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In food and pharmaceutical analysis, the classical indices peroxide value (PV), acid value (AV) and p-anisidine value (ANV) still play an important role as quality and authenticity control parameters of fats and oils. These indices are sum parameters for certain deterioration products (PV for hydroperoxides, AV for free fatty acids, ANV for aldehydes) and are obtained using volumetric or UV/VIS spectroscopic analytical approaches. 1H NMR spectroscopy provides a fast and simple alternative to these classical approaches. In the present work, novel 1H NMR methods to determine hydroperoxides, free fatty acids and aldehydes in fats and oils were developed.
Hydroperoxides:
The influence of solvent, water, free fatty acids and sample weight on the hydroperoxide group proton (OOH) signal was investigated. On the basis of the obtained results, the sample preparation procedure of the new 1H NMR method was established. A rough assignment of the hydroperoxide group signals in edible fats and oils to methyl oleate, methyl linoleate and methyl linolenate was conducted. Furthermore, to gain information on how many different hydroperoxide species originate from trioleate autoxidation, a kinetic study on trioleate monohydroperoxides was performed. The evaluation of the data strongly indicates that all of the conceivable 18 trioleate monohydroperoxides were formed during trioleate autoxidation. The analytical performance of the NMR method was compared to that of the classical PV approach by means of the so-called “relative sensitivity” according to Mandel. It was shown that both methods exhibit a similar analytical performance. A total of 444 edible oil samples were analysed using both methods. For some oil varieties considerable discrepancies were found between the results. In the case of black seed oil and olive oil two substances were identified that influence the classical PV determination and thus cause positive (black seed oil) and negative (olive oil) deviations from the theoretical PV expected from the NMR values.
Free fatty acids:
In order to find the optimal solvent mixture to measure the carboxyl group protons (COOH) of free fatty acids in fats and oils, the effect of solvent on the COOH signal was investigated for different mixtures of CDCl3 and DMSO-d6. The comparison of the NMR method with the classical AV method by means of the relative sensitivity revealed that both methods exhibit a similar analytical performance. 420 edible oil samples were analysed by both approaches. Except for pumpkin seed oil, where slight deviations were observed, there was a good compliance between the results obtained from the two methods. Furthermore, the applicability of the 1H NMR assay to further lipids with relevance in pharmacy was tested. For hard fat, castor oil, waxes and oleyl oleate modifications of the original sample preparation procedure of the NMR method were necessary to achieve comparable results for both methods.
Aldehydes:
The new 1H NMR method enables the determination of the molar amounts of n-alkanals, (E)-2-alkenals and (E,E)-2,4-alkadienals. It was illustrated that the ANV can be modelled as a linear combination of the NMR integrals of these aldehyde species. A functional relationship was derived on the basis In conclusion, the new 1H NMR methods provide an excellent alternative to of calibration experiments. The suitability of the model was shown by comparing the NMR-determined ANVs with the measured classical ANVs of 79 commercially available edible oils of different oil types.
In conclusion, the new 1H NMR methods provide an excellent alternative to the determination of the classical indices PV, AV and ANV. They have several advantages over the classical methods including the consumption of small solvent amounts, the ability to automatize measurement and to acquire several different parameters out of the same NMR spectrum. Especially concerning their selectivity, the 1H NMR methods are highly superior to the classical methods.
Frequent acquisition activities in high-technology industries are due to the intense competition, driven by short product life cycles, more complex products/services and prevalent network effects. This dissertation theoretically analyzes the circumstances leading to technology-driven acquisitions and empirically tests these within a clearly defined market scenario.
Agriculture is mankind’s primary source of food production and plays the key role for cereal supply to humanity. One of the future challenges will be to feed a constantly growing population, which is expected to reach more than nine billion by 2050. The potential to expand cropland is limited, and enhancing agricultural production efficiency is one important means to meet the future food demand. Hence, there is an increasing demand for dependable, accurate and comprehensive agricultural intelligence on crop production. The value of satellite earth observation (EO) data for agricultural monitoring is well recognized. One fundamental requirement for agricultural monitoring is routinely updated information on crop acreage and the spatial distribution of crops. With the technical advancement of satellite sensor systems, imagery with higher temporal and finer spatial resolution became available. The classification of such multi-temporal data sets is an effective and accurate means to produce crop maps, but methods must be developed that can handle such large and complex data sets. Furthermore, to properly use satellite EO for agricultural production monitoring a high temporal revisit frequency over vast geographic areas is often necessary. However, this often limits the spatial resolution that can be used. The challenge of discriminating pixels that correspond to a particular crop type, a prerequisite for crop specific agricultural monitoring, remains daunting when the signal encoded in pixels stems from several land uses (mixed pixels), e.g. over heterogeneous landscapes where individual fields are often smaller than individual pixels.
The main purposes of the presented study were (i) to assess the influence of input dimensionality and feature selection on classification accuracy and uncertainty in object-based crop classification, (ii) to evaluate if combining classifier algorithms can improve the quality of crop maps (e.g. classification accuracy), (iii) to assess the spatial resolution requirements for crop identification via image classification.
Reporting on the map quality is traditionally done with measures that stem from the confusion matrix based on the hard classification result. Yet, these measures do not consider the spatial variation of errors in maps. Measures of classification uncertainty can be used for this purpose, but they have attained only little attention in remote sensing studies. Classifier algorithms like the support vector machine (SVM) can estimate class memberships (the so called soft output) for each classified pixel or object. Based on these estimations, measures of classification uncertainty can be calculated, but it has not been analysed in detail, yet, if these are reliable in predicting the spatial distribution of errors in maps. In this study, SVM was applied for the classification of agricultural crops in irrigated landscapes in Middle Asia at the object-level. Five different categories of features were calculated from RapidEye time series data as classification input. The reliability of classification uncertainty measures like entropy, derived from the soft output of SVM, with regard to predicting the spatial distribution of error was evaluated. Further, the impact of the type and dimensionality of the input data on classification uncertainty was analysed. The results revealed that SMVs applied to the five feature categories separately performed different in classifying different types of crops. Incorporating all five categories of features by concatenating them into one stacked vector did not lead to an increase in accuracy, and partly reduced the model performance most obviously because of the Hughes phenomena. Yet, applying the random forest (RF) algorithm to select a subset of features led to an increase of classification accuracy of the SVM. The feature group with red edge-based indices was the most important for general crop classification, and the red edge NDVI had an outstanding importance for classifying crops. Two measures of uncertainty were calculated based on the soft output from SVM: maximum a-posteriori probability and alpha quadratic entropy. Irrespective of the measure used, the results indicate a decline in classification uncertainty when a dimensionality reduction was performed. The two uncertainty measures were found to be reliable indicators to predict errors in maps. Correctly classified test cases were associated with low uncertainty, whilst incorrectly test cases tended to be associated with higher uncertainty.
The issue of combining the results of different classifier algorithms in order to increase classification accuracy was addressed. First, the SVM was compared with two other non-parametric classifier algorithms: multilayer perceptron neural network (MLP) and RF. Despite their comparatively high classification performance, each of the tested classifier algorithms tended to make errors in different parts of the input space, e.g. performed different in classifying crops. Hence, a combination of the complementary outputs was envisaged. To this end, a classifier combination scheme was proposed, which is based on existing algebraic operators. It combines the outputs of different classifier algorithms at the per-case (e.g. pixel or object) basis. The per-case class membership estimations of each classifier algorithm were compared, and the reliability of each classifier algorithm with respect to classifying a specific crop class was assessed based on the confusion matrix. In doing so, less reliable classifier algorithms were excluded at the per-class basis before the final combination. Emphasis was put on evaluating the selected classification algorithms under limiting conditions by applying them to small input datasets and to reduced training sample sets, respectively. Further, the applicability to datasets from another year was demonstrated to assess temporal transferability. Although the single classifier algorithms performed well in all test sites, the classifier combination scheme provided consistently higher classification accuracies over all test sites and in different years, respectively. This makes this approach distinct from the single classifier algorithms, which performed different and showed a higher variability in class-wise accuracies. Further, the proposed classifier combination scheme performed better when using small training set sizes or when applied to small input datasets, respectively.
A framework was proposed to quantitatively define pixel size requirements for crop identification via image classification. That framework is based on simulating how agricultural landscapes, and more specifically the fields covered by one crop of interest, are seen by instruments with increasingly coarser resolving power. The concept of crop specific pixel purity, defined as the degree of homogeneity of the signal encoded in a pixel with respect to the target crop type, is used to analyse how mixed the pixels can be (as they become coarser) without undermining their capacity to describe the desired surface properties (e.g. to distinguish crop classes via supervised or unsupervised image classification). This tool can be modulated using different parameterizations to explore trade-offs between pixel size and pixel purity when addressing the question of crop identification. Inputs to the experiments were eight multi-temporal images from the RapidEye sensor. Simulated pixel sizes ranged from 13 m to 747.5 m, in increments of 6.5 m. Constraining parameters for crop identification were defined by setting thresholds for classification accuracy and uncertainty. Results over irrigated agricultural landscapes in Middle Asia demonstrate that the task of finding the optimum pixel size did not have a “one-size-fits-all” solution. The resulting values for pixel size and purity that were suitable for crop identification proved to be specific to a given landscape, and for each crop they differed across different landscapes. Over the same time series, different crops were not identifiable simultaneously in the season and these requirements further changed over the years, reflecting the different agro-ecological conditions the investigated crops were growing in. Results further indicate that map quality (e.g. classification accuracy) was not homogeneously distributed in a landscape, but that it depended on the spatial structures and the pixel size, respectively. The proposed framework is generic and can be applied to any agricultural landscape, thereby potentially serving to guide recommendations for designing dedicated EO missions that can satisfy the requirements in terms of pixel size to identify and discriminate crop types.
Regarding the operationalization of EO-based techniques for agricultural monitoring and its application to a broader range of agricultural landscapes, it can be noted that, despite the high performance of existing methods (e.g. classifier algorithms), transferability and stability of such methods remain one important research issue. This means that methods developed and tested in one place might not necessarily be portable to another place or over several years, respectively. Specifically in Middle Asia, which was selected as study region in this thesis, classifier combination makes sense due to its easy implementation and because it enhanced classification accuracy for classes with insufficient training samples. This observation makes it interesting for operational contexts and when field reference data availability is limited. Similar to the transferability of methods, the application of only one certain kind of EO data (e.g. with one specific pixel size) over different landscapes needs to be revisited and the synergistic use of multi-scale data, e.g. combining remote sensing imagery of both fine and coarse spatial resolution, should be fostered. The necessity to predict and control the effects of spatial and temporal scale on crop classification is recognized here as a major goal to achieve in EO-based agricultural monitoring.
1. Since the early nineteenth century describing (and understanding) patterns of distribution of biodiversity across the Earth has represented one of the most significant intellectual challenges to ecologists and biogeographers. Among the most striking patterns of species richness are: the latitudinal and elevational gradients, with peaks in number of species at low latitudes and somewhere at mid altitudes, although other patterns, e.g. declines with increasing elevation, are often observed. Even in highly diverse tropical regions, species richness is not evenly distributed but there are “hotspots” of biodiversity where an exceptional number of species, especially endemics, are concentrated. Unfortunately, such areas are also experiencing dramatic loss of habitat. Among vertebrate taxa, amphibians are facing the most alarming number of extinctions. Habitat destruction, pollution and emergence of infectious diseases such as chytridiomycosis, are causing worldwide population declines. Responses to these drivers can be multidirectional and subtle, i.e. they may not be captured at the species but at the genetic level. Moreover, present patterns of diversity can result from the influence of past geological, climatic and environmental changes.
In this study, I used a multidisciplinary and multilevel approach to understand how and to which extent the landscape influences amphibian diversity. Mount Kilimanjaro is an exceptional tropical region where the landscape is rapidly evolving due to land use changes; additionally, there is a broad lack of knowledge of its amphibian fauna. During two rainy seasons in 2011, I recorded anurans from the foothills to 3500 m altitude; in addition, I focused on two river frog species and collected tissue samples for genetic analysis and swabs for detection of chytridiomycosis, the deadly disease caused by Batrachochytrium dendrobatidis (Bd).
2. I analyzed how species richness and composition change with increasing elevation and anthropogenic disturbance. In order to disentangle the observed patterns of species diversity and distribution, I incorporated inferences from historical biogeography and compared the assemblage of Mt. Kilimanjaro and Mt. Meru (both recent volcanoes) with those of the older Eastern Arc Mountains. Species richness decreased with elevation and locally increased in presence of water bodies, but I did not detect effects of either anthropogenic disturbance or vegetation structure on species richness and composition. Moreover, I found a surprisingly low number of forest species. Historical events seem to underlie the current pattern of species distribution; the young age of Mt. Kilimanjaro and the complex biogeographic processes which occurred in East Africa during the last 20 million years prevented montane forest frogs from colonizing the volcano.
3. I focused on the genetic level of biodiversity and investigated how the landscape, i.e. elevation, topographic relief and land cover, influence genetic variation, population structure and gene flow of two ecologically similar and closely related river frog species, namely Amietia angolensis and Amietia wittei. I detected greater genetic differentiation among populations in the highland species (A. wittei) and higher genetic variation in the lowland species (A. angolensis), although genetic diversity was not significantly correlated with elevation. Importantly, human settlements seemed to restrict gene flow in A. angolensis, whereas steep slopes were positively correlated with gene flow in A. wittei. This results show that even ecologically similar species can respond differently to landscape processes and that the spatial configuration of topographic features combined with species-specific biological attributes can affect dispersal and gene flow in disparate ways.
4. River frogs of the genus Amietia seem to be particularly susceptible to chytridiomycosis, showing the highest pathogen load in Kenya and other African countries. In the last study, I collected swab samples from larvae of A. angolensis and A. wittei for Bd detection. Both species resulted Bd-positive. The presence of Bd on Mt. Kilimanjaro has serious implication. For instance, Bd can be transported by footwear of hikers from contaminated water and soil. Tourists visiting Mt. Kilimanjaro may translocate Bd zoospores to other areas such as the nearby Eastern Arc Mts. where endemic and vulnerable species may still be naïve to the fungus and thus suffer of population declines.
5. My study significantly contributed to the knowledge of the amphibian fauna of Mt. Kilimanjaro and of East Africa in general, and it represents a valuable tool for future conservation actions and measures. Finally, it highlights the importance of using a multidisciplinary (i.e. community ecology, historical biogeography, landscape genetics, disease ecology) and multilevel (i.e. community, species, population, gene) approach to disentangle patterns of biodiversity.
Staphylococcus aureus (SA) causes nosocomial infections including life threatening sepsis by multi-resistant strains (MRSA). It has the ability to form biofilms to protect it from the host immune system and from anti staphylococcal drugs. Biofilm and planctonic life style is regulated by a complex Quorum-Sensing (QS) system with agr as a central regulator. To study biofilm formation and QS mechanisms in SA a Boolean network was build (94 nodes, 184 edges) including two different component systems such as agr, sae and arl. Important proteins such as Sar, Rot and SigB were included as further nodes in the model. System analysis showed there are only two stable states biofilm forming versus planctonic with clearly different subnetworks turned on. Validation according to gene expression data confirmed this. Network consistency was tested first according to previous knowledge and literature. Furthermore, the predicted node activity of different in silico knock-out strains agreed well with corresponding micro array experiments and data sets. Additional validation included the expression of further nodes (Northern blots) and biofilm production compared in different knock-out strains in biofilm adherence assays. The model faithfully reproduces the behaviour of QS signalling mutants. The integrated model allows also prediction of various other network mutations and is supported by experimental data from different strains. Furthermore, the well connected hub proteins elucidate how integration of different inputs is achieved by the QS network. For in silico as well as in vitro experiments it was found that the sae-locus is also a central modulator of biofilm production. Sae knock-out strains showed stronger biofilms. Wild type phenotype was rescued by sae complementation. To elucidate the way in which sae takes influence on biofilm formation the network was used and Venn-diagrams were made, revealing nodes regulated by sae and changed in biofilms. In these Venn-diagrams nucleases and extracellular proteins were found to be promising nodes. The network revealed DNAse to be of great importance. Therefore qualitatively the DNAse amount, produced by different SA mutants was measured, it was tried to dissolve biofilms with according amounts of DNAse and the concentration of nucleic acids, proteins and polysaccharides were measured in biofilms of different SA mutants.
With its thorough validation the network model provides a powerful tool to study QS and biofilm formation in SA, including successful predictions for different knock-out mutant behaviour, QS signalling and biofilm formation. This includes implications for the behaviour of MRSA strains and mutants. Key regulatory mutation combinations (agr–, sae–, sae–/agr–, sigB+, sigB+/sae–) were directly tested in the model but also in experiments. High connectivity was a good guide to identify master regulators, whose detailed behaviour was studied both in vitro and in the model. Together, both lines of evidence support in particular a refined regulatory role for sae and agr with involvement in biofilm repression and/or SA dissemination. With examination of the composition of different mutant biofilms as well as with the examination of the reaction cascade that connects sae to the biofilm forming ability of SA and also by postulating that nucleases might play an important role in that, first steps were taken in proving and explaining regulatory links leading from sae to biofilms. Furthermore differences in biofilms of different mutant SA strains were found leading us in perspective towards a new understanding of biofilms including knowledge how to better regulate, fight and use its different properties.
The field of microRNA research has gained enormous significance during recent years. Current studies have shown that microRNAs play an important role in many biological processes via posttranscriptional gene regulation. This also applies for the TLR-mediated recognition of pathogens by immune cells. Among others, the microRNAs miR-132, miR-146a and miR-155 have been characterized by various authors. However, the specific role of microRNAs in the defense against fungal infections by Aspergillus fumigatus has not been investigated so far, although this ubiquitous mold causes severe infections in immuno-compromised patients. As dendritic cells play a pivotal part in the in vivo recognition of A. fumigatus, the present study investigates the reaction of these cells to A. fumigatus and other pathogens on the microRNA level. For this purpose, dendritic cells were incubated with different forms of A. fumigatus and other pathogens for up to twelve hours. Subsequently, the expression of miR-132, miR-146a and miR-155 was quantified by real-time PCR.
Levels of miR-132 in dendritic cells were significantly increased after stimulation with living germ tubes of A. fum, but showed no change after treatment with LPS. Relative expression level of miR-146a was moderately elevated upon stimulation with LPS, but did not respond to co-cultivation with living germ tubes. MiR-155 was highly induced by both stimuli. These results show, that dependent on the stimulus, microRNAs are differentially regulated in dendritic cells. Among the tested microRNAs, miR-155 showed the strongest and most stable expression values. Therefore, further experiments focused on this mircoRNA. It was shown, that the up-regulation of miR-155 is dependent on the germination stage of the fungus. Induction of miR-155 was low with conidia, moderate with hyphae and high with germ tubes. The extent of miR-155 induction also corresponded with the multiplicity of infection (MOI), with higher MOIs triggering a stronger miR-155 response.
These results suggest that miR-132 and miR-155 play an important role in the immunologic reaction of DCs against A. fumigatus and that a further characterization of these microRNA, especially with respect to their specific function in DCs, could contribute to the understanding of the biological mechanisms of Aspergillosis.
Atherosclerosis is accepted to be a chronic inflammatory disease of the arterial vessel wall. Several cellular subsets of the immune system are involved in its initiation and progression, such as monocytes, macrophages, T and B cells. Recent research has demonstrated that dendritic cells (DCs) contribute to atherosclerosis, too. DCs are defined by their ability to sense and phagocyte antigens, to migrate and to prime other immune cells, such as T cells. Although all DCs share these functional characteristics, they are heterogeneous with respect to phenotype and origin. Several markers have been used to describe DCs in different lymphoid and non-lymphoid organs; however, none of them has proven to be unambiguous. The expression of surface molecules is highly variable depending on the state of activation and the surrounding tissue. Furthermore, DCs in the aorta or the atherosclerotic plaque can be derived from designated precursor cells or from monocytes. In addition, DCs share both their marker expression and their functional characteristics with other myeloid cells like monocytes and macrophages. The repertoire of aortic DCs in healthy and atherosclerotic mice has just recently started to be explored, but yet there is no systemic study available, which describes the aortic DC compartment. Because it is conceivable that distinct aortic DC subsets exert dedicated functions, a detailed description of vascular DCs is required. The first part of this thesis characterizes DC subsets in healthy and atherosclerotic mice. It describes a previously unrecognized DC subset and also sheds light on the origin of vascular DCs. In recent years, microRNAs (miRNAs) have been demonstrated to regulate several cellular functions, such as apoptosis, differentiation, development or proliferation. Although several cell types have been characterized extensively with regard to the miRNAs involved in their regulation, only few studies are available that focus on the role of miRNAs in DCs. Because an improved understanding of the regulation of DC functions would allow for new therapeutic options, research on miRNAs in DCs is required. The second part of this thesis focuses on the role of the miRNA cluster miR- 17~92 in DCs by exploring its functions in healthy and atherosclerotic mice. This thesis clearly demonstrates for the first time an anti-inflammatory and atheroprotective role for the miR17-92 cluster. A model for its mechanism is suggested.
Critical illness like sepsis, shock, and intestinal bowel disease are one of the leading causes of morbidity and mortality in the US and around the world. At present, studies to define new therapeutic interventions that can protect tissues and cells against injury and attenuate inflammation are fields of intense investigation. While research over the past decade has clearly identified GLN as a vital stress substrate facilitating cellular survival following injury, the initiation steps in GLN’s cytoprotective molecular mechanism still remain elusive. Previously published work suggested that stabilization of ECM proteins and activation of ECM receptor osmosignaling may play a central role in the orchestration of many cellular pathways following stress. Thus, I hypothesized that preservation of ECM protein and EGFR levels as well as ECM receptor signaling play key roles in the molecular mechanisms underlying GLN’s protection against thermal injury in the intestine. I was able to confirm via Western blotting and by using silencing RNA against FN, Ntn-1, EGFR, and their negative controls, that GLN-mediated preservation of FN, Ntn-1, and EGFR levels is critical in GLN’s protection against hyperthermia in IEC-6 cells. By using a selective FN-Integrin interaction inhibitor GRGDSP, its negative control peptide GRGESP, and Src-kinase inhibitor PP2, I showed that FN-Integrin signaling and Src-kinase activation are essential in GLN-mediated protection in the intestine. This applied to EGFR signaling as demonstrated using the EGFR tyrosine kinase inhibitor AG1478. In addition to GRGDSP and AG1478, ERK1/2 inhibitors PD98059 and UO126 as well as the p38MAPK inhibitor SB203580 revealed that GLN is protective by activating ERK1/2 and dephosphorylating p38MAPK via FN-Integrin and EGFR signaling. However, GLN-mediated PI3-K/Akt/Hsp70 activation seems to occur independently of FN-Integrin and EGFR signaling as indicated by Western blots as well as experiments using the PI3-K inhibitor LY294002, GRGDSP, and AG1478. The results showed that GLN activates cell survival signaling pathways via integrins as well as EGFRs after hyperthermia. Moreover, I found that GLN-mediated preservation of FN expression after HS is regulated via PI3-K signaling. Whether GLN-mediated PI3-K signaling happens simultaneously to FN-Integrin and EGFR signaling or whether PI3-K signaling coordinates FN-Integrin and EGFR signaling needs to be investigated in future studies. Further, experiments with PD98059 and GRGDSP revealed that ERK1/2 assists in mediating transactivation of HSF-1 following HS. This leads to increases in Hsp70 expression via FN-Integrin signaling, which is known to attenuate apoptosis after thermal injury. Fluorescence microscopy results indicated that HS and GLN regulate cell are size changes and the morphology of F-actin via FN-Integrin signaling. Experiments using GRGDSP and GRGESP showed that GLN enhances cellular survival via FN-Integrin signaling in a manner that does not require increased intracellular GLN concentrations (as quantified using LC-MS/MS). In summary, my thesis work gives new and potentially clinically relevant mechanistic insights into GLN-mediated molecular cell survival pathways. These results warrant clinical translation to assess if clinical outcome of critically ill patients suffering from gastrointestinal diseases can be improved by GLN treatment and/or by targeting the molecular pathways found in my studies.
Scientific surveys provide sufficient evidence that anxiety disorders are one of the most common psy-chiatric disorders in the world. The lifetime prevalence rate of anxiety disorder is 28.8% (Kessler, et al., 2005). The most widely studied anxiety disorders are as follows panic disorder (PD), post-traumatic stress disorder (PTSD), obsessive-compulsive disorder (OCD), social phobia (or social anxiety disorder), specific phobias, and generalized anxiety disorder (GAD). (NIMH Article, 2009). Classical conditioning is the stable paradigm used from the last one century to understand the neurobi-ology of fear learning. Neurobiological mechanism of fear learning is well documented with the condi-tioning studies. In the therapy of anxiety disorders, exposure based therapies are known to be the most effective approaches. Flooding is a form of exposure therapy in which a participant is exposed to the fear situation and kept in that situation until their fear dissipates. The exposure therapy is based on the phenomena of extinction; this means that a conditioned response diminishes if the conditioned stimulus (CS) is repeatedly presented without an unconditioned stimulus (UCS). One problem with extinction as well as with exposure-based therapy is the problem of fear return (for e.g. renewal, spontaneous recov-ery and reinstatement) after successful extinction. Therefore, extinction does not delete the fear memory trace. It has been well documented that memory processes can be modulated or disrupted using several sci-entific paradigms such as behavioral (for e.g. exposure therapy), pharmacological (for e.g. drug manipu-lation), non-invasive stimulation (for e.g. non-invasive stimulation such as electroconvulsive shock (ECS), transcranial magnetic stimulation (TMS), transcranial direct current stimulation (tDCS), etc. However, modulation of memory processes after reactivation or via non-invasive stimulation is still not clear, which is the focus of the current study. In addition, study of genetic variant suggests that genetic differences play a vital role in the psychiatric disorder especially in fear learning. Hence, it is also one of the concerns of the current dissertation to investigate the interaction between gene and reconsolidation of memory. With respect to fear-conditioning, there are three findings in the current dissertation, which are as fol-lows: (i) In the first study we investigated that non-invasive weak electrical stimulation interferes with the consolidation process and disrupts the fear consolidation to attain stable form. This might offer an effective treatment in the pathological memories, for e.g. PTSD, PD, etc. (ii) In the second study we demonstrated whether a brief single presentation of the CS will inhibit the fear recovery. Like earlier studies we also found that reactivation followed by reconsolidation douses fear return. Attenuation of fear recovery was observed in the reminder group compared to the no-reminder group. (iii) Finally, in our third study we found a statistically significant role of brain derived neurotrophic factor (BDNF) polymorphism in reconsolidation. Results of the third study affirm the involvement of BDNF variants (Met vs. Val) in the modulation of conditioned fear memory after its reactivation. In summary, we were able to show in the current thesis modulation of associative learning and recon-solidation via transcranial direct current stimulation and genetic polymorphism.