Mini fruits and vegetables (MFV) are pocket fruits and vegetables whose shape and volume are significantly smaller than those widely sold and well-known normal fruits and vegetables (NFV) on the market. Through the research on the market status and consumption trends of MFV, it was found that MFV have recently become a new market favorite. However, compared with NFV, there was found to be no relevant data on sensory quality, nutritional value, safety, etc. of MFV; this could indicate low consumer awareness of MFV, which in turn affects their planting and sales choices, as well as the market scale remaining small. In this context, six MFV with high degree of marketization were selected and compared with their corresponding NFV to evaluate the nutritional value, biological activity, and sensory properties. The results showed the nutritional value of MFV to be mainly related to their species. The nutritional value of MFV derived from immature, tender vegetables was generally lower than that of mature NFV. For example, the content of zeaxanthin in normal maize was 0.43 mg/kg, which was about 2.87 times that of mini maize (0.15 mg/kg). For newly cultivated mini varieties, their nutritional value often had different trends and rules compared with NFV. The nutritional value obtained by consuming MFV is not equal to that obtained by consuming the corresponding NFV of the same weight.
Apparent molar volumes (VΦ) of mannitol in aqueous solutions of 1-propanol and 2-propanol were obtained from densities of the ternary solutions, accurately measured with a quartz vibrating-tube den- simeter at 298.15 K. These data have been used to deduce limiting partial molar volumes of mannitol ( Φ V � ) and correspondingly limiting partial molar volumes of transfer ( trs Φ V ∆ � ) from water to different alcohol so- lutions at different concentrations. The results indicated that influence of 1-propanol on the volumetric prop- erty of mannitol in aqueous solution was stronger than that of 2-propanol. The changes of the volumetric properties were discussed in the light of interaction between solute and solvent and the different locations of hydroxyl group in propanol isomers. Keywords limiting partial molar volume; mannitol; 1-propanol; 2-propanol
This paper describes the CMU OAQA system evaluated in the BioASQ 3B Question Answering track. We first present a three-layered architecture, and then describe the components integrated for exact answer generation and retrieval. Using over 400 factoid and list questions from past BioASQ 1B and 2B tasks as background knowledge, we focus on how to learn to answer questions using a gold standard dataset of question-answer pairs, using supervised models for answer type prediction and candidate answer scoring. On the three test sets where the system was evaluated (3, 4, and 5), the official evaluation results have shown that the system achieves an MRR of .1615, .5155, .2727 for factoid questions, and an F-measure of .0969, .3168, .1875 for list questions, respectively; five of these scores were the highest reported among all participating systems.
Abstract Heavy metals are of particular concern in grape and wine processing, especially copper. The sources of copper are diverse, including vineyard soil, copper‐containing pesticides on the fruit surface, copper wine‐making equipment, and exogenous addition in winemaking. Copper has potential risks to human nerves, metabolism, and others. It can inhibit yeast growth, delay fermentation, and also mediate oxidation reactions, which has a huge impact on the nutritional quality and sensory quality of fresh wine and aged wine. It is therefore crucial to detect, quantify, and remove copper from grapes and wine. However, the copper situations in the wine industries of various countries are complicated and diverse, and the existing forms of copper are quite different, which makes the research challenging. This review summarizes and analyzes the existence and influence of copper in the wine industry by analyzing the sources of, the current situation regarding, and the detection and removal methods for copper in wine. With the study, a better understanding of copper's impact on wine production will be gained, facilitating further control of copper in wine and helping the wine industry grow.
Potassium (K+) is a macronutrient for plant growth and development. Although a number of K-transporters are encoded in various plant genomes, functional characterization of these transport genes have lagged behind of genomic information in crop plants. In rice genome, a large expansion of high-affinity K-transporters (HAKs/KUPs/KTs) has been identified as compared to those in Arabidopsis genome suggesting a functional diversity of these transporters in cereals. We report here the functional characterization of a HAK member, OsHAK3, in rice using CRISPR-assisted genetic analysis. Expression of OsHAK3 was mainly found in roots and its protein was targeted to the plasma membrane. Loss of function of OsHAK3 led to a reduction of K+ uptake rate and K+ content, consistent with the finding that mutant plants became stunted under low-K+ conditions. In addition, the growth of Oshak3 mutants was more sensitive to salt stress due to altered K/Na ratio in the plants. Together, our data demonstrate that OsHAK3 plays a crucial role in K+ homeostasis, especially under K+-limited conditions and when plants face salinity stress.
This paper addresses the trade-off between Accuracy and Transparency for deep learning applied to sports analytics. Neural nets achieve great predictive accuracy through deep learning, and are popular in sports analytics. But it is hard to interpret a neural net model and harder still to extract actionable insights from the knowledge implicit in it. Therefore, we built a simple and transparent model that mimics the output of the original deep learning model and represents the learned knowledge in an explicit interpretable way. Our mimic model is a linear model tree, which combines a collection of linear models with a regression-tree structure. The tree version of a neural network achieves high fidelity, explains itself, and produces insights for expert stakeholders such as athletes and coaches. We propose and compare several scalable model tree learning heuristics to address the computational challenge from datasets with millions of data points.
