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.

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.

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.

The growing interest of the food, pharmaceutical and cosmetics industries in naturally occurring bioactive compounds or secondary plant metabolites also leads to a growing demand for the development of new and more effective analysis and isolation techniques. The extraction of bioactive compounds from plant material has always been a challenge, accompanied by increasingly strict control requirements for the final products and a growing interest in environmental protection. However, great efforts have been made in this direction and today a considerable number of innovative extraction techniques have been developed using green, environmentally friendly solvents. These solvents include the deep eutectic solvents (DES) and their natural equivalents, the natural deep eutectic solvents (NADES). Due to their adjustable physical-chemical properties and their green character, it is expected that DES/NADES could be the most widely used solvents in the future, not only in extraction processes but also in other research areas such as catalysis, electrochemistry or organic synthesis. Consequently, this review provided an up-to-date systematic overview of the use of DES/NADES in combination with innovative extraction techniques for the isolation of bioactive compounds from various plant materials. The topicality of the field was confirmed by a detailed search on the platform WoS (Web of Science), which resulted in more than 100 original research papers on DES/NADES for bioactive compounds in the last three years. Besides the isolation of bioactive compounds from plants, different analytical methods are presented and discussed.

Antibiotics are widely used and inaccurate or inappropriate prescription of antibiotics causes a significant increase in the prevalence of multidrug‐resistant bacterial infections among children. This research aimed to study antibiotic prescriptions in hospitalised paediatric patients and to determine the prevalence of inappropriate antimicrobial use and the main types of prescribing errors.

Objective: Glutamate-induced excitotoxicity has a role in the pathophysiology of neurodegenerative disorders. Lamotrigine, an antiepileptic drug, also used to treat bipolar disorders, may be protective against excitotoxic insult. The aim of the study was to investigate the neuroprotective effect of lamotrigine against the glutamate excitotoxicity in SH-SY5Y cell line. Materials and Methods: SH-SY5Y human neuroblastoma cells were pre-treated with lamotrigine (50-100-150 μM) prior to exposure to 15 mM glutamate. The 3-(4,5-dimethythiazol – 2-yl)-2,5 – diphenyl tetrazolium bromide (MTT) assay was performed to determine cell viability. The anti-oxidant effect of lamotrigine and the role of inflammatory parameters were determined by measuring superoxide dismutase (SOD), hydrogen peroxide (H2O2), IL-1β, IL-6 and TNF-α. Results: Intracellular calcium levels and lactate dehydrogenase (LDH) activity increased in glutamate exposed cells. Pre-treatment of cells with MK-801 showed no protective features against glutamate excitotoxicity. Treatment with 100 μM lamotrigine was effective in increasing the viability of glutamate exposed cells and in reducing H2O2 increase in these cells. The SOD activity increased by lamotrigine treated cells exposed to glutamate. IL-1β, IL-6 and TNF-α levels increased after induction with glutamate and attenuated by lamotrigine. Conclusion: Overall, our results confirmed the critical role of inflammation and oxidative stress in glutamate-induced excitotoxicity and lamotrigine may exert a protective effect. Keywords: Lamotrigine, Glutamate excitotoxicity,

Bu calisma, panel VAR modelini kullanarak, 1995-2014 doneminde turist girisleri acisindan dunyanin en yuksek siralamasina sahip 10 ulke icin turizm, enerji tuketimi, ekonomik buyume ve CO2 emisyonlari arasindaki nedensel iliskileri arastirmaktadir. Bulgular, turizm ile kirletici yayim arasinda oldugu kadar enerji ile kirletici yayim arasinda cift yonlu bir nedensel baglanti oldugunu one surerken, sonuclar ekonomik buyumeden karbondioksit emisyonuna dogru tek yonlu bir nedensellik oldugunu gostermektedir. Ayni zamanda etki-tepki analizi, karbondioksit emisyonlarinin ekonomik buyume ve enerji tuketimindeki sarsintilara verdigi tepkilerinin on yillik donemde pozitif gorundugunu, turizmdeki sarsintilara tepkinin ise ilk dort yil icinde olumsuz gorundugunu, ancak besinci yilda dengeye dondugunu gostermektedir. Bu sonuc surdurulebilir turizmi destekleyen politika yapicilar icin buyuk onem tasiyabilir.

A worldwide increase in prevalence of allergic diseases has led to adaptations in national and international health care systems. ARIA (Allergic Rhinitis and Its Impact on Asthma) initiative develops internationally applicable guidelines for allergic respiratory diseases. In collaboration with international initiatives, ARIA offers updates of real-life integrated care pathways (ICPs) for digitally assisted, integrated, and individualized treatment of allergic rhinitis (AR). This article presents certain aspects of the health care system in Kuwait with reference to the management of AR and the objective of introducing ICPs and adopting the latest ARIA recommendations. Guidelines for ICPs include aspects of patients and health care providers and cover key areas of management of AR. This model of guidelines supports real-life health care better than traditional models. ARIA recommendations will be locally integrated in the health care system with the aim of improving both pharmacotherapy and allergy immunotherapy.