The Roles of GDF-9, BMP-15, BMP-4 and EMMPRIN in Folliculogenesis and In Vitro Fertilization.

Growth differentiation factor 9 (GDF-9) contributes to early ovarian development and oocyte survival. Higher concentrations of GDF-9 in follicular fluid (FF) are associated with oocyte nuclear maturation and optimal embryo development. In in vitro fertilization (IVF), GDF-9 affects the ability of the oocyte to fertilize and subsequent embryonic development.

SpectroFood dataset: A comprehensive fruit and vegetable hyperspectral meta-dataset for dry matter estimation.

In the dataset presented in this article, samples belonging to one of the following crops, apple, broccoli, leek, and mushroom, were measured by hyperspectral cameras in the visible/near-infrared spectral domain (430-900 nm). The dataset was compiled by putting together measurements from different calibrated hyperspectral imaging cameras and crops to facilitate the training of artificial intelligence models, helping to overcome the generalization problem of hyperspectral models. In particular, this dataset focuses on estimating dry matter content across various crops by a single model in a non-destructive way using hyperspectral measurements.

Assessment of microbial biocontrol agent (BCA) viability to mechanical and thermal stress by simulating spray application conditions.

Background: In order to improve the biological control agent (BCA) efficacy, stress factors threatening the viability of microorganisms during spray application need to be determined. The effect of spray mixture temperature and exposure time on Trichoderma harzianum T 22 and Bacillus amyloliquefaciens QST713 viability were tested. Concurrently the combined effect of mechanical and thermal stress effect on BCA viability were tested at two initial spray mixture temperatures (14 and 25 °C) by simulating a spray application using airblast sprayers featured by different tank capacity and a spray liquid circuit (without and with hydraulic agitation system).

Can Satellites Predict Yield? Ensemble Machine Learning and Statistical Analysis of Sentinel-2 Imagery for Processing Tomato Yield Prediction.

In this paper, we propose an innovative approach for robust prediction of processing tomato yield using open-source AutoML techniques and statistical analysis. Sentinel-2 satellite imagery was deployed to obtain values of five (5) selected vegetation indices (VIs) during the growing season of 2021 (April to September) at 5-day intervals. Actual recorded yields were collected across 108 fields, corresponding to a total area of 410.

Predicting Grape Sugar Content under Quality Attributes Using Normalized Difference Vegetation Index Data and Automated Machine Learning.

Wine grapes need frequent monitoring to achieve high yields and quality. Non-destructive methods, such as proximal and remote sensing, are commonly used to estimate crop yield and quality characteristics, and spectral vegetation indices (VIs) are often used to present site-specific information. Analysis of laboratory samples is the most popular method for determining the quality characteristics of grapes, although it is time-consuming and expensive.