factor >1 was identified for all heavy metals. Excluding Ni in the leaves, all tested heavy metals exhibited a translocation factor < 1. The regression models were deployed to predict the accretion of the heavy metals under investigation within the various parts of L. esculentum. These were founded on the parameters of the equivalent eight heavy metals within the soil, pH and organic matter content. Student’s unpaired t-tests revealed no differences between the actual and predicted heavy metal concentrations in the roots, stems, leaves or fruits of the tomato plant. These data suggest an excellent model goodness of fit in terms of its accuracy to forecast the degree of heavy metal uptake. The constructed models may therefore facilitate the safe propagation of L. esculentum in growing media amended with sewage sludge, and concurrently provide a risk assessment with respect to human well-being.The authors extend their appreciation to the Scientific Research Deanship at King Khalid University and the Ministry of Education in Saudi Arabia for funding this research work through the project number IFP-KKU-2020/3.Peer reviewe

A Computer-Aided Diagnostic System for Diabetic Retinopathy Based on Local and Global Extracted Features

Diabetic retinopathy (DR) is a major public health problem and the leading cause of vision loss in the working age population. This paper presents a novel deep learning system for the detection and diagnosis of DR using optical coherence tomography (OCT) images. The input for this system is three-channel local and global information from OCT images. The local high-level information is represented by the thickness channel and the reflectivity channel. The global low-level information is represented by the grey-level OCT original image. The deep learning system processes the three-channel input to produce the final DR diagnoses. Experimental results on 200 OCT images, augmented to 800 images, which are collected by the University of Louisville, show high system performance related to other competing methods. Moreover, 10-fold and leave-one-subject-out (LOSO) experiments are performed to confirm how significant using the fused images is in improving the performance of the diagnoses, by investigating four different CNN architectures. All of the four architectures achieve acceptable performance and confirm a significant performance improvement using the fused images. Using LOSO, the best network performance has improved from 90.1 &plusmn; 2% using only the grey level dataset to 97.7 &plusmn; 0.5% using the proposed fused dataset. These results confirm the promise of using the proposed system for the detection of DR using OCT images