Molecules assembling and reacting under the constraint of weak and strong surface interactions

On surface chemistry and physics of nanoscale molecular self-assemblies manufactured in a “bottom-up” approach is the base on which this thesis is established. The formation of the mentioned self-assemblies is dependent on the interactions between their organic molecular building blocks and building blocks-substrate interaction. These organic molecules are synthesized for specific purposes by modifying molecular topology, structure and functional groups. The substrates employed are predominantly single crystals composed of coinage metals or single crystals with deposited adlayers. Chemical modification of these compounds via different bonding motifs (i.e. Hydrogen bonding, coordination, Van der Waals interaction, etc) in their self-assemblies is studied after their in-situ deposition using Scanning Tunneling Microscopy (STM), X-ray photoelectron spectroscopy (XPS) and Density Functional Theory (DFT). In chapter [[1]] direct comparison between in-solution and on-surface behavior of the same compounds is presented. Synthesis and surface assembly of the higher pyrazinacenes and their oxidized analogues using PbO2 in solution and annealing on Cu(111) substrate is studied. Upon thermal deposition of these compounds on single Cu(111) crystal, the molecules arrange in a chiral conformation. Subsequent annealing at 150 °C causes the dehydrogenation of the molecules and consequently the formation of linear arrays. Further annealing to 300 °C breaks the linear chains and the molecules appear to adopt a “double-lobe” or “two dark satellite” morphology which we attribute to further oxidation (cyclodehydrogenation). In chapter [[2]] the unprecedented ‘out-of-plane’ oriented, hydrogen-bonded assemblies of a planar module, the perylene derivative DPDI on a specifically-chosen weakly interacting substrate is studied. A single atomic layer of semi-metallic Bi in p(10x10) phase is selected as the substrate as it is known to be electronically decoupled from the underlying metallic Cu(100) single crystal thus can be used to study mainly intermolecular interactions. Extended, hexagonal networks containing “windmill-shaped” nodes with unique bi-chirality features, together with a compact assembly of zigzag structures are the two spontaneously formed supramolecular structures which are of great chemical importance since direct deposition of DPDI on Cu(100) does not lead to any sort of assembly. In chapter [[3]] the self-assembly of functionalized tetraphenylporphyrins in different architectures is presented. Trifluoromethyl and methoxy- functionalized tetraphenylporphyrins were synthesized and used to reveal polymorphism, driven by F…F interactions and C-F…H-C hydrogen bonds. The on-surface behavior of the symmetric and asymmetric functionalized compounds (trans and mono, respectively) is compared with tetrakis(3,4,5-trimethoxyphenyl)- and tetrakis(3,5-trifluoromethylphenyl)-porphyrins

The study of Triple dimensions (Subjective, psychological and spiritual) of Well-being in Prediction of fear of normal delivery in Pregnant women

Background and aims: Increasing the number of elective caesarean section in Iran caused by several factors including fear of normal delivery that is psychological factor. This study was conducted to investigate the relationship between triple dimensions (Subjective, psychological and spiritual) of well-being in prediction of fear of normal delivery in pregnant women. Methods: This research method was descriptive and correlational. The research sample included178 pregnant women that referred to the clinic of two hospitals in Tehran with two tendencies of normal delivery and caesarean for childbirth, and were selected by convenient sampling. Participants completed the spiritual well-being scale, subjective well-being scale, psychological well-being index and researcher made questionnaire of fear of childbirth. Data were analyzed using Pearson correlation and stepwise regression analysis. Results: The results showed negative significant correlation between spiritual well-being, religious and existential well-being, life satisfaction, positive affect and psychological well-being with fear of normal delivery in pregnant women and positive significant correlation between negative affect and fear of childbirth (P≤0.01). Results also showed that existential well-being has negative significant role in predicting the fear of normal delivery. The final research model explains significantly 9% of variance of fear of normal delivery. Conclusion: From correlation between fear of normal delivery with the subjective, psychological and spiritual well-being in pregnant women, it can be concluded the increasing of satisfaction with life, positive affect and enriching the existential and religious beliefs in pregnant women can reduce their tendency for caesarean section and increase their readiness for selecting normal delivery

Optimization of SPME coating characteristics for metabolomics and targeted analysis with LC/MS

Metabolomics data provides complementary information to proteomics, genomics, and transcriptomics, in addition to enabling the tracking of the dynamic reactions in living systems. Metabolomics is widely used in various areas of study such as human diseases, drug discovery, plant analysis, and human nutrition. In metabolomics, the workflow for quantitative and comprehensive metabolic mapping of cellular metabolites can be a very challenging undertaking. Sampling and sample preparation play an important role in untargeted analysis as they influence the final composition of the analyzed extract, which can consequently influence the obtained metabolome. The choice of sample preparation method for metabolomics is based on factors such as non-selectivity, high reproducibility, integration of metabolism quenching, and extraction of a wide range of metabolite polarities. It should provide a good representation of the sample under study and obtain high sample clean-up so as to reduce matrix effects, especially when liquid chromatography coupled mass spectrometry (LC-MS) instrumentation is used for analysis. Solid phase microextraction (SPME) has already been demonstrated as a suitable technique for metabolic profiling of various biological matrices. This noninvasive and solventless extraction technique eliminates the need for metabolism quenching steps, as the coating selectively extracts metabolites, eliminating the co-extraction of interfering biomacromolecules such as proteins or enzymes. One of the main objectives of the currently presented research was the development of a new extraction phase that is compatible with complex food matrices and that provides high extraction recovery for a wide range of metabolites. For this purpose, initial research involved the preparation of a silica-based ionic liquid coating as a stationary phase for a 96-blade SPME system for the extraction of polar metabolites from grape juice without any further sample pretreatment. The lab-made polymer demonstrated high physical and chemical stability, and results indicated that the properties of the coating could be changed by changing the functional groups during the synthesis procedure. Chapter 3 presents different SPME coating chemistries that were developed and applied to provide simultaneous extraction of a wide range of both hydrophobic and hydrophilic cellular metabolites produced by a model organism, Escherichia coli (E.coli). This research reports the first successful application of the developed 96-blade SPME method coupled to LC-MS for bacteria and plant metabolomics. Three different LC-MS methods were also evaluated for the analysis of extracted metabolites. The Orbitrap system provided a powerful platform for metabolomics due its high resolution and mass accuracy. Among different coating chemistries applied for analysis, polystyrene–divinylbenzene–weak anion exchange (PS-DVB-WAX), hydrophilic–lipophilic balance particles (HLB), and their mixtures demonstrated the highest extraction recovery and a wide range of metabolite coverage. A mixture of PS-DVB-WAX and HLB particles with 50:50 weight ratio (PS-DVB-WAX: HLB 50:50 [w/w]) was applied successfully for extraction of a wide range of metabolites, while the pentafluorophenyl Kinetex column coupled to an Orbitrap mass spectrometer method provided the widest metabolomics coverage for the investigated system. The method separated and detected over 200 cellular metabolites with widely varying hydrophobicities, ranging from -7 < log P < 17, including amino acids, peptides, nucleotides, carbohydrates, polycarboxylic acids, vitamins, phosphorylated compounds, and lipids such as hydrophobic phospholipids, as well as glycerolipids, and fatty acids at the stationary phase of the E.coli life cycle. Moreover, the 96-blade SPME system provided a high throughput platform, which surpassed sample throughput requirements for a typical metabolomics study whereby ~100 samples/day are processed. Chapters 4, 5, and 6 present the obtained results of applications of the optimized method towards evaluations of environmental stresses on biological systems. Essential oils, as natural plant products with a complex mixture of constituents, are comprised of multiple antimicrobial properties related to oxygenated terpenoids, particularly phenolic terpenes, phenylpropanoids, and alcohols. This thesis presents an investigation into the mechanisms of bactericidal action of cinnamaldehyde and clove oil against E.coli during bacterial growth, applying 96-blade SPME in direct immersion mode coupled to ultra performance liquid chromatography-mass spectrometry (UPLC-MS). Statistical analysis demonstrated alteration in the metabolic pathway during different time points of the E.coli growth curve, via the up-regulation of saturated fatty acids and amino acids, as well as the down-regulation of unsaturated fatty acids, glycolysis, and TCA cycle metabolites for E.coli treated by cinnamaldehyde, below and above the minimum inhibitory concentration. The presented 96-blade SPME-LC/MS method was developed using multivariate design, and applied to evaluate the synergistic effect of major components of clove oil as an antibacterial agent to E.coli. SPME provided clear separation between different sample treatments, and valuable information regarding the mechanisms of antibacterial action of the two naturally occurring compounds, suggesting different metabolic pathways for samples treated with the active agents. As opposed to the utilization of traditional univariate optimization methods, the current study employs the application of multivariate experimental designs for optimization of extraction-influencing parameters. Based on the obtained results, eugenol, as the major component of clove oil, produced the characteristic features of an antimicrobial agent. There is no synergistic effect between the components of clove bud oil in the actual weight percent of its constituents. Evaluation of discriminating metabolites in treated samples indicated eugenol as a lead compound for the development of an active agent through the control of glycolysis in anticancer cells, as this compound demonstrated glycolysis inhibition of E.coli as a model organism. The optimized SPME-LC-MS method was applied for high-throughput analysis of complex apple matrices without a sample pretreatment step. Untargeted metabolic profiling coupled with multivariate statistical analysis indicated metabolic alterations happening prior to scald development. The obtained results could be applied towards an improvement in the nutritional stability of foodstuffs as well as allow for shelf-life expansion, in addition to increasing their potential market value. The developed 96-blade SPME-LC-MS method is promising for global metabolomics applications, in particular in terms of extraction of unstable and short-lived metabolites in comparison to traditional techniques. SPME has also demonstrated high reproducibility and sample clean-up, which is a top requirement in metabolomics investigations

Joint Path planning and Power Allocation of a Cellular-Connected UAV using Apprenticeship Learning via Deep Inverse Reinforcement Learning

This paper investigates an interference-aware joint path planning and power allocation mechanism for a cellular-connected unmanned aerial vehicle (UAV) in a sparse suburban environment. The UAV's goal is to fly from an initial point and reach a destination point by moving along the cells to guarantee the required quality of service (QoS). In particular, the UAV aims to maximize its uplink throughput and minimize the level of interference to the ground user equipment (UEs) connected to the neighbor cellular BSs, considering the shortest path and flight resource limitation. Expert knowledge is used to experience the scenario and define the desired behavior for the sake of the agent (i.e., UAV) training. To solve the problem, an apprenticeship learning method is utilized via inverse reinforcement learning (IRL) based on both Q-learning and deep reinforcement learning (DRL). The performance of this method is compared to learning from a demonstration technique called behavioral cloning (BC) using a supervised learning approach. Simulation and numerical results show that the proposed approach can achieve expert-level performance. We also demonstrate that, unlike the BC technique, the performance of our proposed approach does not degrade in unseen situations

Single-modal and Multi-modal False Arrhythmia Alarm Reduction using Attention-based Convolutional and Recurrent Neural Networks

This study proposes a deep learning model that effectively suppresses the false alarms in the intensive care units (ICUs) without ignoring the true alarms using single- and multimodal biosignals. Most of the current work in the literature are either rule-based methods, requiring prior knowledge of arrhythmia analysis to build rules, or classical machine learning approaches, depending on hand-engineered features. In this work, we apply convolutional neural networks to automatically extract time-invariant features, an attention mechanism to put more emphasis on the important regions of the input segmented signal(s) that are more likely to contribute to an alarm, and long short-term memory units to capture the temporal information presented in the signal segments. We trained our method efficiently using a two-step training algorithm (i.e., pre-training and fine-tuning the proposed network) on the dataset provided by the PhysioNet computing in cardiology challenge 2015. The evaluation results demonstrate that the proposed method obtains better results compared to other existing algorithms for the false alarm reduction task in ICUs. The proposed method achieves a sensitivity of 93.88% and a specificity of 92.05% for the alarm classification, considering three different signals. In addition, our experiments for 5 separate alarm types leads significant results, where we just consider a single-lead ECG (e.g., a sensitivity of 90.71%, a specificity of 88.30%, an AUC of 89.51 for alarm type of Ventricular Tachycardia arrhythmia