Detection and Classification of Whale Acoustic Signals

This dissertation focuses on two vital challenges in relation to whale acoustic signals: detection and classification.

In detection, we evaluated the influence of the uncertain ocean environment on the spectrogram-based detector, and derived the likelihood ratio of the proposed Short Time Fourier Transform detector. Experimental results showed that the proposed detector outperforms detectors based on the spectrogram. The proposed detector is more sensitive to environmental changes because it includes phase information.

In classification, our focus is on finding a robust and sparse representation of whale vocalizations. Because whale vocalizations can be modeled as polynomial phase signals, we can represent the whale calls by their polynomial phase coefficients. In this dissertation, we used the Weyl transform to capture chirp rate information, and used a two dimensional feature set to represent whale vocalizations globally. Experimental results showed that our Weyl feature set outperforms chirplet coefficients and MFCC (Mel Frequency Cepstral Coefficients) when applied to our collected data.

Since whale vocalizations can be represented by polynomial phase coefficients, it is plausible that the signals lie on a manifold parameterized by these coefficients. We also studied the intrinsic structure of high dimensional whale data by exploiting its geometry. Experimental results showed that nonlinear mappings such as Laplacian Eigenmap and ISOMAP outperform linear mappings such as PCA and MDS, suggesting that the whale acoustic data is nonlinear.

We also explored deep learning algorithms on whale acoustic data. We built each layer as convolutions with either a PCA filter bank (PCANet) or a DCT filter bank (DCTNet). With the DCT filter bank, each layer has different a time-frequency scale representation, and from this, one can extract different physical information. Experimental results showed that our PCANet and DCTNet achieve high classification rate on the whale vocalization data set. The word error rate of the DCTNet feature is similar to the MFSC in speech recognition tasks, suggesting that the convolutional network is able to reveal acoustic content of speech signals.

Discovery and Characterization of a Novel Fatty Acid Synthase Inhibitor with Antineoplastic Activity against Breast Cancer

During oncogenesis, cancer cells go through metabolic reprogramming to maintain their high growth rates and adapt to changes in the microenvironment and the lack of essential nutrients. Several types of cancer are dependent on de novo fatty acid synthesis to sustain their growth rates by providing precursors to construct membranes and produce vital signaling lipids. Fatty acid synthase (FASN) catalyze the terminal step of de novo fatty acid synthesis and it is highly expressed in many types of cancers where it’s up-regulation is correlated with cancer aggressiveness and low therapeutic outcome. Many FASN inhibitors were developed and showed potent anticancer activity however, only one inhibitor advanced to early stage clinical trials with some dose limiting toxicities. Using a modified fluorescence-linked enzyme chemoproteomic strategy (FLECS) screen, we identified HS-106, a thiophenopyrimiden FASN inhibitor that has anti-neoplastic activity against breast cancer in vitro and in vivo. HS-106 was able to inhibit both; purified human FASN activity and cellular fatty acid synthesis activity as evaluated by radioactive tracers incorporation into lipids experiments. In proliferation and apoptosis assays, HS-106 was able to block proliferation and induce apoptosis in several breast cancer cell lines. Several rescue experiment and global lipidome analysis were performed to probe the mechanism by which HS-106 induces apoptosis. HS-106 was found to induce several changes in lipids metabolism: (i) inhibit fatty acids synthesis. (ii) Inhibit fatty acids oxidation as indicated by the ability of inhibiting Malonyl CoA accumulation to block HS-106 induced apoptosis and the increase in the abundance of ceramides. (iii) Increase fatty acids uptake and neutral lipids formation as confirmed 14C Palmitate uptake assay and neutral lipids staining. (iv)Inhibit the formation of phospholipids by inhibiting de novo fatty acid synthesis and diverting exogenous fatty acids to neutral lipids. All of these events would lead to disruption in membranes structure and function. HS-106 was also tested in Lapatinib resistant cell lines and it was able to induce apoptosis and synergizes Lapatinib activity in these cell lines. This may be due the disruption of lipid rafts based on the observation that HS-106 reduces the expression of both HER2 and HER3. HS-106 was found to be well tolerated and bioavailable in mice with high elimination rate. HS-106 efficacy was tested in MMTV neu mouse model. Although did not significantly reduced tumor size (alone), HS-106 was able to double the median survival of the mice and showed potent antitumor activity when combined with Carboplatin. Similar results were obtained when same combinations and dosing schedule was used in C3Tag mouse model except for the inability of HS-106 affect mice survival.

From the above, HS-106 represent a novel FASN inhibitor that has anticancer activity both in vivo and in vitro. Being a chemically tractable molecule, the synthetic route to HS-106 is readily adaptable for the preparation of analogs that are similar in structure, suggesting that, the pharmacological properties of HS-106 can be improved.

Radiotherapy Assessment Using Diffusion Weighted MRI

Purpose: There are two goals of this study. The first goal of this study is to investigate the feasibility of using classic textural feature extraction in radiotherapy response assessment among a unique cohort of early stage breast cancer patients who received the single-dose preoperative radiotherapy. The second goal of this study is to investigate the clinical feasibility of using classic texture features as potential biomarkers which are supplementary to regional apparent diffusion coefficient in gynecological cancer radiotherapy response assessment.

Methods and Materials: For the breast cancer study, 15 patients with early stage breast cancer were enrolled in this retrospective study. Each patient received a single-fraction radiation treatment, and DWI and DCE-MRI scans were conducted before and after the radiotherapy. DWI scans were acquired using a spin-echo EPI sequence with diffusion weighting factors of b = 0 and b = 500 mm2/s, and the apparent diffusion coefficient (ADC) maps were calculated. DCE-MRI scans were acquired using a T1-weighted 3D SPGR sequence with a temporal resolution of about 1 minute. The contrast agent (CA) was intravenously injected with a 0.1 mmol/kg bodyweight dose at 2 ml/s. Two parameters, volume transfer constant (Ktrans) and kep were analyzed using the two-compartment Tofts pharmacokinetic model. For pharmacokinetic parametric maps and ADC maps, 33 textural features were generated from the clinical target volume (CTV) in a 3D fashion using the classic gray level co-occurrence matrix (GLCOM) and gray level run length matrix (GLRLM). Wilcoxon signed-rank test was used to determine the significance of each texture feature’s change after the radiotherapy. The significance was set to 0.05 with Bonferroni correction.

For the gynecological cancer study, 12 female patients with gynecologic cancer treated with fractionated external beam radiotherapy (EBRT) combined with high dose rate (HDR) intracavitary brachytherapy were studied. Each patient first received EBRT treatment followed by five fractions of HDR treatment. Before EBRT and before each fraction of brachytherapy, Diffusion Weighted MRI (DWI-MRI) and CT scans were acquired. DWI scans were acquired in sagittal plane utilizing a spin-echo echo-planar imaging sequence with weighting factors of b = 500 s/mm2 and b = 1000 s/mm2, one set of images of b = 0 s/mm2 were also acquired. ADC maps were calculated using linear least-square fitting method. Distributed diffusion coefficient (DDC) maps and stretching parameter α were calculated. For ADC and DDC maps, 33 classic texture features were generated utilizing the classic gray level run length matrix (GLRLM) and gray level co-occurrence matrix (GLCOM) from high-risk clinical target volume (HR-CTV). Wilcoxon signed-rank statistics test was applied to determine the significance of each feature’s numerical value change after radiotherapy. Significance level was set to 0.05 with multi-comparison correction if applicable.

Results: For the breast cancer study, regarding ADC maps calculated from DWI-MRI, 24 out of 33 CTV features changed significantly after the radiotherapy. For DCE-MRI pharmacokinetic parameters, all 33 CTV features of Ktrans and 33 features of kep changed significantly.

For the gynecological cancer study, regarding ADC maps, 28 out of 33 HR-CTV texture features showed significant changes after the EBRT treatment. 28 out of 33 HR-CTV texture features indicated significant changes after HDR treatments. The texture features that indicated significant changes after HDR treatments are the same as those after EBRT treatment. 28 out of 33 HR-CTV texture features showed significant changes after whole radiotherapy treatment process. The texture features that indicated significant changes for the whole treatment process are the same as those after HDR treatments.

Conclusion: Initial results indicate that certain classic texture features are sensitive to radiation-induced changes. Classic texture features with significant numerical changes can be used in monitoring radiotherapy effect. This might suggest that certain texture features might be used as biomarkers which are supplementary to ADC and DDC for assessment of radiotherapy response in breast cancer and gynecological cancer.

Post-cesarean Section Peritonitis at a Referral Hospital in Rwanda: Factors Associated with Maternal Morbidity and Mortality

Background: Post-cesarean section peritonitis is the leading cause of maternal morbidity and mortality at the main referral hospital in Rwanda. Published data on the management of post-cesarean section peritonitis is limited. This study examined predictors of maternal morbidity and mortality for post-cesarean peritonitis.

Methods: We performed a prospective observational cohort study at the University Teaching Hospital Kigali (CHUK) from January 1 until December 31 2015, followed by a retrospective chart review of all subjects with post-cesarean section peritonitis admitted to CHUK from January 1 until December 31, 2014. All patients admitted with the diagnosis of post-cesarean section peritonitis undergoing exploratory laparotomy at CHUK were enrolled. Patients were followed to either discharge or death. Study variables included baseline demographic/clinical characteristics, admission physical exam, intraoperative findings, and management. Data were analyzed using STATA version 14.

Results: Of the 167 patients enrolled, 81 survived without requiring hysterectomy (49%), 49 survived requiring hysterectomy (29%), and 36 died (22%). In the multivariate analysis, severe sepsis was the most significant predictor of mortality (RR=4.0 [2.2-7.7]) and uterine necrosis was the most significant predictor of hysterectomy (RR=6.3 [1.6-25.2]). There were high rates of antimicrobial resistance (AMR) among the bacterial isolates cultured from intra-abdominal pus, with 52% of bacteria resistant to third-generation cephalosporins.

Conclusions: Post-cesarean section peritonitis carries a high mortality rate in Rwanda. It is also associated with a high rate of hysterectomy. Understanding the disease process and identifying factors associated with outcomes can help guide management during admission.