AMDPWE: Alphonso Mango Dataset for Precision Weight Estimation
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PROVIDER: S-EPMC10694035 | biostudies-literature | 2023 Nov
REPOSITORIES: biostudies-literature
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Project description:IntroductionFetal growth assessment by ultrasound is an essential part of modern obstetric care. The formula by Persson and Weldner for estimated fetal weight (EFW), used in Sweden since decades, has not yet been evaluated. The objective of this study was to evaluate accuracy and precision of the formula by Persson and Weldner, and to compare it to two other formulae using biparietal diameter instead of head circumference.Material and methodsThe study population consisted of 31 521 singleton pregnancies delivered at 22+0 gestational weeks or later, with an ultrasound EFW performed within 2 days before delivery, registered in the Swedish Pregnancy Register between 2014 and 2021. Fetal biometric ultrasound measurements were used to calculate EFW according to the formulae by Persson and Weldner, Hadlock 2 and Shepard. Bland-Altman analysis, systematic error (mean percentage error), random error (standard deviation [SD] of mean percentage error), proportion of weight estimates within ±10% of birthweight, and proportion with underestimated and overestimated weight was calculated. Moreover, calculations were made after stratification into small, appropriate, and large for gestational age (SGA, AGA and LGA), respectively, and gestational age at examination.ResultsFor the formula by Persson and Weldner, MPE was -2.7 (SD 8.9) and the proportion of EFW within ±10% from actual birthweight was 76.0%. MPE was largest for fetuses estimated as severe SGA (<3rd percentile, -5.4) and for the most preterm fetuses (<24 weeks, -5.4). For Hadlock 2 and Shepard's formulae, MPE were 3.9 (SD 8.9) and 3.4 (SD 9.7), respectively, and the proportions of EFW within ±10% from actual birthweight were 69.4% and 67.1%, respectively. MPE was largest for fetuses estimated as severe LGA (>97th percentile), 7.6 and 9.4, respectively.ConclusionsThe recommended Swedish formula by Persson and Weldner is generally accurate for fetal weight estimation. The systematic underestimation of EFW and random error is largest in extreme preterm and estimated SGA-fetuses, which is of importance in clinical decision making. The accuracy of EFW with the formula by Persson and Weldner is as good as or better than Hadlock 2 and Shepard's formulae.
| S-EPMC10201965 | biostudies-literature
Project description:Alphonso is known as the "King of mangos" due to its unique flavor, attractive color, low fiber pulp and long shelf life. We analyzed the transcriptome of Alphonso mango through Illumina sequencing from seven stages of fruit development and ripening as well as flower. Total transcriptome data from these stages ranged between 65 and 143 Mb. Importantly, 20,755 unique transcripts were annotated and 4,611 were assigned enzyme commission numbers, which encoded 142 biological pathways. These included ethylene and flavor related secondary metabolite biosynthesis pathways, as well as those involved in metabolism of starch, sucrose, amino acids and fatty acids. Differential regulation (p-value ≤ 0.05) of thousands of transcripts was evident in various stages of fruit development and ripening. Novel transcripts for biosynthesis of mono-terpenes, sesqui-terpenes, di-terpenes, lactones and furanones involved in flavor formation were identified. Large number of transcripts encoding cell wall modifying enzymes was found to be steady in their expression, while few were differentially regulated through these stages. Novel 79 transcripts of inhibitors of cell wall modifying enzymes were simultaneously detected throughout Alphonso fruit development and ripening, suggesting controlled activity of these enzymes involved in fruit softening.
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Project description:Freshly-harvested mature green mangoes (cvs. 'Alphonso' and 'Banganapalli') were individually shrink wrapped using two semi-permeable Cryovac films® (D-955 and LD-935) and a locally available LDPE film. The shrink wrapped and non-wrapped fruit were stored at 8 °C for 5 weeks and transferred to ambient conditions for subsequent ripening, to study the feasibility of alleviation of chilling injury (CI) and to determine shrink wrapping effect on fruit quality. Shrink wrapped mangoes of 'Banganapalli' and 'Alphonso' cultivars packed in D-955 (15 μm thickness) film could be stored for 5 weeks at 8 °C in fresh and unripe green condition. After storage, these cultivars respectively lost only 0.5 and 1.4 % mass in case of shrink wrapping as compared to 5.8 and 6.9 % loss in non-wrapped fruit. After removal from low temperature and unwrapping, shrink wrapped mangoes showed normal respiratory behaviour with production of CO2 and ethylene peaks (climacteric peaks) during ripening, whereas non-wrapped fruit did not show any respiratory peaks. Shrink wrapped mangoes ripened normally within a week at ambient temperature (24-32 °C and 60-70 % RH) with good surface yellow colour (reflected by hue and chroma values), edible softness, retention of nutritional quality and acceptable organoleptic quality. These quality parameters were better in fruit wrapped with D-955 film compared to LD-935 and LDPE films. Total carotenoids in terms of β-carotene content were significantly higher in shrink wrapped fruit when compared to non-wrapped fruit. Among different shrink films, total antioxidant capacity and DPPH radical scavenging abilities were higher in LD-935 wrapped fruit in case of 'Alphonso' cultivar whereas these were on par in LD-935 and D-955 film wrapped fruit in case of 'Banganapalli' cultivar.
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Project description:Data in this article presents fatty acid composition of three mango cultivars; Alphonso, Pairi and Kent through fruit development and ripening. Change in the ω-6 and ω-3 fatty acids level during mango fruit development and ripening is depicted. Also, data on aroma volatile 'lactones' composition from pulp and skin tissues of these cultivars at their ripe stage, respectively is provided. Statistical data is also shown, which correlates modulation in lactone content with that of fatty acid composition and content during fruit development and ripening in all the three mango cultivars.
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Project description:Tumorized precision dataset for ONCOLINER: A new solution for monitoring, improving, and harmonizing somatic variant calling across genomic oncology centers
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Project description:Agriculture is one of the few remaining sectors that is yet to receive proper attention from the machine learning community. The importance of datasets in the machine learning discipline cannot be overemphasized. The lack of standard and publicly available datasets related to agriculture impedes practitioners of this discipline to harness the full benefit of these powerful computational predictive tools and techniques. To improve this scenario, we develop, to the best of our knowledge, the first-ever standard, ready-to-use, and publicly available dataset of mango leaves. The images are collected from four mango orchards of Bangladesh, one of the top mango-growing countries of the world. The dataset contains 4000 images of about 1800 distinct leaves covering seven diseases. Although the dataset is developed using mango leaves of Bangladesh only, since we deal with diseases that are common across many countries, this dataset is likely to be applicable to identify mango diseases in other countries as well, thereby boosting mango yield. This dataset is expected to draw wide attention from machine learning researchers and practitioners in the field of automated agriculture.
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Project description:: Pre-harvest fruit yield estimation is useful to guide harvesting and marketing resourcing, but machine vision estimates based on a single view from each side of the tree ("dual-view") underestimates the fruit yield as fruit can be hidden from view. A method is proposed involving deep learning, Kalman filter, and Hungarian algorithm for on-tree mango fruit detection, tracking, and counting from 10 frame-per-second videos captured of trees from a platform moving along the inter row at 5 km/h. The deep learning based mango fruit detection algorithm, MangoYOLO, was used to detect fruit in each frame. The Hungarian algorithm was used to correlate fruit between neighbouring frames, with the improvement of enabling multiple-to-one assignment. The Kalman filter was used to predict the position of fruit in following frames, to avoid multiple counts of a single fruit that is obscured or otherwise not detected with a frame series. A "borrow" concept was added to the Kalman filter to predict fruit position when its precise prediction model was absent, by borrowing the horizontal and vertical speed from neighbouring fruit. By comparison with human count for a video with 110 frames and 192 (human count) fruit, the method produced 9.9% double counts and 7.3% missing count errors, resulting in around 2.6% over count. In another test, a video (of 1162 frames, with 42 images centred on the tree trunk) was acquired of both sides of a row of 21 trees, for which the harvest fruit count was 3286 (i.e., average of 156 fruit/tree). The trees had thick canopies, such that the proportion of fruit hidden from view from any given perspective was high. The proposed method recorded 2050 fruit (62% of harvest) with a bias corrected Root Mean Square Error (RMSE) = 18.0 fruit/tree while the dual-view image method (also using MangoYOLO) recorded 1322 fruit (40%) with a bias corrected RMSE = 21.7 fruit/tree. The video tracking system is recommended over the dual-view imaging system for mango orchard fruit count.
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