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This paper provides a comprehensive study of theoretical and practical issues of violation of human rights by war crimes, protection of legitimate interests of individuals in national and international law as a result of such violation. The purpose of this study is a comprehensive analysis of theoretical and applied issues related to the protection of violated rights and legitimate interests of individuals in public international law as a result of the commission of war crimes, and the formulation of scientifically sound proposals for improving the current legislation of Ukraine and the practice of its application in this area. The paper analyses the current scientific opinions and legislation on the regulation of illegal acts and liability for damage caused to victims as a result of violation of human rights and fundamental freedoms by war crimes. Furthermore, the existing forms, methods, and means of protecting the rights and legitimate interests of such persons in Ukraine and in the international arena were determined. The paper also provides a comprehensive study of the existing concept, legal nature and main features of war crimes and their legal regulation in Ukrainian legislation. Legislative amendments to the regulations governing this issue are proposed and the study justifies the position regarding the existence of an exclusively judicial procedure for resolving issues of human rights violations by war crimes, the result of which should be the adoption of a court decision. As a result of the study, the current scientific statements and achievements are clearly identified, as well as those that have emerged due to the constant development of public relations and substantial changes in international relations of various states, which causes armed conflicts and war crimes. One of the achievements of the scientific study is the proof of the importance of the problem of violated human rights in modern society and the extreme need for its research. After all, modern legislation requires substantial changes and improvement of the existing provisions with the subsequent possibility of their practical application

Background: Centralizing procurement for prescription drugs has the potential to reduce drug spending by creating economies of scale and by improving purchasing power. In March 2019, the Chinese government launched a volume-based purchasing (VBP) pilot program using a competitive bidding process to purchase high-quality generic drugs for which branded drug substitutes were available. We evaluated the impact of introducing the VBP pilot on drug purchase quantity and spending. Methods: We performed an interrupted time-series design to estimate the change in monthly drug purchase quantity and spending comparing fourteen months before and seven months after the VBP pilot. We obtained monthly prescription drug purchase data for all purchases from public hospitals in the three large pilot cities (Beijing, Shanghai and Xi’an) and two non-pilot cities (Changsha and Zhengzhou) between January 2018 to September 2019. We measured quantity and spending outcomes separately for the branded and generic substitutes that were selected in the VBP, and other drugs for the treatment of the same diseases. We evaluated heterogeneity of impact by pilot city, drug type, and therapeutic class (cardiovascular disease, mental disorder and cancer) separately. Findings: The VBP pilot in these three cities covered 60 million individuals. The implementation of the pilot reform was associated with a 132% (95%-CI: 104%-165%, p<0·001) increase in the purchase quantity of selected drugs in pilot cities compared to an 17% decrease (95%-CI: 9%-25%, p<0·001) in control cities. In contrast, the purchase quantity of branded and other drugs in pilot cities decreased by 38% (95%-CI: 27%-46%, p<0·001) and 77% (95%-CI: 71%-81%, p<0·001), respectively; while in control cities, these remained at similar levels. Overall, there was a 35% (95%-CI: 28%-41%, p<0·001) decrease in the purchase spending for all drugs in the first post-policy month, from 8·1 billion CNY estimated in the absence of VBP down to 5·3 billion CNY. The largest reduction in spending was in Shanghai and from drugs for the treatment of cardiovascular diseases. Interpretation: The evidence suggests positive impact of the VBP pilot in reducing the overall drug spending and increasing the use of high-quality generics in three pilot cities. This overall trend is not observed in two non-pilot cities. Assessment of long-term impact of the VBP policy is needed on various outcomes such as drug prescription and utilization patterns, patients’ health outcomes and spending. Funding: None to declare. Declaration of Interest: None to declare.

Background The volume of biomedical literature and clinical data is growing at an exponential rate. Therefore, efficient access to data described in unstructured biomedical texts is a crucial task for the biomedical industry and research. Named Entity Recognition (NER) is the first step for information and knowledge acquisition when we deal with unstructured texts. Recent NER approaches use contextualized word representations as input for a downstream classification task. However, distributed word vectors (embeddings) are very limited in Spanish and even more for the biomedical domain. Methods In this work, we develop several biomedical Spanish word representations, and we introduce two Deep Learning approaches for pharmaceutical, chemical, and other biomedical entities recognition in Spanish clinical case texts and biomedical texts, one based on a Bi-STM-CRF model and the other on a BERT-based architecture. Results Several Spanish biomedical embeddigns together with the two deep learning models were evaluated on the PharmaCoNER and CORD-19 datasets. The PharmaCoNER dataset is composed of a set of Spanish clinical cases annotated with drugs, chemical compounds and pharmacological substances; our extended Bi-LSTM-CRF model obtains an F-score of 85.24% on entity identification and classification and the BERT model obtains an F-score of 88.80% . For the entity normalization task, the extended Bi-LSTM-CRF model achieves an F-score of 72.85% and the BERT model achieves 79.97%. The CORD-19 dataset consists of scholarly articles written in English annotated with biomedical concepts such as disorder, species, chemical or drugs, gene and protein, enzyme and anatomy. Bi-LSTM-CRF model and BERT model obtain an F-measure of 78.23% and 78.86% on entity identification and classification, respectively on the CORD-19 dataset. Conclusion These results prove that deep learning models with in-domain knowledge learned from large-scale datasets highly improve named entity recognition performance. Moreover, contextualized representations help to understand complexities and ambiguity inherent to biomedical texts. Embeddings based on word, concepts, senses, etc. other than those for English are required to improve NER tasks in other languages. This work was partially supported by the Research Program of the Ministry of Economy and Competitiveness - Government of Spain, (DeepEMR project TIN2017-87548-C2-1-R).

Self-medication is a latent problem in our society. It is the own initiative or decision to take drugs, the consumption of drugs regardless of their origin, or medical prescription1,2. This has been increasing and has become a phenomenon that is highly studied by researchers. Thus, in pre-pandemic times, self-medication was a considerable problem in several countries, although with differences between countries in Europe and Latin America. So we have, in Spain3; The National Health System announced that in 2017 15.3% of people self-medicate. Also, in Mexico4; 86% consume medications without a doctor's prescription. La automedicación es un problema latente en nuestra sociedad. Es la iniciativa propia o decisión de tomar medicamentos el consumo de fármacos sin importar su origen, ni prescripción médica1,2. Esta ha ido en aumento y se ha vuelto un fenómeno muy estudiado por investigadores. Así, en épocas pre-pandemia la automedicación era un problema considerable en varios países, aunque con diferencias entre países de Europa y Latinoamérica. Así tenemos, en España3; el Sistema Nacional de Salud dio a conocer que en el año 2017 el 15,3 % de personas se automedica. Asimismo, en México4; el 86 % consume medicamentos sin la prescripción de un médico. Sin embargo, el miedo, la saturación de hospitales y la afectación económica por la pandemia COVID-195 ha incentivado a realizar con más frecuencia esta práctica e inclusive aumentó la prevalencia en la población joven6. Puesto que, justificaban la práctica por diversos factores entre ellos; el 33,49 % de los jóvenes relacionan la similitud de los síntomas con enfermedades pasadas, el 19,17 % percibían la enfermedad como algo leve, el 18,24 % desde el aspecto económico les resultaba más barato la automedicación7. Además, en Perú, al inicio de la pandemia se distribuyó kits para tratar la enfermedad leve por coronavirus; por lo que algunos cuando eran hospitalizados por la enfermedad, referían haberse automedicado con ivermectina que les habían entregado a sus familiares8. Además, la pandemia ha incrementado el consumo de tranquilizantes para tratar la ansiedad y el insomnio, trastornos que han aumentado a consecuencia de la pandemia9. En tal sentido, algunos por el miedo y la ansiedad, han optado por el automedicarse sustancias tales como el dióxido de cloro, vitaminas, hierbas, cloroquina10. En tanto, se usaron también medicamentos que permitan mejorar el sistema inmunológico como vitaminas C, D, A y B y suplementos de zinc11. Finalmente, la población, debido al SARS-CoV-2 como una acción preventiva y tranquilizadora, recurrió al acopio de fármacos y su eventual autoconsumo sin consultar con una prescripción. Sin embargo, fue una medida errónea debido a que existen fármacos como los antibióticos que debilitan el sistema inmune12. Frente a lo expuesto, es necesario reflexionar que una consecuencia de la automedicación es la resistencia a los antibióticos, esta aumenta la letalidad de la enfermedad porque el tratamiento se vuelve más difícil debido a la pérdida de eficacia de los mismos. Por lo que es importante implementar en todas las farmacias la vigilancia del expendio de antibióticos, ansiolíticos, etc. con receta. Además, se debe concientizar a la población sobre los riesgos de la automedicación para la salud, puesto que puede causar resistencia a los antibióticos y la dependencia de ansiolíticos y analgésicos.

Abstract Background Natural language processing (NLP) and text mining technologies for the extraction and indexing of chemical and drug entities are key to improving the access and integration of information from unstructured data such as biomedical literature. Methods In this paper we evaluate two important tasks in NLP: the named entity recognition (NER) and Entity indexing using the SNOMED-CT terminology. For this purpose, we propose a combination of word embeddings in order to improve the results obtained in the PharmaCoNER challenge. Results For the NER task we present a neural network composed of BiLSTM with a CRF sequential layer where different word embeddings are combined as an input to the architecture. A hybrid method combining supervised and unsupervised models is used for the concept indexing task. In the supervised model, we use the training set to find previously trained concepts, and the unsupervised model is based on a 6-step architecture. This architecture uses a dictionary of synonyms and the Levenshtein distance to assign the correct SNOMED-CT code. Conclusion On the one hand, the combination of word embeddings helps to improve the recognition of chemicals and drugs in the biomedical literature. We achieved results of 91.41% for precision, 90.14% for recall, and 90.77% for F1-score using micro-averaging. On the other hand, our indexing system achieves a 92.67% F1-score, 92.44% for recall, and 92.91% for precision. With these results in a final ranking, we would be in the first position.

This book is available as open access through the Bloomsbury Open programme and is available on www.bloomsburycollections.com. It is funded by Trinity College Dublin, DARIAH-EU and the European Commission.This book explores the challenges society faces with big data, through the lens of culture rather than social, political or economic trends, as demonstrated in the words we use, the values that underpin our interactions, and the biases and assumptions that drive us. Focusing on areas such as data and language, data and sensemaking, data and power, data and invisibility, and big data aggregation, it demonstrates that humanities research, focussing on cultural rather than social, political or economic frames of reference for viewing technology, resists mass datafication for a reason, and that those very reasons can be instructive for the critical observation of big data research and innovation.

Abstract Since the 1980s, authors of the Austrian School have argued that the problem of rational allocation in a planned economy is not computational or technical in nature (static optimisation, with given information) but a question of dynamic efficiency (innovation and the creation of new information), and that this would be impossible without market processes and free entrepreneurship. In this article, we argue to the contrary that a planned economy can effectively drive dynamic efficiency. We first reveal that the Austrian thesis on the impossibility of dynamic efficiency in socialist planning is based on tautological arguments, or on problems already solved by technological development. Secondly, we present an institutional formula for promoting innovative activities and entrepreneurship within a framework of social ownership of the means of production and social control of investment.