CropPol: a dynamic, open and global database on crop pollination

This is the final version. Available from Wiley via the DOI in this record The original dataset (v1.1.0) of the CropPol database can be accessed from the ECOLOGY repository. Main upgrades of these datasets will be versioned and deposited in Zenodo (DOI: 10.5281/zenodo.5546600)Data availability. V.C. Computer programs and data-processing algorithms: The algorithms used in deriving, processing, or transforming data can be accessed in the DataS1.zip file and the Zenodo repository (DOI: 10.5281/zenodo.5546600). V.D. Archiving: The data is archived for long-term storage and access in Zenodo (DOI: 10.5281/zenodo.5546600)Seventy five percent of the world's food crops benefit from insect pollination. Hence, there has been increased interest in how global change drivers impact this critical ecosystem service. Because standardized data on crop pollination are rarely available, we are limited in our capacity to understand the variation in pollination benefits to crop yield, as well as to anticipate changes in this service, develop predictions, and inform management actions. Here, we present CropPol, a dynamic, open and global database on crop pollination. It contains measurements recorded from 202 crop studies, covering 3,394 field observations, 2,552 yield measurements (i.e. berry weight, number of fruits and kg per hectare, among others), and 47,752 insect records from 48 commercial crops distributed around the globe. CropPol comprises 32 of the 87 leading global crops and commodities that are pollinator dependent. Malus domestica is the most represented crop (32 studies), followed by Brassica napus (22 studies), Vaccinium corymbosum (13 studies), and Citrullus lanatus (12 studies). The most abundant pollinator guilds recorded are honey bees (34.22% counts), bumblebees (19.19%), flies other than Syrphidae and Bombyliidae (13.18%), other wild bees (13.13%), beetles (10.97%), Syrphidae (4.87%), and Bombyliidae (0.05%). Locations comprise 34 countries distributed among Europe (76 studies), Northern America (60), Latin America and the Caribbean (29), Asia (20), Oceania (10), and Africa (7). Sampling spans three decades and is concentrated on 2001-05 (21 studies), 2006-10 (40), 2011-15 (88), and 2016-20 (50). This is the most comprehensive open global data set on measurements of crop flower visitors, crop pollinators and pollination to date, and we encourage researchers to add more datasets to this database in the future. This data set is released for non-commercial use only. Credits should be given to this paper (i.e., proper citation), and the products generated with this database should be shared under the same license terms (CC BY-NC-SA). This article is protected by copyright. All rights reserved.OBServ Projec

Molecular modeling as a predictive tool for the development of solid dispersions

In this study molecular modeling is introduced as a novel approach for the development of pharmaceutical solid dispersions. A computational model based on quantum mechanical (QM) calculations was used to predict the miscibility of various drugs in various polymers by predicting the binding strength between the drug and dimeric form of the polymer. The drug/polymer miscibility was also estimated by using traditional approaches such as Van Krevelen/Hoftyzer and Bagley solubility parameters or Flory–Huggins interaction parameter in comparison to the molecular modeling approach. The molecular modeling studies predicted successfully the drug–polymer binding energies and the preferable site of interaction between the functional groups. The drug–polymer miscibility and the physical state of bulk materials, physical mixtures, and solid dispersions were determined by thermal analysis (DSC/MTDSC) and X-ray diffraction. The produced solid dispersions were analyzed by X-ray photoelectron spectroscopy (XPS), which confirmed not only the exact type of the intermolecular interactions between the drug–polymer functional groups but also the binding strength by estimating the N coefficient values. The findings demonstrate that QM-based molecular modeling is a powerful tool to predict the strength and type of intermolecular interactions in a range of drug/polymeric systems for the development of solid dispersions