The Young Scientists Network: How the European Federation for Medicinal Chemistry (EFMC) Became Young Again

The European Federation for Medicinal Chemistry (EFMC) created the Young Scientists Network (YSN) to support early-career medicinal chemists and chemical biologists. By doing this, it addressed the rapid changes taking place in the scientific community and in our society, such as the rise of social media, the evolution of the gender balance in the scientific population, and educational needs. Creating the YSN was also a way to ensure that the next generation of scientists would contribute to shaping EFMC's strategy, while recognizing and addressing their needs. The YSN was set up as a very dynamic concept, and has now developed to the point where its impact is evident. The activities it promotes complement EFMC's community support and scientific opportunities, rejuvenating the Federation and preparing it for the future. It also provides opportunities for many brilliant young scientists, who do not hesitate to invest time and energy in supporting our community and shaping their own future.     

Implementing lean practices in manufacturing SMEs: testing ‘critical success factors’ using Necessary Condition Analysis

Lean practices are known to increase operational performance. Previous research has identified critical success factors for implementing lean practices. This research aims to examine the extent to which success factors are critical for various degrees of lean practice implementation. Using multiple-respondent self-assessments from 33 Dutch manufacturing small and medium-sized enterprises (SMEs), we conducted a Necessary Condition Analysis. Our findings indicated that the criticality of success factors is progression dependent. In the initial stages of the lean journey, SMEs could improve their lean practices in a bottom-up manner through local factors such as a learning focus, improvement training and support congruence. When lean practices are more advanced, some company-wide factors must be present: top management support, a shared improvement vision and a supplier link. Our findings question the universality of success factors such as strategic involvement and indicate the need for a more dynamic model of lean implementation.     

Detection and analysis of triterpenic compounds in apple extracts

A new technique for the rapid detection and analysis of triterpenic compounds in apple extracts using HPLC was developed and validated. The main advantage of this technique is the short duration of the analysis – this makes this technique superior to others currently applied for the routine HPLC analysis of triterpenic compounds. The developed, optimized, and validated technique was used for the evaluation of triterpenic compounds in samples of different cultivars of apples, their peels, and flesh. In total, four triterpenic compounds were isolated and identified. Ursolic acid was the dominant compound in all the tested apple samples. The highest amounts of triterpenic compounds were detected in the peels of the ‘Lodel’ apple cultivar, and thus apples of this cultivar may be potentially useful for the isolation of individual compounds and the production of functional food and dietary supplements.     

Decision Support for the Comparative Evaluation and Selection of Analytical Methods: Detection of Genetically Modified Organisms as an Example

The selection of the best-fit-for-purpose analytical method to be implemented in the laboratory is difficult due to availability of multiple methods, targets, aims of detection, and different kinds and sources of more or less reliable information. Several factors, such as method performance, practicability, cost of setup, and running costs need to be considered together with personnel training when selecting the most appropriate method. The aim of our work was to prepare a flexible multicriteria decision analysis model suitable for evaluation and comparison of analytical methods used for the purpose of detecting and/or quantifying genetically modified organisms, and to use this model to evaluate a variety of changing analytical methods. Our study included selection of PCR-, isothermal-, protein-, microarray-, and next-generation sequencing-based methods in simplex and/or multiplex formats. We show that the overall result of their fitness for purpose is relatively similar; however, individual criteria or a group of related criteria exposed more substantial differences between the methods. The proposed model of this decision support system enables easy modifications and is thus suitable for any other application of complex analytical methods.     

Quality and nutritional characteristics of traditional curd cheese enriched with thermo-coagulated acid whey protein and indigenous Lactococcus lactis strain

Consumer interest in a healthy lifestyle and health-promoting natural products is a major driving force for the increasing global demand for bio-functional and sustainable dairy foods. Supplementation of curd cheese with thermo-coagulated acid whey protein (TAWP) led to 8–10% higher contents of moisture, 23–31% of lactose, 12–13% of unsaturated, 5–6% of monounsaturated, 63% of polyunsaturated, and 3–4% of long-chain fatty acids. Lipid quality indices – TI, AI, h/H, and Omega 6/3 among others, were also significantly better than those of control cheese. The addition of indigenous Lactococcus lactis strain enhanced the flavour of cheese samples, decreased the counts of yeast and mould up to 1 log cfu g⁻¹ after 10 days of storage. The replacement of curd with TAWP resulted in novel cheese with overall sensory perception above 77 points and added nutritional and functional value, however, body and texture parameters of modified cheese samples require an improvement.     

Encapsulation of Synthesized Plant Growth Regulator Based on Copper(II) Complex in Chitosan/Alginate Microcapsules

A new copper complex, trans-diaqua-trans-bis [1-hydroxy-1,2-di (methoxycarbonyl) ethenato] copper (abbreviation Cu(II) complex), was synthesized and its plant growth regulation properties were investigated. The results show a sharp dependence of growth regulation activity of the Cu(II) complex on the type of culture and its concentration. New plant growth regulator accelerated the development of the corn root system (the increase in both length and weight) but showed a smaller effect on the development of the wheat and barley root systems. Stimulation of corn growth decreased with increasing Cu(II) complex concentration from 0.0001% to 0.01% (inhibition at high concentrations—0.01%). The development of corn stems was also accelerated but to a lesser extent. Chitosan-coated calcium alginate microcapsules suitable for delivery of Cu(II) complex to plants were prepared and characterized. Analysis of the FTIR spectrum showed that complex molecular interactions between functional groups of microcapsule constituents include mainly electrostatic interactions and hydrogen bonds. Microcapsules surface exhibits a soft granular surface structure with substructures consisting of abundant smaller particles with reduced surface roughness. Release profile analysis showed Fickian diffusion is the rate-controlling mechanism of Cu(II) complex releasing. The obtained results give new insights into the complexity of the interaction between the Cu(II) complex and microcapsule formulation constituents, which can be of great help in accelerating product development for the application in agriculture     

Survey of data-selection methods in statistical machine translation

Statistical machine translation has seen significant improvements in quality over the past several years. The single biggest factor in this improvement has been the accumulation of ever larger stores of data. We now find ourselves, however, the victims of our own success, in that it has become increasingly difficult to train on such large sets of data, due to limitations in memory, processing power, and ultimately, speed (i.e. data-to-models takes an inordinate amount of time). Moreover, the training data has a wide quality spectrum. A variety of methods for data cleaning and data selection have been developed to address these issues. Each of these methods employs a search or filtering algorithm to select a subset of the data, given a defined set of feature functions. In this paper we provide a comparative overview of research in this area based on application scenario, feature functions and search method.