Since their introduction to the United States in 2008, synthetic cannabinoids became the most widely used recreational drug behind marijuana, then regressed to an estimated prevalence of less than 1%. Contrary to expectations for a drug declining in use, emergency department presentations and acute poisonings related to the use of synthetic cannabinoids are increasing. Alongside this phenomenon, a growing body of literature is beginning to uncover a relationship between psychosis and synthetic cannabinoid use. A current gap in the literature exists surrounding harm prevention methods and targeted intervention strategies for users of synthetic cannabinoids. To date, no known studies have examined individuals with a history of use of these substances and investigated the reasons they decided to discontinue recreational use. The purpose of the current study was to fill this gap in the literature while also further confirming and expanding existing research on the characterization of synthetic substance use, perceived harm of synthetic cannabinoids, and users’ knowledge about synthetic cannabinoids. Cross sectional survey methods in a non-experimental comparative design was utilized with participants recruited through the online crowd sourcing platform Amazon MTurk. Significant motivating factors for both discontinuation and continuation of synthetic cannabinoid use were found including personal experience, accessibility, preference towards other substance, and questions surrounding the source and purity of the synthetic cannabinoids. It was also found that individuals who currently use synthetic cannabinoids have less general knowledge about the substance class when compared to individuals who have discontinued use. These results suggest that psychoeducational campaigning surrounding general knowledge about the substance class as well as information on the physiological effects of synthetic cannabinoids may be an effective harm reduction method.

abstract: Gene circuit engineering facilitates the discovery and understanding of fundamental biology and has been widely used in various biological applications. In synthetic biology, gene circuits are often constructed by two main strategies: either monocistronic or polycistronic constructions. The Latter architecture can be commonly found in prokaryotes, eukaryotes, and viruses and has been largely applied in gene circuit engineering. In this work, the effect of adjacent genes and noncoding regions are systematically investigated through the construction of batteries of gene circuits in diverse scenarios. Data-driven analysis yields a protein expression metric that strongly correlates with the features of adjacent transcriptional regions (ATRs). This novel mathematical tool helps the guide for circuit construction and has the implication for the design of synthetic ATRs to tune gene expression, illustrating its potential to facilitate engineering complex gene networks. The ability to tune RNA dynamics is greatly needed for biotech applications, including therapeutics and diagnostics. Diverse methods have been developed to tune gene expression through transcriptional or translational manipulation. Control of RNA stability/degradation is often overlooked and can be the lightweight alternative to regulate protein yields. To further extend the utility of engineered ATRs to regulate gene expression, a library of RNA modules named degradation-tuning RNAs (dtRNAs) are designed with the ability to form specific 5’ secondary structures prior to RBS. These modules can modulate transcript stability while having a minimal interference on translation initiation. Optimization of their functional structural features enables gene expression level to be tuned over a wide dynamic range. These engineered dtRNAs are capable of regulating gene circuit dynamics as well as noncoding RNA levels and can be further expanded into cell-free system for gene expression control in vitro. Finally, integrating dtRNA with synthetic toehold sensor enables improved paper-based viral diagnostics, illustrating the potential of using synthetic dtRNAs for biomedical applications. / Dissertation/Thesis / Doctoral Dissertation Biomedical Engineering 2020

Cell-free transcription and translation systems promise to accelerate and simplify the engineering of synthetic proteins, biological circuits or metabolic pathways. Microfluidic droplet platforms can generate millions of reactions in parallel. This allows cell-free reactions to be miniaturized down to picoliter volumes. Nevertheless, the true potential of microfluidics have not been reached for cell-free bioengineering. Better approaches are needed for reaching sufficient in-drop expression levels while efficiently creating DNA diversity among droplets. This work develops a droplet microfluidic platform for single or multiple protein expression from a single DNA coated bead per droplet. This opens up the possibility to diversify a million droplets for synthetic biology applications.

1 Master Thesis Proposal Institute of Political Studies, IEPS programme Faculty of Social Sciences Charles University in Prague Date: 16.10.2020 Author: Ceren Küçükkayıkcı Supervisor: doc. Ing. Tomas Cahlik, CSc. E-mail: 91064497@fsv.cuni.cz E-mail: cahlik@fsv.cuni.cz Phone: +420773924728 Phone: Specialisation: IEPS Defense Planned: September 2021 Proposed Topic: The Impact of Political Determinants on Economic Growth : Analysis of AKP Era Registered in SIS: Yes Date of registration: 30.05.2019 (in case of No give an expected date) Topic characteristics / Research Question(s): The general question of this thesis is how the change in political determinants have an impact on economic growth in Turkey during the AKP era between 2002 and 2019. We would like to demonstrate the reason for the economic failure of AKP in line with the political and economic institutions and political and civil rights. AKP era will be analyzed into three sections, which are 2003-2007, 2007-2011, and 2011 and 2019. Economic growth will be measured by GDP per capita. Working hypotheses: 1. Hypothesis #1: There is an indirect relationship between political determinants and economic growth. 2. Hypothesis#2: Economic institutions and political institutions are endogenous and are defined by the collective choice of society. 2 3....

Strigolactones (SLs) are a class of plant hormones regulating several aspects of plant growth and development according to nutrient availability, particularly the modulation of root and shoot architectures. Under nutrient deficiency, SLs are abundantly released into the soil to recruit a plant-beneficial partner, arbuscular mycorrhizal fungi (AMF), and establish plant-AMF symbiosis that provides the plant with minerals and water. However, released SLs are also seed germination signals for the root parasitic plants Orobanchacea family and pave their way to the host plants’ roots. “New comers” in the field of plant hormones, their large structural variety intrigues and led to ask why plants produce many different types of SLs. In this work, we generated tools that can help to link the SL structural diversity with their biological function(s). The most common way to evaluate SL activity is based on their ability to be parasitic seeds’ germination stimulants. Despite being the most sensitive assay for SL quantification, parasitic seed-based bioassays are laborious and time-consuming as performed usually manually. Therefore, we developed a detection algorithm, SeedQuant, which identifies and counts germinated and non-germinated seeds 600 times faster than a trained human; thus, reducing the data processing from days down to minutes. To gain quantitative insights in SL perception, depending on the structural diversity, we developed a precise and detailed protocol for the use of a genetically encoded biosensor in Arabidopsis protoplast, StrigoQuant. StrigoQuant takes advantage of the SL-dependent degradation of the repressor protein AtSMXL6 coupled with luciferase reporter proteins. We also tried to adapt this molecular sensor to the rice repressor protein D53, but the use of rice protoplasts made it very challenging. Moreover, to better understand the later steps in SL biosynthesis in vivo, we generated CRISPR/Cas9-based rice mutants and shed light on the biological function of different SLs at the organismal level. MAX1-900 mutants defined the minor role of the canonical SL 4-deoxyorobanchol (4DO) - a major plant SL - in determining rice architecture, while being a crucial contributor to rhizospheric interactions. Finally, we reviewed other strategies to decipher plant signaling pathways in general.

Synthetic Aperture Focusing Technique (SAFT) is a powerful method to createfocused images of the inside of opaque samples by using delay-and-sum of acquireddata. It gives a high resolution and when using a generation laser and a detectionlaser it is also non-contact. This thesis was made at Swerim, and the aim wasto create an 3D-SAFT algorithm and to visualise the reconstructed image. Twosamples were used, both were 3D-printed with known defects that varied in sizefrom 0.05 mm to 1 mm. The defects were lined up in rows, with 10 in each row.After the algorithm was used on the acquired data from the two samples, six toeight defects were found in each row. Both samples had three rows of defects. Themeasured sizes of the defects were not exactly as the actual size but ranged a fewmillimetre too small or big compared to the real size. Overall the algorithm workswell. The resolution of the 3D images are the same as for the 2D-SAFT algorithmalready made by Swerim. As of now the 3D images may not be worth the time ittakes to process, but if a better way to visualise the data is made in the future, itwill be good to be able to see the defects in 3D.

The main goal of this thesis was to design and implement a application that would be able to generate fingerprint damage into a synthetic fingerprint. The application can create fingerprint images damaged by pressure, moisture and skin disease dyshidrosis with different intensity of damage. The application also allows annotation of the generated damage and its export. Selected damages were analyzed before the design was created. A database of fingerprints from five users was created to analyze the damage caused by pressure and moisture. The generated images and the achieved results are tested with VeriFinger and FiQiVi. For testing, 19 sets with fingerprints of different intensity and different type of damage were created. Experiments showed that the quality of the fingerprint decreased the most during the generation of moisture with the highest intensity of damage, where the quality decreased by 61.8 %. This thesis can be used for further research in the field of biometric fingerprint processing.

Much of Nature is composed of highly modular and composable nested multicomponent living systems. Synthetic biology and bioengineering exploit this modularity to understand and engineer living things. This thesis explores two projects coupled by these principles, the first utilizing a synthetic genomics approach to probe the evolutionary history, flexibility, and modularity of core metabolism, and the second adapting and engineering components of a living material to generate living architecture and embed add program new behaviors into the living biocomposite. Chapter 1 details the synthetic resurrection of a core metabolic pathway lost from the metazoan lineage millions of years ago. All metazoans are auxotrophic for 9 of the 20 amino acids, the so-called “essential” amino acids. The pressures behind the loss of the 9 are a deep evolutionary puzzle. To investigate this event and probe the limits of core metabolic flexibility, we generated a synthetic valine prototrophic mammalian cell line, restoring valine self-sufficiency to the metazoan lineage. The restoration of this pathway implies the modern mammalian metabolism is still compatible with autogenous valine production, suggests profound modularity in core metabolism, and underscores the potential usefulness of large-scale synthetic genomics approaches in a answering deep evolutionary questions. Chapter 2 describes the engineering of a hybrid fungal-bacterial biocomposite by adapting and leveraging existing behaviors and microbial constituents of a living material. Fungal biocomposites are composed of a particulate lignocellulosic feedstock bound together into a bulk biocomposite by a network of dense fungal mycelium. Using a bioprospecting approach, we designed architectural and design strategies that relied on the natural substrate flexibility and growth patterns of the fungal component of the biocomposite to form origami-inspired human scale folding structures. Similarly, we isolated, characterized, and engineered a natural microbial component of the biocomposite’s own microbiome and used its pre-adapted ability to engraft in the growing biomaterial to embed new genetic functionalities in biocomposite objects. We believe that the strategy of bioprospecting useful components and behaviors holds promise for the development of future biomaterials adapted from living systems.

Synthetic biology endeavors to forward engineer genetic circuits with novel function. A major inspiration for the field has been the enormous success in the engineering of digital electronic circuits over the past half century. This dissertation approaches synthetic biology from the perspective of the engineering design cycle, a concept ubiquitous across many engineering disciplines. First, an analysis of the state of the engineering design cycle in synthetic biology is presented, pointing out the most limiting challenges currently facing the field. Second, a principle commonly used in electronics to weigh the tradeoffs between hardware and software implementations of a function, called co-design, is applied to synthetic biology. Designs to implement a specific logical function in three distinct domains are proposed and their pros and cons weighed. Third, automatic transitioning between an abstract design, its physical implementation, and accurate models of the corresponding system are critical for success in synthetic biology. We present a framework for accomplishing this task and demonstrate how it can be used to explore a design space. A major limitation of the aforementioned approach is that adequate parameter values for the performance of genetic components do not yet exist. Thus far, it has not been possible to uniquely attribute the function of a device to the function of the individual components in a way that enables accurate prediction of the function of new devices assembled from the same components. This lack presents a major challenge to rapid progression through the design cycle. We address this challenge by first collecting high time-resolution fluorescence trajectories of individual cells expressing a fluorescent protein, as well as snapshots of the number of corresponding mRNA molecules per cell. We then leverage the information embedded in the cell-cell variability of the population to extract parameter values for a stochastic model of gene expression more complex than typically used. Such analysis opens the door for models of genetic components that can more reliably predict the function of new combinations of these basic components. / Ph. D.

<p><a></a><a>Microbial chemical factories (MCFs) have become an attractive platform for producing valuable drugs, chemicals, and biofuels due to increasing environmental concerns, energy demands, and the difficulties associated with chemically synthesizing complex molecules. However, the potential of using microbes to produce many valuable products has not been fully realized due to low productivity and yields. Production may be enhanced through the dynamic control of metabolic pathways to alleviate metabolic imbalances and toxic product build-up, but very few tools are available to broadly implement this paradigm. I aim to expand this toolbox by developing tunable sensor-regulator devices that act as feedback controllers for toxic intermediate formation by responding to cues of cell health for improved production. Elastin-like polypeptide (ELP) will act as the sensing domain of the controller to indirectly sense toxic metabolite accumulation through changes in intracellular pH. ELPs make ideal sensors since they exhibit a sharp, inverse phase transition to indicators of cellular health such as pH and ionic strength, and external stimuli such as temperature. In this research, a library of ELPs that exhibit pH sensitivity and transition under various conditions was made and purified using a new organic solvent extraction method. It is hypothesized that fusion of ELP to orthogonal transcription factor will allow for the controlled expression of target genes in response to stimuli without disrupting native processes. As proof of concept, an ELP fusion to orthogonal sigma factor was designed to drive the expression of a fluorescent reporter protein. Initial designs successfully alter gene expression by 21% in response to temperature. To improve this response, an alternative feed-forward loop architecture was modelled, which predicted an improved response of 35% and increased ultrasensitivity. Refinement of this design </a>and combinatorial construct libraries will generate various regulators with diverse outputs that may be integrated in bioproduction pathways for improved performance.</p>