Physicochemical,rheological and antioxidant profiling of yogurt prepared from non-enzymatically and enzymatically hydrolyzed potato powder under refrigeration

Evidences show that the storage period greatly affects the quality of yogurt.In this study,three types of yogurt:control yogurt(CY),non-hydrolyzed potato powder yogurt(PPY)and enzymatically hydrolyzed potato powder yogurt(EHPPY)were prepared at 42℃ for 5 h and stored for 28 days at 4℃.The yogurts were evaluated for quality characteristics at different storage periods.Negligible differences in pH values,titratable acidities and viable counts were detected in all three types of yogurt during storage.However,compared to other yogurts,EHPPY exhibited desirable water holding capacity,throughout the storage period.Apart from this,sensory properties and antioxidant activities(2-diphenyl-1-picryl-hydrazyl(DPPH)free radical scavenging activity and ferric reducing antioxidant power(FRAP))of EHPPY were also significantly improved during the storage period.Furthermore,the storage(G’)and loss(G”)modulus of PPY,EHPPY were lower than CY at 4℃ while a hysteresis loop was shown by all yogurts at the temperature range of 4-50℃ indicating higher G’(elasticity)than G”(viscosity).Based on our findings,EHPP could be an important functional ingredient in improving the quality and storage stability of yogurt for its production at an industrial level.

Interaction of methionine sulfoxide reductase B5 with SlMYC2 stimulates the transcription of MeJA-mediated autophagy-related genes in tomato fruit

Methyl jasmonate(MeJA)has been shown to induce autophagy in various plant stress responses and metabolic pathways.MYC2 is involved in MeJA-mediated postharvest fruit biological metabolism,but it is unclear how it affects MeJA-induced fruit autophagy.In this study,we noticed that silencing SlMYC2 significantly reduced the increase in autophagy-related genes(SlATGs)expression induced by MeJA.SlMYC2 could also bind to the promoters of several SlATGs,including SlATG13a,SlATG13b,SlATG18a,and SlATG18h,and activate their transcript levels.Moreover,SlMsrB5,a methionine sulfoxide reductase,could interact with SlMYC2.Methionine oxidation in SlMYC2 and mimicking sulfoxidation in SlMYC2 by mutation of methionine-542 to glutamine reduced the DNA-binding ability and transcriptional activity of SlMYC2,respectively.SlMsrB5 partially repaired oxidized SlMYC2 and restored its DNA-binding ability.On the other hand,silencing SlMsrB5 inhibited the transcript levels of SlMYC2-targeted genes(SlATG13a,SlATG13b,SlATG18a,and SlATG18h).Similarly,dual-luciferase reporter(DLR)analysis revealed that SlMsrB5–SlMYC2 interaction significantly increased the ability of SlMYC2-mediated transcriptional activation of SlATG13a,SlATG13b,SlATG18a,and SlATG18h.These findings demonstrate that SlMsrB5-mediated cyclic oxidation/reduction of methionine in SlMYC2 inf luences SlATGs expression.Collectively,these findings reveal the mechanism of SlMYC2 in SlATGs transcriptional regulation,providing insight into the mechanism of MeJA-mediated postharvest fruit quality regulation.

Estimates and burden of foodborne pathogens in RTE beverages in relation to vending practices

Objectives:Growing trend of street-vended food in underdeveloped countries offers low-cost food to many sections of population.Although it provides job opportunities to many urban dwellers,several health hazards are associated with this business.The present study investigates the burden of foodborne pathogens in Ready-To-Eat(RTE)beverages in relation to vending practices among street vendors of Rawalpindi City,Pakistan according to standardized methods and protocols.Materials and Methods:Six densely populated locations of Rawalpindi city were selected.Commonly consumed sugar cane juice(SCJ)and tamarind prune(dried plums)drink(TPD)(locally called as Imli Alu Bukhara sherbet)from five vendors from each location were chosen in summer season where the temperature reaches above 40℃.Mean and the standard deviation were obtained by univariate and bivariate analyses.Association between the study variables was assessed through cross-tabulations,chi-square,and correlation tests.Results:All the samples were found unsatisfactory in comparison to guidelines of aerobic plate count.Total coliform was observed in 86.7 per cent of SCJ and 70.0 per cent of TPD samples.Fourteen samples of SCJ exceeded the limit of>1100 MPN/ml value,whereas samples of TPD exceeded this limit for Escherichia coli.All of SCJ and 93.3 per cent of TPD samples depicted the presence of Salmonella aureus.Salmonella spp.were found significantly high in 73.3 per cent samples of SCJ and 23.3 per cent samples of TPD.Conclusions:The incidence of high bioloads attributes towards a potential reservoir of foodborne pathogens due to unhygienic vending practices.

Global Wheat Head Detection(GWHD)Dataset:A Large and Diverse Dataset of High-Resolution RGB-Labelled Images to Develop and Benchmark Wheat Head Detection Methods

The detection of wheat heads in plant images is an important task for estimating pertinent wheat traits including head population density and head characteristics such as health,size,maturity stage,and the presence of awns.Several studies have developed methods for wheat head detection from high-resolution RGB imagery based on machine learning algorithms.However,these methods have generally been calibrated and validated on limited datasets.High variability in observational conditions,genotypic differences,development stages,and head orientation makes wheat head detection a challenge for computer vision.Further,possible blurring due to motion or wind and overlap between heads for dense populations make this task even more complex.Through a joint international collaborative effort,we have built a large,diverse,and well-labelled dataset of wheat images,called the Global Wheat Head Detection(GWHD)dataset.It contains 4700 high-resolution RGB images and 190000 labelled wheat heads collected from several countries around the world at different growth stages with a wide range of genotypes.Guidelines for image acquisition,associating minimum metadata to respect FAIR principles,and consistent head labelling methods are proposed when developing new head detection datasets.The GWHD dataset is publicly available at http://www.global-wheat.com/and aimed at developing and benchmarking methods for wheat head detection.

Global Wheat Head Detection Challenges:Winning Models and Application for Head Counting

Data competitions have become a popular approach to crowdsource new data analysis methods for general and specialized data science problems.Data competitions have a rich history in plant phenotyping,and new outdoor field datasets have the potential to embrace solutions across research and commercial applications.We developed the Global Wheat Challenge as a generalization competition in 2020 and 2021 to find more robust solutions for wheat head detection using field images from different regions.We analyze the winning challenge solutions in terms of their robustness when applied to new datasets.We found that the design of the competition had an influence on the selection of winning solutions and provide recommendations for future competitions to encourage the selection of more robust solutions.

Global Wheat Head Detection 2021:An Improved Dataset for Benchmarking Wheat Head Detection Methods

The Global Wheat Head Detection(GWHD)dataset was created in 2020 and has assembled 193,634 labelled wheat heads from 4700 RGB images acquired from various acquisition platforms and 7 countries/institutions.With an associated competition hosted in Kaggle,GWHD_2020 has successfully attracted attention from both the computer vision and agricultural science communities.From this first experience,a few avenues for improvements have been identified regarding data size,head diversity,and label reliability.To address these issues,the 2020 dataset has been reexamined,relabeled,and complemented by adding 1722 images from 5 additional countries,allowing for 81,553 additional wheat heads.We now release in 2021 a new version of the Global Wheat Head Detection dataset,which is bigger,more diverse,and less noisy than the GWHD_2020 version.