Biochemical characterizations of selected plant species from the genera Triticum, Avena and Triticosecale under conditions of heat stress

Porast temperature na globalnom nivou negativno utiče na prinos žitarica širom sveta. Istraživanja povezana sa načinom odgovora žitarica na toplotni stres su važna i omogućavaju razumevanje biohemijske osnove toplotne tolerancije kod žitarica. Ona otvaraju mogućnost za korišćenje novih znanja u efikasnom odabiru komercijalnih sorti žitarica kao i mogućnost za kreiranjem novih toplotno-tolerantnih sorti žitarica. Predmet istraživanja ove doktorske disertacije je uticaj toplotnog stresa na različite sorte žitarica i njihov biohemijski i molekulski odgovor u toku tri eksperimentalne godine, sa ciljem širenja fundamentalnih znanja o biohemijskoj osnovi toplotne tolerancije kod žitarica. U toku trogodišnje analize praćen je uticaj toplotnog stresa na veći broj parametara biljnih zaštitnih mehanizama i na elemente prinosa. Utvrđeno je da su antioksidativni i zaštitni mehanizmi bili izraženiji u toku toplijih godina istraživanja (2016. i 2018.) u odnosu na 2017. godinu koja je bila umerenija, što ukazuje da temperatura značajno utiče na analizirane parametre. Na osnovu rezultata utvrđeno je da toplotni stres utiče na pad vrednosti proteina i fotosintetičkih pigmenata u listu žitarica, a dovodi do porasta prolina i malondialdehida. Imunoblot analiza ekspresije eEF1A, EF-Tu, HSP101 i HSP18 je pokazala da dolazi do značajnog povećanja njihove ekspresije u uslovima toplotnog stresa kod žitarica. U uslovima toplotnog stresa došlo je i do povećanja aktivnosti antioksidativnih enzima. Rezultati ove doktorske disertacije, sugerišu da se analizirane sorte žitarica značajno razlikuju u sposobnosti reagovanja na toplotni stres, što može biti korisno za razvoj sorti primenom biotehnologije i programa oplemenjivanja, kako bi se dobile nove sorte otpornije na visoku temperaturu.Rising temperatures on global scale are affecting cereal yields worldwide. Research related to cereals respond to heat stress is very important because they provide an understanding of the biochemical basis of heat tolerance in cereals. This research opens the possibility for the use of new knowledge in the efficient selection of commercial varieties of cereals as well as the possibility for the creation of new heat-tolerant cereals varieties. The subject of this doctoral dissertation is the influence of heat stress on different varieties of cereals and their biochemical and molecular response during the three experimental years, with the aim of expanding fundamental knowledge about the biochemical basis of heat tolerance in cereals. During the three years of analysis, the influence of heat stress on several parameters of plant protecting mechanisms and on yield elements was investigated. It was found that antioxidative and protective mechanisms were more induced during the warmer years of research (2016 and 2018) compared to moderate year 2017, which indicates that the temperature significantly affected the analyzed parameters. Based on the results, it was found that heat stress affects the decrease in proteins and photosynthetic pigments in cereal leaves and leads to an increase in proline and malondialdehyde content. Immunoblot analysis of the expression of eEF1A, EFTu, HSP101 and HSP18 showed that there is a significant increase in their expression under conditions of heat stress in cereals. Under the conditions of heat stress, there was an increase in the activity of antioxidative enzymes. The results of this doctoral dissertation suggest that the analyzed cereal varieties differ significantly in their ability to respond to heat stress, which could be useful for development of varieties using biotechnology through breeding programs, to obtain new varieties more resistant to high temperatures

Merenje performansi u sportu primenom kinematičkih senzora

The main aim of this work was to determine the potential of kinematic sensors regarding estimation of bio/motor abilities and measurement of movement kinematics in precision, rapid movement. and complex tasks...Glavni cilj ovog rada je da se utvrdi potencijal kinematičkih senzorau odnosu na procenu bio-motoričkih sposobnosti i merenje kinematike kretanja u preciznim, brzim pokretima i kompleksnim motoričkim zadacima..

Effectiveness of Expressive Writing in the Reduction of Psychological Distress During the COVID-19 Pandemic: A Randomized Controlled Trial

Objective Due to the wide impact of the COVID-19 pandemic on mental health, the need for scalable interventions that can effectively reduce psychological distress has been recognized. Expressive writing (EW) can be beneficial for different conditions, including depression, suicidal ideation, and coping with trauma. Therefore, we aim to assess the applicability and effectiveness of an online format of EW in the reduction of psychological distress in context of the COVID-19 pandemic. Methods In this parallel-group, randomized controlled trial, participants (n = 120) were randomly allocated to (1) the intervention group-who completed five EW sessions over the 2 week period-or (2) the control group-who received treatment as usual (TAU). Participants were assessed for primary and secondary outcome measures at baseline, post-treatment, and follow-up-1-month after the treatment. The primary outcome was severity of psychological distress assessed at post-treatment, operationalized as Depression Anxiety Stress Scale (DASS) summary score. Secondary outcomes were severity of depression, anxiety, and stress (DASS subscale scores), well-being (WHO-5), subjective perception of quality of life (SQOL), and subjective evaluation of difficulties coping with pandemic, which were also assessed at post-treatment. Per protocol, analysis was conducted with available cases only. Results A less favorable outcome was found in the intervention group on psychological distress, and symptoms of stress, after controlling for baseline scores. Increased stress was recorded in the treatment group, with no effect in the control group. There was no significant difference between the groups on depression, anxiety, well-being, and subjective quality of life. No group effect for any of the outcomes measures was recorded at follow-up. Additional analysis revealed moderation effects of age and gender with older and male participants scoring higher on distress measures. Conclusion Engaging in EW during the pandemic was found to elevate stress; thus, when applied in the context of the COVID-19 pandemic, it may be harmful. Hence, EW or similar self-guided interventions should not be applied without prior evidence on their effects in the context of a pandemic and similar stressful and unpredictable circumstances

QUALITATIVE AND QUANTITATIVE EVALUATION OF THE CHARACTERISTICS OF THE ISOMETRIC MUSCLE FORCE OF DIFFERENT MUSCLE GROUPS IN CADET JUDO ATHLETES: A GENDER-BASED MULTIDIMENSIONAL MODEL

The aim of this paper is to define the initial quantitative and qualitative multidimensional model for evaluating basic contractile characteristics of isometric muscle force in systematically trained and selected cadet judo athletes. In this research absolute values of the obtained results, and values derived by relativization of absolute values in relation to skeletal muscle mass (SMM) were considered. The basic method used in this research was laboratory testing. All data sampling was performed by the dynamometry method, using tensiometric probes. The research sample in this study consisted of 21 cadet judo athletes, of which 14 were male and 7 were female. All measurements were performed using standardized testing procedures on the following muscle groups: flexor muscles of the left (HGL) and the right hand (HGR), back (DL) and leg extensor muscles (LE) and ankle joint plantar flexor muscles (PF). Based on the obtained results, separate multidimensional mathematical models for the estimation of contractile potential and development level were defined for both basic characteristics of isometric muscle force: maximal isometric muscle force (Fmax) and maximal explosive isometric muscle force (RFDmax). A qualitative assessment of contractile potential for each of the tested muscle groups, i.e. variables, was enabled by defining standard values for 7 distinct preparedness levels for both basic isometric muscle force contractile characteristics of male and female cadet judo athletes

Bayesian sparsification for deep neural networks with Bayesian model reduction

Deep learning's immense capabilities are often constrained by the complexity of its models, leading to an increasing demand for effective sparsification techniques. Bayesian sparsification for deep learning emerges as a crucial approach, facilitating the design of models that are both computationally efficient and competitive in terms of performance across various deep learning applications. The state-of-the-art -- in Bayesian sparsification of deep neural networks -- combines structural shrinkage priors on model weights with an approximate inference scheme based on stochastic variational inference. However, model inversion of the full generative model is exceptionally computationally demanding, especially when compared to standard deep learning of point estimates. In this context, we advocate for the use of Bayesian model reduction (BMR) as a more efficient alternative for pruning of model weights. As a generalization of the Savage-Dickey ratio, BMR allows a post-hoc elimination of redundant model weights based on the posterior estimates under a straightforward (non-hierarchical) generative model. Our comparative study highlights the advantages of the BMR method relative to established approaches based on hierarchical horseshoe priors over model weights. We illustrate the potential of BMR across various deep learning architectures, from classical networks like LeNet to modern frameworks such as Vision Transformers and MLP-Mixers

Optimization-simulation approach to solving stochastic programming problems

Стохастичко програмирање је део операционих истраживања које се бави начином на који је могуће укључити неизвесност у процес доношења одлука и које прихвата чињеницу да доносиоцу одлуке неће увек бити доступне све потребне информације. Основни проблем у примени стохастичких модела произилази из неизвесности параметара и чињенице да се оптимално решење дефинише и добија за детерминистички двојник (представник) оригинала. Проблем је оценити квалитет решења одређеног детерминистичког двојника са становишта вредности критеријумске функције, која може бити случајног карактера, као и са становишта вероватноће задовољења стохастичких ограничења. Проблеми стохастичког програмирања се појављују у различитим областима, али неки од најчешће решаваних проблема су у области планирања производње, ланца снабдевања, логистике, транспорта, управљање портфолиом, маркетинга и уопште у области финансија као и у многим другим областима. Приступи решавању проблема стохастичког програмирања се могу поделити у три основна правца: стохастичка оптимизација, робусна оптимизација и вероватносно задовољење ограничења (chance constrained programming) и који представљају полазну тачку свих даљих истраживања у овој области оптимизације. Робусни приступ је конзервативни приступ који је оријентисан на најгори могући сценарио уз дефинисање таквог детерминистичког двојника оригиналног проблема у коме се елиминише сва неизвесност из модела. Вероватносно задовољење ограничења је приступ који посебно третира неизвесност која се јавља у параметрима ограничења и посебно се бави решавањем таквих проблема. Основна претпоставка у овом приступу је да је потребно задовољити неко ограничење које је неизвесно, са најмање унапред одређеном вероватноћом. Повод за развој и примену приступа вероватносног задовољења ограничења је потреба да се скуп ограничења опише у смислу дефинисања вероватноће задовољења ограничења која представља ризик који је доносилц одлуке спреман да прихвати да добијено оптимално решење неће бити допустиво. Основни и најзахтевнији изазов приступа вероватносног задовољења ограничења је његова рачунска изводљивост, која је пре свега повезана са могућношћу проналажења расподеле вероватноће случајних променљивих...Stochastic programming is a part of the operational research which investigates ways to incorporate uncertainty in the process of decision-making and that accepts the fact that the decision maker will not always have all the information needed readily available. The main problem in application of stochastic programming comes from the uncertainty of parameters in model and the fact that optimal solution is defined for the deterministic equivalent (double) of the original problem. Another problem is to evaluate the quality of a specific deterministic equivalent from the perspective of the value of criterion function, that can be a random, as well as from the perspective of probability of satisfying stochastic constraints. Stochastic programming is applied in many areas, and some of the most common problems solved using stochastic programming are in the fields of production planning, supply chains, logistics, transportation, portfolio management, marketing and in the field of finance, and many other areas. There are three common approaches in solving stochastic programming problems: stochastic optimization, robust optimization and chance constrained programming, and they represent the starting point of all the research in this field of optimization. Robust optimization is a conservative approach that is orientated on the worst case scenario by defining such a deterministic equivalent of the original problem that removes all uncertainty from the model. Chance constrained programming is an approach that treats uncertainty in the parameters of the constraints in the model and uses different techniques in solving these problems. The basic presumption in this approach is that a certain constraint, which is stochastic and uncertain, has to be satisfied with a predefined probability. The reason for developing and application of such an approach is the need to describe the constraints in such a manner that the predefined probability of satisfying constraints is actually a risk that the solution obtained won‘t be satisfied and which the decision maker is willing to accept. The main and most challenging part of the chance constraint approach is tractability, that is above all connected to the possibility of finding the appropriate probability distributions of the stochastic parameters

Revealing human sensitivity to a latent temporal structure of changes

Precisely timed behavior and accurate time perception plays a critical role in our everyday lives, as our wellbeing and even survival can depend on well-timed decisions. Although the temporal structure of the world around us is essential for human decision making, we know surprisingly little about how representation of temporal structure of our everyday environment impacts decision making. How does the representation of temporal structure affect our ability to generate well-timed decisions? Here we address this question by using a well-established dynamic probabilistic learning task. Using computational modeling, we found that human subjects' beliefs about temporal structure are reflected in their choices to either exploit their current knowledge or to explore novel options. The model-based analysis illustrates a large within-group and within-subject heterogeneity. To explain these results, we propose a normative model for how temporal structure is used in decision making, based on the semi-Markov formalism in the active inference framework. We discuss potential key applications of the presented approach to the fields of cognitive phenotyping and computational psychiatry