Mechanisms and molecular approaches for heat tolerance in rice (Oryza sativa L.) under climate change scenario

Rice, a staple cereal crop in many parts of the world, has been confronted with multiple environmental stresses including high temperature, negatively impacts the booting as well as anthesis growth stages. The situation is further complicated by the changing climatic conditions, resulting in gradual escalation of temperature as well as changing the rainfall pattern and frequency, thus raising a concern of food security worldwide. The situation can be combat by developing rice varieties with excellent genetics with improved morpho-physiological, biochemical, and molecular mechanisms, together can minimize the adverse effects of heat stress. Here, several strategies(encompassing genetic and genomic, and mechanisms involved) for mitigating the impact of high temperature on rice have been discussed. Finally, the utilization of genomic knowledge in augmenting the conventional breeding approaches have been comprehensively elaborated to develop heat tolerant germplasm.

Fusion-Based Machine Learning Architecture for Heart Disease Prediction

The contemporary evolution in healthcare technologies plays a considerable and signicant role to improve medical services and save human lives.Heart disease or cardiovascular disease is the most fatal and complex disease which it is hardly to be detected through our naked eyes,as numerous people have been suffering from this disease globally.Heart attacks occur when the ranges of vital signs such as blood pressure,pulse rate,and body temperature exceed their normal values.The efcient diagnosis of heart diseases could play a substantial role in the eld of cardiology,while diagnostic time could be reduced.It has been a key challenge for researchers and medical experts to diagnose heart diseases accurately and timely.Therefore,machine learning-based techniques are used for the diagnosis with higher accuracy,using datasets compiled from former medical patients’reports.In recent years,numerous studies have been presented in the literature propose machine learning techniques for diagnosing heart diseases.However,the existing techniques have some limitations in terms of their accuracy.In this paper,a novel Support Vector Machine(SVM)based architecture for heart disease prediction,empowered with a fuzzy based decision level fusion,is presented.The SVMbased architecture has improved the accuracy signicantly as compared to existing solutions,where 96.23%accuracy has been achieved.

Deep Learning Based Classification of Wrist Cracks from X-ray Imaging

Wrist cracks are the most common sort of cracks with an excessive occurrence rate.For the routine detection of wrist cracks,conventional radiography(X-ray medical imaging)is used but periodically issues are presented by crack depiction.Wrist cracks often appear in the human arbitrary bone due to accidental injuries such as slipping.Indeed,many hospitals lack experienced clinicians to diagnose wrist cracks.Therefore,an automated system is required to reduce the burden on clinicians and identify cracks.In this study,we have designed a novel residual network-based convolutional neural network(CNN)for the crack detection of the wrist.For the classification of wrist cracks medical imaging,the diagnostics accuracy of the RN-21CNN model is compared with four well-known transfer learning(TL)models such as Inception V3,Vgg16,ResNet-50,and Vgg19,to assist the medical imaging technologist in identifying the cracks that occur due to wrist fractures.The RN-21CNN model achieved an accuracy of 0.97 which is much better than its competitor`s approaches.The results reveal that implementing a correct generalization that a computer-aided recognition system precisely designed for the assistance of clinician would limit the number of incorrect diagnoses and also saves a lot of time.

Osteoporosis Prediction for Trabecular Bone using Machine Learning: A Review

Trabecular bone holds the utmost importance due to its significance regarding early bone loss.Diseases like osteoporosis greatly affect the structure of the Trabecular bone which results in different outcomes like high risk of fracture.The objective of this paper is to inspect the characteristics of the Trabecular Bone by using the Magnetic Resonance Imaging(MRI)technique.These characteristics prove to be quite helpful in studying different studies related to Trabecular bone such as osteoporosis.The things that were considered before the selection of the articles for the systematic review were language,research field,and electronic sources.Only those articles written in the English language were selected as it is the most prominent language used in scientific,engineering,computer science,and biomedical researches.This literature review was conducted on the articles published between 2006 and 2020.A total of 62 research papers out of 1050 papers were extracted which were according to our topic of review after screening abstract and article content for the title and abstract screening.The findings from those researches were compiled at the end of the result section.This systematic literature review presents a comprehensive report on scientific researches and studies that have been done in the medical area concerning trabecular bone.

A Weighted Spatially Constrained Finite Mixture Model for Image Segmentation

Spatially Constrained Mixture Model(SCMM)is an image segmentation model that works over the framework of maximum a-posteriori and Markov Random Field(MAP-MRF).It developed its own maximization step to be used within this framework.This research has proposed an improvement in the SCMM’s maximization step for segmenting simulated brain Magnetic Resonance Images(MRIs).The improved model is named as the Weighted Spatially Constrained Finite Mixture Model(WSCFMM).To compare the performance of SCMM and WSCFMM,simulated T1-Weighted normal MRIs were segmented.A region of interest(ROI)was extracted from segmented images.The similarity level between the extracted ROI and the ground truth(GT)was found by using the Jaccard and Dice similarity measuring method.According to the Jaccard similarity measuring method,WSCFMM showed an overall improvement of 4.72%,whereas the Dice similarity measuring method provided an overall improvement of 2.65%against the SCMM.Besides,WSCFMM signicantly stabilized and reduced the execution time by showing an improvement of 83.71%.The study concludes that WSCFMM is a stable model and performs better as compared to the SCMM in noisy and noise-free environments.

Hep-Pred: Hepatitis C Staging Prediction Using Fine Gaussian SVM

Hepatitis C is a contagious blood-borne infection,and it is mostly asymptomatic during the initial stages.Therefore,it is difficult to diagnose and treat patients in the early stages of infection.The disease’s progression to its last stages makes diagnosis and treatment more difficult.In this study,an AI system based on machine learning algorithms is presented to help healthcare professionals with an early diagnosis of hepatitis C.The dataset used for our Hep-Pred model is based on a literature study,and includes the records of 1385 patients infected with the hepatitis C virus.Patients in this dataset received treatment dosages for the hepatitis C virus for about 18 months.A former study divided the disease into four main stages.These stages have proven helpful for doctors to analyze the liver’s condition.The traditional way to check the staging is the biopsy,which is a painful and time-consuming process.This article aims to provide an effective and efficient approach to predict hepatitis C staging.For this purpose,the proposed technique uses a fine Gaussian SVM learning algorithm,providing 97.9%accurate results.

Spatial Correlation Module for Classification of Multi-Label Ocular Diseases Using Color Fundus Images

To prevent irreversible damage to one’s eyesight,ocular diseases(ODs)need to be recognized and treated immediately.Color fundus imaging(CFI)is a screening technology that is both effective and economical.According to CFIs,the early stages of the disease are characterized by a paucity of observable symptoms,which necessitates the prompt creation of automated and robust diagnostic algorithms.The traditional research focuses on image-level diagnostics that attend to the left and right eyes in isolation without making use of pertinent correlation data between the two sets of eyes.In addition,they usually only target one or a few different kinds of eye diseases at the same time.In this study,we design a patient-level multi-label OD(PLML_ODs)classification model that is based on a spatial correlation network(SCNet).This model takes into consideration the relevance of patient-level diagnosis combining bilateral eyes and multi-label ODs classification.PLML_ODs is made up of three parts:a backbone convolutional neural network(CNN)for feature extraction i.e.,DenseNet-169,a SCNet for feature correlation,and a classifier for the development of classification scores.The DenseNet-169 is responsible for retrieving two separate sets of attributes,one from each of the left and right CFI.After then,the SCNet will record the correlations between the two feature sets on a pixel-by-pixel basis.After the attributes have been analyzed,they are integrated to provide a representation at the patient level.Throughout the whole process of ODs categorization,the patient-level representation will be used.The efficacy of the PLML_ODs is examined using a soft margin loss on a dataset that is readily accessible to the public,and the results reveal that the classification performance is significantly improved when compared to several baseline approaches.

Enhanced Solar Water Desalination by CuCo_(2)S_(4)-decorated Carbon Foam Derived from Waste Plastics

Interfacial solar desalination is an emerging technology for freshwater production,but the finding of novel solar evaporators is still challenging.In the present research,graphitic carbon foam(CF)was synthesized from the upcycling of waste plastic polyethylene terephthalate(PET)waste bottles functionalized with carrollite CuCo_(2)S_(4) as a photothermal layer.Analytical characterization[X-ray diffraction(XRD),Fourier transform infrared spectroscopy(FTIR),and scanning electron microscopy-energy dispersive spectroscopy(SEM-EDS)]confirms the functionalization of carrollite CuCo_(2)S_(4) on graphitic carbon foam.The UV-Vis spectroscopy analysis showed an enhanced optical absorption in the UV-Vis-near IR region(>96%)for functionalized CuCo_(2)S_(4)-CF foam compared to carbon foam(67%).The interfacial solar desalination experiment presented a significantly enhanced evaporation rate of 2.4 kg·m^(−2)·h^(−1) for CuCo_(2)S_(4)-CF compared to that of CF(1.60 kg·m^(−2)·h^(−1))and that of CuCo_(2)S_(4)(1.60 kg·m^(−2)·h^(−1)).The obtained results proved that the newly synthesized CuCo_(2)S_(4)-CF from the upcycled plastic into new material for the photothermal desalination process can enhance the practice of a circular economy to produce fresh water.

Advances in two-dimensional molybdenum ditelluride(MoTe2):A comprehensive review of properties,preparation methods,and applications

In the past decade,molybdenum ditelluride(MoTe2)has received significant attention from the scientific community due to its structural features and unique properties originate from them.In the current review,the properties,various preparation approaches,and versatile applications of MoTe2 are presented.The review provides a brief update on the state of our fundamental understanding of MoTe2 material and also discusses the issues that need to be resolved.To introduce MoTe2,we briefly summarize its structural,optoelectronic,magnetic,and mechanical properties in the beginning.Then,different preparation meth-ods of MoTe2,such as exfoliation,laser treatment,deposition,hydrothermal,microwave,and molecular beam epitaxy,are included.The excellent electri-cal conductivity,strong optical activity,tunable bandgap,high sensitivity,and impressive stability make it an ideal contender for different applications,includ-ing energy storage,catalysis,sensors,solar cells,photodetectors,and transistors.The performance of MoTe2 in these applications is systematically introduced along with mechanistic insights.At the end of the article,the challenges and possible future directions are highlighted to further modify MoTe2 material for the numerous functionalities.Therefore,the availability of different phases and layer structures implies a potential for MoTe2 to lead an era of two-dimensional materials that began from the exfoliation of graphene.

Fungal diversity notes 1611-1716: taxonomic and phylogenetic contributions on fungal genera and species emphasis in south China

This article is the 15th contribution in the Fungal Diversity Notes series,wherein 115 taxa from three phyla,nine classes,28 orders,48 families,and 64 genera are treated.Fungal taxa described and illustrated in the present study include a new family,five new genera,61 new species,five new combinations,one synonym,one new variety and 31 records on new hosts or new geographical distributions.Ageratinicolaceae fam.nov.is introduced and accommodated in Pleosporales.The new genera introduced in this study are Ageratinicola,Kevinia,Pseudomultiseptospora(Parabambusicolaceae),Marasmiellomycena,and Vizzinia(Porotheleaceae).Newly described species are Abrothallus altoandinus,Ageratinicola kunmingensis,Allocryptovalsa aceris,Allophoma yuccae,Apiospora cannae,A.elliptica,A.pallidesporae,Boeremia wisteriae,Calycina papaeana,Clypeo-coccum lichenostigmoides,Coniochaeta riskali-shoyakubovii,Cryphonectria kunmingensis,Diaporthe angustiapiculata,D.campylandrae,D.longipapillata,Diatrypella guangdongense,Dothiorella franceschinii,Endocalyx phoenicis,Epicoc-cum terminosporum,Fulvifomes karaiensis,F.pannaensis,Ganoderma ghatensis,Hysterobrevium baoshanense,Inocybe avellaneorosea,I.lucida,Jahnula oblonga,Kevinia lignicola,Kirschsteiniothelia guangdongensis,Laboulbenia caprina,L.clavulata,L.cobiae,L.cosmodisci,L.nilotica,L.omalii,L.robusta,L.similis,L.stigmatophora,Laccaria rubriporus,Lasiodiplodia morindae,Lyophyllum agnijum,Marasmiellomycena pseudoomphaliiformis,Melomastia beihaiensis,Nemania guangdongensis,Nigrograna thailandica,Nigrospora ficuum,Oxydothis chinensis,O.yunnanensis,Petriella thailandica,Phaeoacremonium chinensis,Phialocephala chinensis,Phytophthora debattistii,Polyplosphaeria nigrospora,Pronectria loweniae,Seriascoma acutispora,Setoseptoria bambusae,Stictis anomianthi,Tarzetta tibetensis,Tarzetta urceolata,Tetraploa obpyriformis,Trichoglossum beninense,and Tricoderma pyrrosiae.We provide an emendation for Urnula ailaoshanensis Agaricus duplocingulatoides var.brevisporus introduced as a new variety based on morphology and phylogeny.

Microcosm study on fate and dynamics of mangrove tannins during leaf litter leaching

Background Mangrove tannins can participate in wetland biogeochemical cycling.However,their fate and dynamics during leaf litter leaching have yet to be elucidated in coastal aquatic environments.Methods By using a simulated microcosm experiment,changes in leaf litter mass and tannin compounds,including total phenolics(TP),extractable condensed tannins(ECT),and bound condensed tannins(BCT),were examined in the litters and leachates of four common mangrove species:Kandelia obovata(Ko),Aegiceras corniculatum(Ac),Sonneratia apetala(Sa),and Avicennia marina(Am).Results Leaching caused a notable decline in litter mass,TP,and ECT in the leaf litter of Ko,Ac,Sa,and Am,while BCT increased significantly in Ko and Ac.Loss in foliar TP was higher than in leaf litter mass,and loss in foliar ECT was higher than in TP of Ko and Ac,but Sa showed the opposite result.The temporal changes of TP and ECT concentrations in leachates followed a similar trend,with an initial increase followed by a decrease.ECT dynamics in Ko and Ac leachates correlated with their TP concentrations,while in Sa leachate,the peak ECT occurred 72 h later than its TP peak.The leachate ECT concentrations were highest in Ac,followed by Ko,and significantly lower in Sa.The peak TP and ECT proportions in leachates accounted for 9.2-23.9%and 7.7-9.4%of the total decreases in foliar TP and ECT,respectively.Conclusion During the leaching process,tannins’fate was species-specific,while the dynamics were almost similar.