Stable isotope-assisted approaches can improve untargeted liquid chromatography-high resolution mass spectrometry (LC-HRMS) metabolomics studies. Here, we demonstrate at the example of chemically stressed wheat that metabolome-wide internal standardization by globally 13C-labeled metabolite extract (GLMe-IS) of experimental-condition-matched biological samples can help to improve the detection of treatment-relevant metabolites and can aid in the post-acquisition assessment of putative matrix effects in samples obtained upon different treatments. For this, native extracts of toxin- and mock-treated (control) wheat ears were standardized by the addition of uniformly 13C-labeled wheat ear extracts that were cultivated under similar experimental conditions (toxin-treatment and control) and measured with LC-HRMS. The results show that 996 wheat-derived metabolites were detected with the non-condition-matched 13C-labeled metabolite extract, while another 68 were only covered by the experimental-condition-matched GLMe-IS. Additional testing is performed with the assumption that GLMe-IS enables compensation for matrix effects. Although on average no severe matrix differences between both experimental conditions were found, individual metabolites may be affected as is demonstrated by wrong decisions with respect to the classification of significantly altered metabolites. When GLMe-IS was applied to compensate for matrix effects, 272 metabolites showed significantly altered levels between treated and control samples, 42 of which would not have been classified as such without GLMe-IS.

Double strand break (DSB) repair mainly occurs through 3 pathways: non-homologous end-joining (NHEJ), alternative end-joining (Alt-EJ), and homologous recombination (HR). We present an assay system that enables simultaneous measurement of all three pathways using Cas9-generated DSBs and next generation sequencing to profile and quantify pathway choice. The assay system has provided several insights. First, absence of the key Alt-EJ factor Pol q only abrogates ~50% of total Alt-EJ. Second, single-strand templated repair (SSTR) requires BRCA1 and MRE11 activity, but not BRCA2, establishing that SSTR commonly used in genome editing is not conventional HR. Third, BRCA1 promotes Alt-EJ usage at two-ended DSBs in contrast to BRCA2. These fundamental differences between BRCA1 and BRCA2 deficiency have implications for therapeutic targeting of HR-deficient cancers. This assay can be used in any system which permits Cas9 delivery and, importantly, allows rapid genotype-to-phenotype correlation in isogenic cell line pairs.

To increase the ecological validity of outcomes from laboratory evaluations of hearing and hearing devices, it is desirable to introduce more realistic outcome measures in the laboratory. This article presents and discusses three outcome measures that have been designed to go beyond traditional speech-in-noise measures to better reflect realistic everyday challenges. The outcome measures reviewed are: the Sentence-final Word Identification and Recall (SWIR) test that measures working memory performance while listening to speech in noise at ceiling performance; a neural tracking method that produces a quantitative measure of selective speech attention in noise; and pupillometry that measures changes in pupil dilation to assess listening effort while listening to speech in noise. According to evaluation data, the SWIR test provides a sensitive measure in situations where speech perception performance might be unaffected. Similarly, pupil dilation has also shown sensitivity in situations where traditional speech-in-noise measures are insensitive. Changes in working memory capacity and effort mobilization were found at positive signal-to-noise ratios (SNR), that is, at SNRs that might reflect everyday situations. Using stimulus reconstruction, it has been demonstrated that neural tracking is a robust method at determining to what degree a listener is attending to a specific talker in a typical cocktail party situation. Using both established and commercially available noise reduction schemes, data have further shown that all three measures are sensitive to variation in SNR. In summary, the new outcome measures seem suitable for testing hearing and hearing devices under more realistic and demanding everyday conditions than traditional speech-in-noise tests.

This paper investigates a secure data exchange between many small distributed consumers/prosumers and the aggregator in the process of energy balancing. It addresses the challenges of ensuring data exchange in a simple, scalable, and affordable way. The communication platform for data exchange is using Ethereum Blockchain technology. It provides a distributed ledger database across a distributed network, supports simple connectivity for new stakeholders, and enables many small entities to contribute with their flexible energy to the system balancing. The architecture of a simulation/emulation environment provides a direct connection of a relational database to the Ethereum network, thus enabling dynamic data management. In addition, it extends security of the environment with security mechanisms of relational databases. Proof-of-concept setup with the simulation of system balancing processes, confirms the suitability of the solution for secure data exchange in the market, operation, and measurement area. For the most intensive and space-consuming measurement data exchange, we have investigated data aggregation to ensure performance optimisation of required computation and space usage.

One of the major goals in the European Union for reducing greenhouse gas emissions is the electrification of heat. Therefore, it is expected that the winter peak demand will rise significantly in the next few years. Demand Response could play an important role in reducing the need for network reinforcements by providing flexibility. The major motivation behind this paper is to evaluate the difference in demand flexibility between temperature-dependent consumers using electricity for heating and consumers using other energy sources. In this paper, temperature-dependent consumers are first identified by analyzing their smart metering data with machine learning. Further, the response of consumers is evaluated using probabilistic baseline models. The results show that heat electrification will increase the demand during low temperatures, whereas these consumers will also be able to offer far more flexibility during low temperatures and high demand. To the best of our knowledge, there is no empirical study, that would investigate these using state of the art methods in such detail. The paper presents part of the analyses that were carried out after the real demand response program in the scope of the Slovenian-Japanese NEDO project.

In this paper, we propose a global digital platform to avoid and combat epidemics by providing relevant real-time information to support selective lockdowns. It leverages the pervasiveness of wireless connectivity while being trustworthy and secure. The proposed system is conceptualized to be decentralized yet federated, based on ubiquitous public systems and active citizen participation. Its foundations lie on the principle of informational self-determination. We argue that only in this way it can become a trustworthy and legitimate public good infrastructure for citizens by balancing the asymmetry of the different hierarchical levels within the federated organization while providing highly effective detection and guiding mitigation measures toward graceful lockdown of the society. To exemplify the proposed system, we choose a remote patient monitoring as use case. This use case is evaluated considering different numbers of endorsed peers on a solution that is based on the integration of distributed ledger technologies and NB-IoT (narrowband IoT). An experimental setup is used to evaluate the performance of this integration, in which the end-to-end latency is slightly increased when a new endorsed element is added. However, the system reliability, privacy, and interoperability are guaranteed. In this sense, we expect active participation of empowered citizens to supplement the more usual top-down management of epidemics.

This article aims to provide a thorough overview of the use of Artificial Intelligence (AI) techniques in studying the gut microbiota and its role in the diagnosis and treatment of some important diseases. The association between microbiota and diseases, together with its clinical relevance, is still difficult to interpret. The advances in AI techniques, such as Machine Learning (ML) and Deep Learning (DL), can help clinicians in processing and interpreting these massive data sets. Two research groups have been involved in this Scoping Review, working in two different areas of Europe: Florence and Sarajevo. The papers included in the review describe the use of ML or DL methods applied to the study of human gut microbiota. In total, 1109 papers were considered in this study. After elimination, a final set of 16 articles was considered in the scoping review. Different AI techniques were applied in the reviewed papers. Some papers applied ML, while others applied DL techniques. 11 papers evaluated just different ML algorithms (ranging from one to eight algorithms applied to one dataset). The remaining five papers examined both ML and DL algorithms. The most applied ML algorithm was Random Forest and it also exhibited the best performances.

Introduction: Insufficient physical activity is one of the leading public health problems in the world, but also in Bosnia and Herzegovina. Modern civilization is characterized by a significant decrease in physical activity, and the number of people whose lifestyle can be called sedentary has never been higher, which is especially emphasised among children and adolescents. Aim of the study is to examine public health significance of physical activity on the occurrence and the degree of obesity in children and adolescents in primary and secondary schools and to determine the applicability of the Fels questionnaire on physical activity of children in rural areas of Bosnia and Herzegovina. Methods: We used a transversal research method of a cross-sectional study at a one-time point, and for obtaining results we used the Fels physical activity questionnaire for children and measurement protocol. Results: 276 primary and secondary school students in two cities participated in this survey. Respondents in Busovaca are more physically active than their peers in Sarajevo. One-third of the total number of respondents is overweight and obese, and respondents in Sarajevo are significantly more nourished than their peers in Busovaca. The Fels questionnaire is conditionally applicable, especially in rural areas. Conclusion: This study confirmed that the Fels questionnaire for assessing the level of physical activity for children and young people, which is the general instrument for research of physical activity in children, is too generalized because it is based on a homogeneous urban population.

Electricity sector has been facing many changes over the last two decades due to rise in penetration of distributed energy resources that significantly affect the operations of distribution grids. Increase in intermittent electricity production from renewable energy sources, requires activating the flexibility contained in the distributed energy resources. Local electricity market as well as demand response present a mechanism to utilize this flexibility. In this paper, we analyze potentials of energy exchange within energy community created at medium voltage feeder of Elektroprivreda BH – d.d. Sarajevo. We use software tool PVSOL Premium to model prosumers and Python for the analysis of power flows and voltage conditions. As a result, we propose the energy community interaction matrix providing the information about prosumers and consumers as a foundation for automation of local energy exchange within the energy community.

Due to limited computational resources, acoustic models of early automatic speech recognition ( ASR ) systems were built in low-dimensional feature spaces that incur considerable information loss at the outset of the process. Several comparative studies of automatic and human speech recognition suggest that this information loss can adversely affect the robustness of ASR systems. To mitigate that and allow for learning of robust models, we propose a deep 2 D convolutional network in the waveform domain. The first layer of the network decomposes waveforms into frequency sub-bands, thereby representing them in a structured high-dimensional space. This is achieved by means of a parametric convolutional block defined via cosine modulations of compactly supported windows. The next layer embeds the wave-form in an even higher-dimensional space of high-resolution spectro-temporal patterns, implemented via a 2 D convolutional block. This is followed by a gradual compression phase that selects most relevant spectro-temporal patterns using wide-pass 2 D filtering. Our results show that the approach significantly out-performs alternative waveform-based models on both noisy and spontaneous conversational speech ( 24% and 11% relative error reduction, respectively). Moreover, this study provides empirical evidence that learning directly from the waveform domain could be more effective than learning using hand-crafted features.

Deep scattering spectrum consists of a cascade of wavelet transforms and modulus non-linearity. It generates features of different orders, with the first order coefficients approximately equal to the Mel-frequency cepstrum, and higher order coefficients recovering information lost at lower levels. We investigate the effect of including the information recovered by higher order coefficients on the robustness of speech recognition. To that end, we also propose a modification to the original scattering transform tailored for noisy speech. In particular, instead of the modulus non-linearity we opt to work with power coefficients and, therefore, use the squared modulus non-linearity. We quantify the robustness of scattering features using the word error rates of acoustic models trained on clean speech and evaluated using sets of utterances corrupted with different noise types. Our empirical results show that the second order scattering power spectrum coefficients capture invariants relevant for noise robustness and that this additional information improves generalization to unseen noise conditions (almost 20% relative error reduction on AURORA 4). This finding can have important consequences on speech recognition systems that typically discard the second order information and keep only the first order features (known for emulating MFCC and FBANK values) when representing speech.