The 2021 Memory Law in Bosnia and Herzegovina–Reconciliation or Polarization?

In July 2021, the outgoing international High Representative (HR) of Bosnia and Herzegovina (BiH) imposed a set of amendments to the Criminal Code of BiH, which outlawed the denial of genocide and relativization of war crimes. The decision was largely welcomed by Bosniaks but met with animosity to many in the Bosnian Serb entity and sparked one of the worst crises in post-war BiH. In this article, we demonstrate that because of the manner of the adoption and the legal stipulations, the HR’s decision played straight into the raging BiH memory wars. It exacerbated tensions and distrust in state institutions, rather than contributing to reconciliation and peace as intended. We present a two-pronged argument, aligned with the scholarship that critically assesses external transitional justice. First, we argue that a punitive memory law adopted in such an opaque and elite-driven manner can hardly contribute to reconciliation in a divided post-war context as it does not contribute to an inclusive societal discussion about the past but merely silences the most outrageous dissenters. Second, even the minimal objectives of curbing hate speech and incitements of hatred – which some memory laws achieve – has delivered mixed results in the BiH case due to the poor design and implementation of the law. We showcase how the practical application has stalled, laying bare the weak capacities of state- level institutions, further reducing public trust in their functioning – the opposite of what was declaratively intended

Applications of Machine Learning in Human Microbiome Studies: A Review on Feature Selection, Biomarker Identification, Disease Prediction and Treatment

The number of microbiome-related studies has notably increased the availability of data on human microbiome composition and function. These studies provide the essential material to deeply explore host-microbiome associations and their relation to the development and progression of various complex diseases. Improved data-analytical tools are needed to exploit all information from these biological datasets, taking into account the peculiarities of microbiome data, i.e., compositional, heterogeneous and sparse nature of these datasets. The possibility of predicting host-phenotypes based on taxonomy-informed feature selection to establish an association between microbiome and predict disease states is beneficial for personalized medicine. In this regard, machine learning (ML) provides new insights into the development of models that can be used to predict outputs, such as classification and prediction in microbiology, infer host phenotypes to predict diseases and use microbial communities to stratify patients by their characterization of state-specific microbial signatures. Here we review the state-of-the-art ML methods and respective software applied in human microbiome studies, performed as part of the COST Action ML4Microbiome activities. This scoping review focuses on the application of ML in microbiome studies related to association and clinical use for diagnostics, prognostics, and therapeutics. Although the data presented here is more related to the bacterial community, many algorithms could be applied in general, regardless of the feature type. This literature and software review covering this broad topic is aligned with the scoping review methodology. The manual identification of data sources has been complemented with: (1) automated publication search through digital libraries of the three major publishers using natural language processing (NLP) Toolkit, and (2) an automated identification of relevant software repositories on GitHub and ranking of the related research papers relying on learning to rank approach.This study was supported by COST Action CA18131 “Statistical and machine learning techniques in human microbiome studies”. Estonian Research Council grant PRG548 (JT). Spanish State Research Agency Juan de la Cierva Grant IJC2019-042188-I (LM-Z). EO was founded and OA was supported by Estonian Research Council grant PUT 1371 and EMBO Installation grant 3573. AG was supported by Statutory Research project of the Department of Computer Networks and Systems

Applications of Machine Learning in Human Microbiome Studies: A Review on Feature Selection, Biomarker Identification, Disease Prediction and Treatment

The number of microbiome-related studies has notably increased the availability of data on human microbiome composition and function. These studies provide the essential material to deeply explore host-microbiome associations and their relation to the development and progression of various complex diseases. Improved data-analytical tools are needed to exploit all information from these biological datasets, taking into account the peculiarities of microbiome data, i.e., compositional, heterogeneous and sparse nature of these datasets. The possibility of predicting host-phenotypes based on taxonomy-informed feature selection to establish an association between microbiome and predict disease states is beneficial for personalized medicine. In this regard, machine learning (ML) provides new insights into the development of models that can be used to predict outputs, such as classification and prediction in microbiology, infer host phenotypes to predict diseases and use microbial communities to stratify patients by their characterization of state-specific microbial signatures. Here we review the state-of-the-art ML methods and respective software applied in human microbiome studies, performed as part of the COST Action ML4Microbiome activities. This scoping review focuses on the application of ML in microbiome studies related to association and clinical use for diagnostics, prognostics, and therapeutics. Although the data presented here is more related to the bacterial community, many algorithms could be applied in general, regardless of the feature type. This literature and software review covering this broad topic is aligned with the scoping review methodology. The manual identification of data sources has been complemented with: (1) automated publication search through digital libraries of the three major publishers using natural language processing (NLP) Toolkit, and (2) an automated identification of relevant software repositories on GitHub and ranking of the related research papers relying on learning to rank approach

Syndecan-4 Is Essential for Development of Concentric Myocardial Hypertrophy via Stretch-Induced Activation of the Calcineurin-NFAT Pathway

Sustained pressure overload leads to compensatory myocardial hypertrophy and subsequent heart failure, a leading cause of morbidity and mortality. Further unraveling of the cellular processes involved is essential for development of new treatment strategies. We have investigated the hypothesis that the transmembrane Z-disc proteoglycan syndecan-4, a co-receptor for integrins, connecting extracellular matrix proteins to the cytoskeleton, is an important signal transducer in cardiomyocytes during development of concentric myocardial hypertrophy following pressure overload. Echocardiographic, histochemical and cardiomyocyte size measurements showed that syndecan-4−/− mice did not develop concentric myocardial hypertrophy as found in wild-type mice, but rather left ventricular dilatation and dysfunction following pressure overload. Protein and gene expression analyses revealed diminished activation of the central, pro-hypertrophic calcineurin-nuclear factor of activated T-cell (NFAT) signaling pathway. Cardiomyocytes from syndecan-4−/−-NFAT-luciferase reporter mice subjected to cyclic mechanical stretch, a hypertrophic stimulus, showed minimal activation of NFAT (1.6-fold) compared to 5.8-fold increase in NFAT-luciferase control cardiomyocytes. Accordingly, overexpression of syndecan-4 or introducing a cell-permeable membrane-targeted syndecan-4 polypeptide (gain of function) activated NFATc4 in vitro. Pull-down experiments demonstrated a direct intracellular syndecan-4-calcineurin interaction. This interaction and activation of NFAT were increased by dephosphorylation of serine 179 (pS179) in syndecan-4. During pressure overload, phosphorylation of syndecan-4 was decreased, and association between syndecan-4, calcineurin and its co-activator calmodulin increased. Moreover, calcineurin dephosphorylated pS179, indicating that calcineurin regulates its own binding and activation. Finally, patients with hypertrophic myocardium due to aortic stenosis had increased syndecan-4 levels with decreased pS179 which was associated with increased NFAT activation. In conclusion, our data show that syndecan-4 is essential for compensatory hypertrophy in the pressure overloaded heart. Specifically, syndecan-4 regulates stretch-induced activation of the calcineurin-NFAT pathway in cardiomyocytes. Thus, our data suggest that manipulation of syndecan-4 may provide an option for therapeutic modulation of calcineurin-NFAT signaling

Applications of Machine Learning in Human Microbiome Studies: A Review on Feature Selection, Biomarker Identification, Disease Prediction and Treatment

The number of microbiome-related studies has notably increased the availability of data on human microbiome composition and function. These studies provide the essential material to deeply explore host-microbiome associations and their relation to the development and progression of various complex diseases. Improved data-analytical tools are needed to exploit all information from these biological datasets, taking into account the peculiarities of microbiome data, i.e., compositional, heterogeneous and sparse nature of these datasets. The possibility of predicting host-phenotypes based on taxonomy-informed feature selection to establish an association between microbiome and predict disease states is beneficial for personalized medicine. In this regard, machine learning (ML) provides new insights into the development of models that can be used to predict outputs, such as classification and prediction in microbiology, infer host phenotypes to predict diseases and use microbial communities to stratify patients by their characterization of state-specific microbial signatures. Here we review the state-of-the-art ML methods and respective software applied in human microbiome studies, performed as part of the COST Action ML4Microbiome activities. This scoping review focuses on the application of ML in microbiome studies related to association and clinical use for diagnostics, prognostics, and therapeutics. Although the data presented here is more related to the bacterial community, many algorithms could be applied in general, regardless of the feature type. This literature and software review covering this broad topic is aligned with the scoping review methodology. The manual identification of data sources has been complemented with: (1) automated publication search through digital libraries of the three major publishers using natural language processing (NLP) Toolkit, and (2) an automated identification of relevant software repositories on GitHub and ranking of the related research papers relying on learning to rank approach

Multi-exposure image stacks for testing HDR deghosting methods

This dataset contains a set of RAW and JPEG image stacks of multi-exposure image sequences that could be used for testing HDR reconstruction and deghosting methods. The unique feature of this dataset is that it contains both test image stacks, with motion and misalignment, and motion-free reference image stacks, which can be used with full-reference image quality metrics. In addition to that, we also provide the results of the subjective quality assessment experiment peformed on the set of merged HDR images. Please check the README.txt file for mode details

An evaluation framework for design-time context-adaptation of process modelling languages

© IFIP International Federation for Information Processing 2017. To enhance the performance and efficiency of business processes, it is essential to take the dynamics of their execution context into account during process modelling. This paper first proposes an evaluation framework that identifies the main requirements for supporting the modelling of context-adaptive processes. Using this framework, we analyse four popular business process modelling languages: Coloured Petri Nets (CPN), Business Process Modelling and Notation 2.0 (BPMN), Yet Another Workflow Language (YAWL), and Unified Modelling Language Activity Diagrams (UML AD). The analysis is carried out by evaluating how the respective language notations fulfil the identified requirements in several real-life scenarios. Lastly, a comparative analysis of the languages focussed on their support for modelling context-adaptive business processes is provided.status: publishe

Modeling a Clinical Pathway for Contraception

BACKGROUND: The Centers for Disease Control and Prevention (CDC) produced a 72-page document titled "U.S. Selective Practice Recommendations for Contraceptive Use" in 2016. This document contains the medical eligibility criteria (MEC) for contraceptive initiation or continuation based on a patient's current health status. Notations such as Business Process Model and Notation (BPMN) and Decision Model and Notation (DMN) might be useful to model such recommendations. OBJECTIVE: Our objective was to use BPMN and DMN to model and standardize the processes and decisions involved in initiating birth control according to the CDC's MEC for birth control initiation. This model could then be incorporated into an electronic health records system or other digital platform. METHODS: Medical terminology, processes, and decisions were modeled in coordination with the CDC to ensure correctness. Challenges in terminology bindings were identified and categorized. RESULTS: A model was successfully produced. Integration of clearly defined data elements proved to be the biggest challenge. CONCLUSION: BPMN and DMN have strengths and weaknesses when modeling medical processes; however, they can be used to successfully create models for clinical pathways.status: publishe

Mapping the Bosnian-Herzegovinian Diaspora: Utilizing the Socio-Economic Potential of the Diaspora for Development of BiH

The study is the result of research performed by an international and interdisciplinary team of researchers including Dr. Hariz Halilovich, Dr. Jasmin Hasic, Dr. Dzeneta Karabegovic;, Dr. Ajlina Karamehic;-Muratovic, and Dr. Nermin Oruc; under the coordination of the International Organization for Migration, Mission to BiH and the Ministry of Human rights and Refugees of Bosnia and Herzegovina, within the framework of the project "Mainstreaming the Concept on Migration and Development into Relevant Policies, Plans and Actions in Bosnia and Herzegovina (BiH): Diaspora for Development (D4D)". The project aims to strengthen the role of BiH diaspora in development processes in BiH. The Diaspora for Development (D4D) is a project of the Government of Switzerland and the Ministry of Human Rights and Refugees of BiH, in partnership with UNDP BiH and IOM BiH. The content of this publication, including the findings presented in this report, do not necessarily reflect the views of the the Ministry of Human Rights and Refugees of BiH, the Government of Switzerland, the UNDP in BiH and the IOM in BiH