Multi-Parametric Analysis and Modeling of Relationships between Mitochondrial Morphology and Apoptosis

Mitochondria exist as a network of interconnected organelles undergoing constant fission and fusion. Current approaches to study mitochondrial morphology are limited by low data sampling coupled with manual identification and classification of complex morphological phenotypes. Here we propose an integrated mechanistic and data-driven modeling approach to analyze heterogeneous, quantified datasets and infer relations between mitochondrial morphology and apoptotic events. We initially performed high-content, multi-parametric measurements of mitochondrial morphological, apoptotic, and energetic states by high-resolution imaging of human breast carcinoma MCF-7 cells. Subsequently, decision tree-based analysis was used to automatically classify networked, fragmented, and swollen mitochondrial subpopulations, at the single-cell level and within cell populations. Our results revealed subtle but significant differences in morphology class distributions in response to various apoptotic stimuli. Furthermore, key mitochondrial functional parameters including mitochondrial membrane potential and Bax activation, were measured under matched conditions. Data-driven fuzzy logic modeling was used to explore the non-linear relationships between mitochondrial morphology and apoptotic signaling, combining morphological and functional data as a single model. Modeling results are in accordance with previous studies, where Bax regulates mitochondrial fragmentation, and mitochondrial morphology influences mitochondrial membrane potential. In summary, we established and validated a platform for mitochondrial morphological and functional analysis that can be readily extended with additional datasets. We further discuss the benefits of a flexible systematic approach for elucidating specific and general relationships between mitochondrial morphology and apoptosis

Accountable, Explainable Artificial Intelligence Incorporation Framework for a Real-Time Affective State Assessment Module

The rapid growth of artificial intelligence (AI) and machine learning (ML) solutions has seen it adopted across various industries. However, the concern of ‘black-box’ approaches has led to an increase in the demand for high accuracy, transparency, accountability, and explainability in AI/ML approaches. This work contributes through an accountable, explainable AI (AXAI) framework for delineating and assessing AI systems. This framework has been incorporated into the development of a real-time, multimodal affective state assessment system

Elucidation of the Tetraterpene Hydrocarbon Biosynthetic Pathway in the Green Microalga Botryococcus braunii Race L

The colony-forming green microalga Botryococcus braunii is a potential source of biofuel feedstocks as it produces large amount of liquid hydrocarbon oils that can be converted into combustion engine fuels. There are three different races of B. braunii based on the hydrocarbons it synthesizes. Race A produces fatty acid derived alkadienes and alkatrienes, race B produces the triterpenoid The colony-forming green microalga Botryococcus braunii is a potential source of biofuel feedstocks as it produces large amount of liquid hydrocarbon oils that can be converted into combustion engine fuels. There are three different races of B. braunii based on the hydrocarbons it synthesizes. Race A produces fatty acid derived alkadienes and alkatrienes, race B produces the triterpenoid hydrocarbons tetramethylsqualene and botryococcenes, and race L, the focus of this study, produces the C₄₀ tetraterpenoid hydrocarbon lycopadiene via a previously uncharacterized biosynthetic pathway. Structural similarities suggest this pathway follows a biosynthetic mechanism analogous to that of C₃₀ squalene. Confirming this hypothesis, the studies presented here identified C₂₀ geranylgeranyl diphosphate (GGPP) as a precursor for lycopaoctaene biosynthesis, the first committed intermediate in the production of lycopadiene. Two squalene synthase (SS)-like cDNAs were identified in race L with one encoding a true SS, and the other an enzyme with lycopaoctaene synthase (LOS) activity. Interestingly, LOS utilizes alternative C₁₅ and C₂₀ prenyl diphosphate substrates to produce combinatorial hybrid hydrocarbons, but almost exclusively utilizes GGPP in vivo. This discovery highlights how SS enzyme diversification resulted in the production of specialized tetraterpenoid oils in race L of B. braunii. To understand LOS substrate and product specificity, rational mutagenesis experiments were conducted based on sequence alignments with several SS proteins as well as a structural comparison with the human SS (HSS) crystal structure. Characterization of the LOS mutants in vitro identified Ser276 and Ala288 in the LOS active site as key amino acids responsible for controlling substrate binding, and thus the promiscuity of this enzyme. Mutating these residues to those found in HSS largely converted LOS from lycopaoctaene production to C₃₀ squalene production. Furthermore, these studies were confirmed in vivo by expressing LOS in E. coli cells metabolically engineered to produce high FPP and GGPP levels. These studies also offer insights into tetraterpenoid hydrocarbon metabolism in B. braunii and provide a foundation for engineering LOS for robust production of specific hydrocarbons of a desired chain length

Detection and removal of eyeblink artifacts from EEG using wavelet analysis and independent component analysis

Electrical signals generated by brain activity that are measured by the electroencephalogram can be distorted by electrical activity originating from eyeblinks and eye movements. This thesis proposes a new technique to identify and remove eyeblink artifacts from EEG data. An algorithm using a combination of wavelet analysis and independent component analysis (ICA) is implemented to detect the temporal location of the eyeblink artifact and eliminate it without compromising the integrity of the primary EEG data. The discrete wavelet transform is performed on 10 second epochs of data to detect the occurrence of ocular artifact. ICA is used to separate out the independent components within the data and the temporal locations of the eyeblink are used to remove the artifact and reconstruct the EEG data without that source of distortion. The results obtained indicate that the technique implemented may be robust enough to effectively process EEG data and is capable of removing eyeblink artifacts successfully when they are prominent and the data does not contain a great deal of movement artifact. The results show an 88.68% detection rate, a false positive rate of 4.03%, and an 87.23% removal rate for all eyeblinks that were accurately detected. The statistics obtained compared favorably with work done by others in this field of investigation

Detection and handling of overlapping speech for speaker diarization

For the last several years, speaker diarization has been attracting substantial research attention as one of the spoken language technologies applied for the improvement, or enrichment, of recording transcriptions. Recordings of meetings, compared to other domains, exhibit an increased complexity due to the spontaneity of speech, reverberation effects, and also due to the presence of overlapping speech. Overlapping speech refers to situations when two or more speakers are speaking simultaneously. In meeting data, a substantial portion of errors of the conventional speaker diarization systems can be ascribed to speaker overlaps, since usually only one speaker label is assigned per segment. Furthermore, simultaneous speech included in training data can eventually lead to corrupt single-speaker models and thus to a worse segmentation. This thesis concerns the detection of overlapping speech segments and its further application for the improvement of speaker diarization performance. We propose the use of three spatial cross-correlationbased parameters for overlap detection on distant microphone channel data. Spatial features from different microphone pairs are fused by means of principal component analysis, linear discriminant analysis, or by a multi-layer perceptron. In addition, we also investigate the possibility of employing longterm prosodic information. The most suitable subset from a set of candidate prosodic features is determined in two steps. Firstly, a ranking according to mRMR criterion is obtained, and then, a standard hill-climbing wrapper approach is applied in order to determine the optimal number of features. The novel spatial as well as prosodic parameters are used in combination with spectral-based features suggested previously in the literature. In experiments conducted on AMI meeting data, we show that the newly proposed features do contribute to the detection of overlapping speech, especially on data originating from a single recording site. In speaker diarization, for segments including detected speaker overlap, a second speaker label is picked, and such segments are also discarded from the model training. The proposed overlap labeling technique is integrated in Viterbi decoding, a part of the diarization algorithm. During the system development it was discovered that it is favorable to do an independent optimization of overlap exclusion and labeling with respect to the overlap detection system. We report improvements over the baseline diarization system on both single- and multi-site AMI data. Preliminary experiments with NIST RT data show DER improvement on the RT ¿09 meeting recordings as well. The addition of beamforming and TDOA feature stream into the baseline diarization system, which was aimed at improving the clustering process, results in a bit higher effectiveness of the overlap labeling algorithm. A more detailed analysis on the overlap exclusion behavior reveals big improvement contrasts between individual meeting recordings as well as between various settings of the overlap detection operation point. However, a high performance variability across different recordings is also typical of the baseline diarization system, without any overlap handling