Challenges of cross–sectoral collaboration of social enterprises in the Baltic states

Social enterprises are a new phenomenon in the Baltic States. To create a substantial social impact in society and scale their business, social enterprises in Baltic states need to create partnerships and collaborate with di fferent sectors. By forming partnerships with different sectors, social enterprises can provide effective solutions to social problems. The aim of this article is to identify the main factors for social enterprises to create successful collaboration and partnerships with private, and public sector organisations. The methods of research are an analysis of scientific literature, social entrepreneurs’ interviews, content analysis. The study also analyzed the main obstacles for social enterprises to collaborate with different sectors. The empirical findings of the study disclosed how to improve partnerships development with private, and public sectors. Following the theoretical and empirical research, the article suggests possible means of improving and developing partnerships

D-O-C Stable Isotopes, 14C Radiocarbon and Radiogenic Isotope Techniques Applied in Wine Products for Geographical Origin and Authentication

Oxygen, deuterium and carbon isotopes were measured in wine products in two Greek vineyards, Amydaio (north) and Nemea (south). The enriched isotope values in Nemea reflects the higher evapo-transpiration rate and the more arid condition of Southern Greece. White wines were slightly more depleted than red wines of the same year and the same growing region probably due to different harvest periods. Further was pointed out the variety of isotope values with respect to vintage year indicating that the vintage year contributes to the development of isotopes in wine water. In both vineyards the trend lines intersect the oxygen and deuterium isotopes of irrigation water highlighting the source water and the initial isotopic composition of grape berries. δ13C values of ethanol confirmed the origin of C3 plants and the authentication of wine products without detecting adulteration with industrial alcohol. The results of 14C measurements in ethanol extracted from Greek wines follow the known pattern of 14C variations in atmospheric CO2. The homogeneity of 87Sr/86Sr and 144Nd/143Nd isotope values confirms that the territorial and geological signal is transferred through the vineyards in the final product, wine, certifying the exclusively provenance of the wine areas Amydaio and Nemea

Η συμμετοχή των δανειστών στην εκτελεστική διαδικασία

Η εργασία πραγματεύεται τις δυνατότητες συμμετοχής του δανειστή που δεν επέσπευσε την εκτέλεση να συμμετάσχει στην ήδη αρξάμενη εκτελεστική διαδικασία. Εξετάζεται αφενός η δυνατότητα συμμετοχής δανειστή στις δίκες περί την εκτέλεση και αφετέρου η συμμετοχή του δανειστή στην εκτελεστική διαδικασία μέσω των θεσμών της αναγγελίας και της υποκατάστασης δανειστή σε θέση επισπεύδοντος.The paper discusses the possibilities for a creditor who has not accelerated enforcement to participate in the already initiated enforcement proceedings. It examines both the possibility of creditor participation in enforcement court proceedings and the participation of the creditor in enforcement through the procedural institutions of creditor' s notification and substitution of a creditor in the position of an enforcement agent

Challenges of Cross-Sectoral Collaboration of Social Enterprises in Baltic States

Purpose: The aim of this article is to identify the main Challenges that social enterprises face while establishing and maintaining collaboration with private, and public sector organizations. The study also suggested means of improving and developing partnerships as well as the main factors that, social enterprises form cross-sector collaboration with private and public sector organizations

Algorithmic discovery, development and personalized selection of higher-order drug cocktails : A label-free live-cell imaging & secretomics approach

An upward trend in clinical pharmacology is the use of multiple drugs to combat complex and co-occurring diseases due to better efficacy, decreased toxicity and reduced risk of evolving resistance. Despite high late-stage attrition rates and the need for multi drug treatments, most drug discovery and development efforts are still mainly focused on new one-size-fits-all monotherapies. This is unfortunate given the complex, heterogeneous and often only partially understood pathophysiology of many diseases. In this context, polypharmacotherapies hold strong potential, especially when patient tailored. However, as of today, the personalized combination therapy area remains vastly unexplored. A major reason is lack of standardized and robust tools that allow systematic in vitro drug combination sensitivity testing of different disease models and patient derived cells. This thesis fills in this lack by introducing two methodological frameworks, namely COMBImageDL and COMBSecretomics, designed to enable systematic second- and higher-order drug combination studies within and beyond cancer pharmacology. They include advanced quality control procedures, non-parametric resampling statistics to quantify uncertainty and a data driven methodology to evaluate response patterns and discern higher- from lower- and single-drug effects. Both are based on a standardized and reproducible format that could be employed with any experimental platform that provides the required raw data. COMBImageDL searches exhaustively for drug cocktails that induce changes in cell viability and time evolving cell culture morphology by employing conventional endpoint synergy analyses jointly with quantitative label-free live-cell imaging. Deep neural network learning, MapReduce parallel processing and method-specific parameter tuning are key components of the design. The purely phenotypic functionality of COMBImageDL is extended by COMBSecretomics, which searches exhaustively for drug cocktails that can modify, or even reverse malfunctioning secretomic patterns. It processes complex datasets involving drug treated cells observed before and after being stimulated by relevant proteins. Finally, the highest single agent method is generalized for higher-order drug combination analysis and adjusted for secreted protein profiles. The frameworks were used in five pharmacological studies being industrial, academic and clinical collaborations in areas where novel and personalized multi drug regimens are highly needed; oncology (acute myeloid leukemia and glioblastoma multiforme) and osteoarthritis. These studies demonstrate intriguing drug combination findings and in general the great potential of tools like COMBImageDL and COMBSecretomics to accelerate the discovery and development of novel potent polypharmacotherapeutic candidates

CUSP9-9097

Exhaustive in vitro evaluation of the 9-drug cocktail CUSP9 for treatment of glioblastoma using COMBImageDL.Label-free live-cell imaging data for the third replicate 384-well plate (barcode 9097) containing all single drugs (at one fixed concentration each) and all plausible drug combinations up to order 4 from the CUSP9 protocol as add-on to standard-of-care Temozolomide, using the human glioblastoma cell line M059K. 22 grayscale images/frames of size 800x992 were acquired per experimental well, one every 4h for 84h, starting without treatment at 0h. The first and second channel of each frame contain the original grayscale pixel values, while the third one contains the binary segmentation map (foreground vs. background) generated with a modified version of the deep convolutional neural network architecture U-Net.M059K_190429_9097.xlsx: Excel file containing the experimental layout designed for plate 9097 and used to produce the corresponding transfer scheme for acoustic liquid dispension

Algorithmic discovery, development and personalized selection of higher-order drug cocktails : A label-free live-cell imaging & secretomics approach

An upward trend in clinical pharmacology is the use of multiple drugs to combat complex and co-occurring diseases due to better efficacy, decreased toxicity and reduced risk of evolving resistance. Despite high late-stage attrition rates and the need for multi drug treatments, most drug discovery and development efforts are still mainly focused on new one-size-fits-all monotherapies. This is unfortunate given the complex, heterogeneous and often only partially understood pathophysiology of many diseases. In this context, polypharmacotherapies hold strong potential, especially when patient tailored. However, as of today, the personalized combination therapy area remains vastly unexplored. A major reason is lack of standardized and robust tools that allow systematic in vitro drug combination sensitivity testing of different disease models and patient derived cells. This thesis fills in this lack by introducing two methodological frameworks, namely COMBImageDL and COMBSecretomics, designed to enable systematic second- and higher-order drug combination studies within and beyond cancer pharmacology. They include advanced quality control procedures, non-parametric resampling statistics to quantify uncertainty and a data driven methodology to evaluate response patterns and discern higher- from lower- and single-drug effects. Both are based on a standardized and reproducible format that could be employed with any experimental platform that provides the required raw data. COMBImageDL searches exhaustively for drug cocktails that induce changes in cell viability and time evolving cell culture morphology by employing conventional endpoint synergy analyses jointly with quantitative label-free live-cell imaging. Deep neural network learning, MapReduce parallel processing and method-specific parameter tuning are key components of the design. The purely phenotypic functionality of COMBImageDL is extended by COMBSecretomics, which searches exhaustively for drug cocktails that can modify, or even reverse malfunctioning secretomic patterns. It processes complex datasets involving drug treated cells observed before and after being stimulated by relevant proteins. Finally, the highest single agent method is generalized for higher-order drug combination analysis and adjusted for secreted protein profiles. The frameworks were used in five pharmacological studies being industrial, academic and clinical collaborations in areas where novel and personalized multi drug regimens are highly needed; oncology (acute myeloid leukemia and glioblastoma multiforme) and osteoarthritis. These studies demonstrate intriguing drug combination findings and in general the great potential of tools like COMBImageDL and COMBSecretomics to accelerate the discovery and development of novel potent polypharmacotherapeutic candidates