9,564 research outputs found
Pollution-induced community tolerance in freshwater biofilms – from molecular mechanisms to loss of community functions
Exposure to herbicides poses a threat to aquatic biofilms by affecting their community structure, physiology and function. These changes render biofilms to become more tolerant, but on the downside community tolerance has ecologic costs. A concept that addresses induced community tolerance to a pollutant (PICT) was introduced by Blanck and Wängberg (1988). The basic principle of the concept is that microbial communities undergo pollution-induced succession when exposed to a pollutant over a long period of time, which changes communities structurally and functionally and enhancing tolerance to the pollutant exposure. However, the mechanisms of tolerance and the ecologic consequences were hardly studied up to date. This thesis addresses the structural and functional changes in biofilm communities and applies modern molecular methods to unravel molecular tolerance mechanisms.
Two different freshwater biofilm communities were cultivated for a period of five weeks, with one of the communities being contaminated with 4 μg L-1 diuron. Subsequently, the communities were characterized for structural and functional differences, especially focusing on their crucial role of photosynthesis. The community structure of the autotrophs was assessed using HPLC-based pigment analysis and their functional alterations were investigated using Imaging-PAM fluorometry to study photosynthesis and community oxygen profiling to determine net primary production. Then, the molecular fingerprints of the communities were measured with meta-transcriptomics (RNA-Seq) and GC-based community metabolomics approaches and analyzed with respect to changes in their molecular functions. The communities were acute exposed to diuron for one hour in a dose-response design, to reveal a potential PICT and uncover related adaptation to diuron exposure. The combination of apical and molecular methods in a dose-response design enabled the linkage of functional effects of diuron exposure and underlying molecular mechanisms based on a sensitivity analysis.
Chronic exposure to diuron impaired freshwater biofilms in their biomass accrual. The contaminated communities particularly lost autotrophic biomass, reflected by the decrease in specific chlorophyll a content. This loss was associated with a change in the molecular fingerprint of the communities, which substantiates structural and physiological changes. The decline in autotrophic biomass could be due to a primary loss of sensitive autotrophic organisms caused by the selection of better adapted species in the course of chronic exposure. Related to this hypothesis, an increase in diuron tolerance has been detected in the contaminated communities and molecular mechanisms facilitating tolerance have been found. It was shown that genes of the photosystem, reductive-pentose phosphate cycle and arginine metabolism were differentially expressed among the communities and that an increased amount of potential antioxidant degradation products was found in the contaminated communities. This led to the hypothesis that contaminated communities may have adapted to oxidative stress, making them less sensitive to diuron exposure. Moreover, the photosynthetic light harvesting complex was altered and the photoprotective xanthophyll cycle was increased in the contaminated communities. Despite these adaptation strategies, the loss of autotrophic biomass has been shown to impair primary production. This impairment persisted even under repeated short-term exposure, so that the tolerance mechanisms cannot safeguard primary production as a key function in aquatic systems.:1. The effect of chemicals on organisms and their functions .............................. 1
1.1 Welcome to the anthropocene .......................................................................... 1
1.2 From cellular stress responses to ecosystem resilience ................................... 3
1.2.1 The individual pursuit for homeostasis ....................................................... 3
1.2.2 Stability from diversity ................................................................................. 5
1.3 Community ecotoxicology - a step forward in monitoring the effects of chemical
pollution? ................................................................................................................. 6
1.4 Functional ecotoxicological assessment of microbial communities ................... 9
1.5 Molecular tools – the key to a mechanistic understanding of stressor effects
from a functional perspective in microbial communities? ...................................... 12
2. Aims and Hypothesis ......................................................................................... 14
2.1 Research question .......................................................................................... 14
2.2 Hypothesis and outline .................................................................................... 15
2.3 Experimental approach & concept .................................................................. 16
2.3.1 Aquatic freshwater biofilms as model community ..................................... 16
2.3.2 Diuron as model herbicide ........................................................................ 17
2.3.3 Experimental design ................................................................................. 18
3. Structural and physiological changes in microbial communities after chronic
exposure - PICT and altered functional capacity ................................................. 21
3.1 Introduction ..................................................................................................... 21
3.2 Methods .......................................................................................................... 23
3.2.1 Biofilm cultivation ...................................................................................... 23
3.2.2 Dry weight and autotrophic index ............................................................. 23
3.2.4 Pigment analysis of periphyton ................................................................. 23
3.2.4.1 In-vivo pigment analysis for community characterization ....................... 24
3.2.4.2 In-vivo pigment analysis based on Imaging-PAM fluorometry ............... 24
3.2.4.3 In-vivo pigment fluorescence for tolerance detection ............................. 26
3.2.4.4 Ex-vivo pigment analysis by high-pressure liquid-chromatography ....... 27
3.2.5 Community oxygen metabolism measurements ....................................... 28
3.3 Results and discussion ................................................................................... 29
3.3.1 Comparison of the structural community parameters ............................... 29
3.3.2 Photosynthetic activity and primary production of the communities after
selection phase ................................................................................................. 33
3.3.3 Acquisition of photosynthetic tolerance .................................................... 34
3.3.4 Primary production at exposure conditions ............................................... 36
3.3.5 Tolerance detection in primary production ................................................ 37
3.4 Summary and Conclusion ........................................................................... 40
4. Community gene expression analysis by meta-transcriptomics ................... 41
4.1 Introduction to meta-transcriptomics ............................................................... 41
4.2. Methods ......................................................................................................... 43
4.2.1 Sampling and RNA extraction................................................................... 43
4.2.2 RNA sequencing analysis ......................................................................... 44
4.2.3 Data assembly and processing................................................................. 45
4.2.4 Prioritization of contigs and annotation ..................................................... 47
4.2.5 Sensitivity analysis of biological processes .............................................. 48
4.3 Results and discussion ................................................................................... 48
4.3.1 Characterization of the meta-transcriptomic fingerprints .......................... 49
4.3.2 Insights into community stress response mechanisms using trend analysis
(DRomic’s) ......................................................................................................... 51
4.3.3 Response pattern in the isoform PS genes .............................................. 63
4.5 Summary and conclusion ................................................................................ 65
5. Community metabolome analysis ..................................................................... 66
5.1 Introduction to community metabolomics ........................................................ 66
5.2 Methods .......................................................................................................... 68
5.2.1 Sampling, metabolite extraction and derivatisation................................... 68
5.2.2 GC-TOF-MS analysis ............................................................................... 69
5.2.3 Data processing and statistical analysis ................................................... 69
5.3 Results and discussion ................................................................................... 70
5.3.1 Characterization of the metabolic fingerprints .......................................... 70
5.3.2 Difference in the metabolic fingerprints .................................................... 71
5.3.3 Differential metabolic responses of the communities to short-term exposure
of diuron ............................................................................................................ 73
5.4 Summary and conclusion ................................................................................ 78
6. Synthesis ............................................................................................................. 79
6.1 Approaches and challenges for linking molecular data to functional
measurements ...................................................................................................... 79
6.2 Methods .......................................................................................................... 83
6.2.1 Summary on the data ............................................................................... 83
6.2.2 Aggregation of molecular data to index values (TELI and MELI) .............. 83
6.2.3 Functional annotation of contigs and metabolites using KEGG ................ 83
6.3 Results and discussion ................................................................................... 85
6.3.1 Results of aggregation techniques ........................................................... 85
6.3.2 Sensitivity analysis of the different molecular approaches and endpoints 86
6.3.3 Mechanistic view of the molecular stress responses based on KEGG
functions ............................................................................................................ 89
6.4 Consolidation of the results – holistic interpretation and discussion ............... 93
6.4.1 Adaptation to chronic diuron exposure - from molecular changes to
community effects.............................................................................................. 93
6.4.2 Assessment of the ecological costs of Pollution-induced community
tolerance based on primary production ............................................................. 94
6.5 Outlook ............................................................................................................ 9
Deep Learning for Scene Flow Estimation on Point Clouds: A Survey and Prospective Trends
Aiming at obtaining structural information and 3D motion of dynamic scenes, scene flow estimation has been an interest of research in computer vision and computer graphics for a long time. It is also a fundamental task for various applications such as autonomous driving. Compared to previous methods that utilize image representations, many recent researches build upon the power of deep analysis and focus on point clouds representation to conduct 3D flow estimation. This paper comprehensively reviews the pioneering literature in scene flow estimation based on point clouds. Meanwhile, it delves into detail in learning paradigms and presents insightful comparisons between the state-of-the-art methods using deep learning for scene flow estimation. Furthermore, this paper investigates various higher-level scene understanding tasks, including object tracking, motion segmentation, etc. and concludes with an overview of foreseeable research trends for scene flow estimation
Deep Transfer Learning Applications in Intrusion Detection Systems: A Comprehensive Review
Globally, the external Internet is increasingly being connected to the
contemporary industrial control system. As a result, there is an immediate need
to protect the network from several threats. The key infrastructure of
industrial activity may be protected from harm by using an intrusion detection
system (IDS), a preventive measure mechanism, to recognize new kinds of
dangerous threats and hostile activities. The most recent artificial
intelligence (AI) techniques used to create IDS in many kinds of industrial
control networks are examined in this study, with a particular emphasis on
IDS-based deep transfer learning (DTL). This latter can be seen as a type of
information fusion that merge, and/or adapt knowledge from multiple domains to
enhance the performance of the target task, particularly when the labeled data
in the target domain is scarce. Publications issued after 2015 were taken into
account. These selected publications were divided into three categories:
DTL-only and IDS-only are involved in the introduction and background, and
DTL-based IDS papers are involved in the core papers of this review.
Researchers will be able to have a better grasp of the current state of DTL
approaches used in IDS in many different types of networks by reading this
review paper. Other useful information, such as the datasets used, the sort of
DTL employed, the pre-trained network, IDS techniques, the evaluation metrics
including accuracy/F-score and false alarm rate (FAR), and the improvement
gained, were also covered. The algorithms, and methods used in several studies,
or illustrate deeply and clearly the principle in any DTL-based IDS subcategory
are presented to the reader
Machine Learning Research Trends in Africa: A 30 Years Overview with Bibliometric Analysis Review
In this paper, a critical bibliometric analysis study is conducted, coupled
with an extensive literature survey on recent developments and associated
applications in machine learning research with a perspective on Africa. The
presented bibliometric analysis study consists of 2761 machine learning-related
documents, of which 98% were articles with at least 482 citations published in
903 journals during the past 30 years. Furthermore, the collated documents were
retrieved from the Science Citation Index EXPANDED, comprising research
publications from 54 African countries between 1993 and 2021. The bibliometric
study shows the visualization of the current landscape and future trends in
machine learning research and its application to facilitate future
collaborative research and knowledge exchange among authors from different
research institutions scattered across the African continent
Learning disentangled speech representations
A variety of informational factors are contained within the speech signal and a single short recording of speech reveals much more than the spoken words. The best method to extract and represent informational factors from the speech signal ultimately depends on which informational factors are desired and how they will be used. In addition, sometimes methods will capture more than one informational factor at the same time such as speaker identity, spoken content, and speaker prosody.
The goal of this dissertation is to explore different ways to deconstruct the speech signal into abstract representations that can be learned and later reused in various speech technology tasks. This task of deconstructing, also known as disentanglement, is a form of distributed representation learning. As a general approach to disentanglement, there are some guiding principles that elaborate what a learned representation should contain as well as how it should function. In particular, learned representations should contain all of the requisite information in a more compact manner, be interpretable, remove nuisance factors of irrelevant information, be useful in downstream tasks, and independent of the task at hand. The learned representations should also be able to answer counter-factual questions.
In some cases, learned speech representations can be re-assembled in different ways according to the requirements of downstream applications. For example, in a voice conversion task, the speech content is retained while the speaker identity is changed. And in a content-privacy task, some targeted content may be concealed without affecting how surrounding words sound. While there is no single-best method to disentangle all types of factors, some end-to-end approaches demonstrate a promising degree of generalization to diverse speech tasks.
This thesis explores a variety of use-cases for disentangled representations including phone recognition, speaker diarization, linguistic code-switching, voice conversion, and content-based privacy masking. Speech representations can also be utilised for automatically assessing the quality and authenticity of speech, such as automatic MOS ratings or detecting deep fakes. The meaning of the term "disentanglement" is not well defined in previous work, and it has acquired several meanings depending on the domain (e.g. image vs. speech). Sometimes the term "disentanglement" is used interchangeably with the term "factorization". This thesis proposes that disentanglement of speech is distinct, and offers a viewpoint of disentanglement that can be considered both theoretically and practically
Predictive Maintenance of Critical Equipment for Floating Liquefied Natural Gas Liquefaction Process
Predictive Maintenance of Critical Equipment for Liquefied Natural Gas Liquefaction Process
Meeting global energy demand is a massive challenge, especially with the quest of more affinity towards sustainable and cleaner energy. Natural gas is viewed as a bridge fuel to a renewable energy. LNG as a processed form of natural gas is the fastest growing and cleanest form of fossil fuel. Recently, the unprecedented increased in LNG demand, pushes its exploration and processing into offshore as Floating LNG (FLNG). The offshore topsides gas processes and liquefaction has been identified as one of the great challenges of FLNG. Maintaining topside liquefaction process asset such as gas turbine is critical to profitability and reliability, availability of the process facilities. With the setbacks of widely used reactive and preventive time-based maintenances approaches, to meet the optimal reliability and availability requirements of oil and gas operators, this thesis presents a framework driven by AI-based learning approaches for predictive maintenance. The framework is aimed at leveraging the value of condition-based maintenance to minimises the failures and downtimes of critical FLNG equipment (Aeroderivative gas turbine).
In this study, gas turbine thermodynamics were introduced, as well as some factors affecting gas turbine modelling. Some important considerations whilst modelling gas turbine system such as modelling objectives, modelling methods, as well as approaches in modelling gas turbines were investigated. These give basis and mathematical background to develop a gas turbine simulated model. The behaviour of simple cycle HDGT was simulated using thermodynamic laws and operational data based on Rowen model. Simulink model is created using experimental data based on Rowen’s model, which is aimed at exploring transient behaviour of an industrial gas turbine. The results show the capability of Simulink model in capture nonlinear dynamics of the gas turbine system, although constraint to be applied for further condition monitoring studies, due to lack of some suitable relevant correlated features required by the model.
AI-based models were found to perform well in predicting gas turbines failures. These capabilities were investigated by this thesis and validated using an experimental data obtained from gas turbine engine facility. The dynamic behaviours gas turbines changes when exposed to different varieties of fuel. A diagnostics-based AI models were developed to diagnose different gas turbine engine’s failures associated with exposure to various types of fuels. The capabilities of Principal Component Analysis (PCA) technique have been harnessed to reduce the dimensionality of the dataset and extract good features for the diagnostics model development.
Signal processing-based (time-domain, frequency domain, time-frequency domain) techniques have also been used as feature extraction tools, and significantly added more correlations to the dataset and influences the prediction results obtained. Signal processing played a vital role in extracting good features for the diagnostic models when compared PCA. The overall results obtained from both PCA, and signal processing-based models demonstrated the capabilities of neural network-based models in predicting gas turbine’s failures. Further, deep learning-based LSTM model have been developed, which extract features from the time series dataset directly, and hence does not require any feature extraction tool. The LSTM model achieved the highest performance and prediction accuracy, compared to both PCA-based and signal processing-based the models.
In summary, it is concluded from this thesis that despite some challenges related to gas turbines Simulink Model for not being integrated fully for gas turbine condition monitoring studies, yet data-driven models have proven strong potentials and excellent performances on gas turbine’s CBM diagnostics. The models developed in this thesis can be used for design and manufacturing purposes on gas turbines applied to FLNG, especially on condition monitoring and fault detection of gas turbines. The result obtained would provide valuable understanding and helpful guidance for researchers and practitioners to implement robust predictive maintenance models that will enhance the reliability and availability of FLNG critical equipment.Petroleum Technology Development Funds (PTDF) Nigeri
Cost-effective non-destructive testing of biomedical components fabricated using additive manufacturing
Biocompatible titanium-alloys can be used to fabricate patient-specific medical components using additive manufacturing (AM). These novel components have the potential to improve clinical outcomes in various medical scenarios. However, AM introduces stability and repeatability concerns, which are potential roadblocks for its widespread use in the medical sector. Micro-CT imaging for non-destructive testing (NDT) is an effective solution for post-manufacturing quality control of these components. Unfortunately, current micro-CT NDT scanners require expensive infrastructure and hardware, which translates into prohibitively expensive routine NDT. Furthermore, the limited dynamic-range of these scanners can cause severe image artifacts that may compromise the diagnostic value of the non-destructive test. Finally, the cone-beam geometry of these scanners makes them susceptible to the adverse effects of scattered radiation, which is another source of artifacts in micro-CT imaging.
In this work, we describe the design, fabrication, and implementation of a dedicated, cost-effective micro-CT scanner for NDT of AM-fabricated biomedical components. Our scanner reduces the limitations of costly image-based NDT by optimizing the scanner\u27s geometry and the image acquisition hardware (i.e., X-ray source and detector). Additionally, we describe two novel techniques to reduce image artifacts caused by photon-starvation and scatter radiation in cone-beam micro-CT imaging.
Our cost-effective scanner was designed to match the image requirements of medium-size titanium-alloy medical components. We optimized the image acquisition hardware by using an 80 kVp low-cost portable X-ray unit and developing a low-cost lens-coupled X-ray detector. Image artifacts caused by photon-starvation were reduced by implementing dual-exposure high-dynamic-range radiography. For scatter mitigation, we describe the design, manufacturing, and testing of a large-area, highly-focused, two-dimensional, anti-scatter grid.
Our results demonstrate that cost-effective NDT using low-cost equipment is feasible for medium-sized, titanium-alloy, AM-fabricated medical components. Our proposed high-dynamic-range strategy improved by 37% the penetration capabilities of an 80 kVp micro-CT imaging system for a total x-ray path length of 19.8 mm. Finally, our novel anti-scatter grid provided a 65% improvement in CT number accuracy and a 48% improvement in low-contrast visualization. Our proposed cost-effective scanner and artifact reduction strategies have the potential to improve patient care by accelerating the widespread use of patient-specific, bio-compatible, AM-manufactured, medical components
Annals [...].
Pedometrics: innovation in tropics; Legacy data: how turn it useful?; Advances in soil sensing; Pedometric guidelines to systematic soil surveys.Evento online. Coordenado por: Waldir de Carvalho Junior, Helena Saraiva Koenow Pinheiro, Ricardo Simão Diniz Dalmolin
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