The degradtion of humic substance using continuous photocatalysis systems

Photocatalytic oxidation is an emerging technology in water and wastewater treatment. Photocatalysis often leads to complete degradation of organic pollutants without the need for chemicals. This study investigated the degradation of humic substances in water using photocatalysis systems coupled with physio-chemical processes such as adsorption and/or flocculation. Dissolved Organic Carbon (DOC) removal of PAC-TiO2 was improved by a factor of two to three times compared with TiO2 alone. Solid Phase Micro Extraction (SPME)/Gas Chromatograph (GC) flame ionisation detector (FID) was used to investigate intermediates of photocatalytic oxidation in a batch reactor with TiO2 alone and with powder activated carbon (PAC) with TiO2. GC peaks showed that PAC-TiO2 adsorbed some by-products which were photo-resistant and prevented the reverse reaction that occurred when TiO2 was used alone. The two other types of photocatalytic reactors used were the continuous photocatalytic reactor and recirculated photocatalytic reactor. The results show that the recirculated reactor had the highest efficiency in removing organic matter in a short photo-oxidation (detention) time of less than 10min. The use of PAC-TiO2 in recirculated continuous reactor resulted in 80% removal of organic matter even when it was operated for a short detention time and allowed the use of a smaller dose of TiO2

A Deeper Look at the Hessian Eigenspectrum of Deep Neural Networks and its Applications to Regularization

Loss landscape analysis is extremely useful for a deeper understanding of the generalization ability of deep neural network models. In this work, we propose a layerwise loss landscape analysis where the loss surface at every layer is studied independently and also on how each correlates to the overall loss surface. We study the layerwise loss landscape by studying the eigenspectra of the Hessian at each layer. In particular, our results show that the layerwise Hessian geometry is largely similar to the entire Hessian. We also report an interesting phenomenon where the Hessian eigenspectrum of middle layers of the deep neural network are observed to most similar to the overall Hessian eigenspectrum. We also show that the maximum eigenvalue and the trace of the Hessian (both full network and layerwise) reduce as training of the network progresses. We leverage on these observations to propose a new regularizer based on the trace of the layerwise Hessian. Penalizing the trace of the Hessian at every layer indirectly forces Stochastic Gradient Descent to converge to flatter minima, which are shown to have better generalization performance. In particular, we show that such a layerwise regularizer can be leveraged to penalize the middlemost layers alone, which yields promising results. Our empirical studies on well-known deep nets across datasets support the claims of this workComment: Accepted at AAAI 202

EPHA2 mutations with oncogenic characteristics in squamous cell lung cancer and malignant pleural mesothelioma.

Squamous cell carcinoma (SCC) and malignant pleural mesothelioma (MPM) are thoracic malignancies with very poor prognosis and limited treatment options. It is an established fact that most of the solid tumors have overexpression of EPHA2 receptor tyrosine kinase. EPHA2 is known to exhibit opposing roles towards cancer progression. It functions in inhibiting cancer survival and migration via a ligand and tyrosine kinase dependent signaling (Y772). Whereas it is known to promote tumor progression and cell migration through a ligand-independent signaling (S897). We analyzed the expression profile and mutational status of the ephrin receptor A2 (EPHA2) in SCC and MPM cell lines and primary patient specimens. The EPHA2 receptor was found to be either overexpressed, mutated or amplified in SCC and MPM. In particular, the EPHA2 mutants A859D and T647M were interesting to explore, A859D Y772 dead mutant exhibited lower levels of phosphorylation at Y772 compared to T647M mutant. Molecular Dynamics simulations studies suggested that differential changes in conformation might form the structural basis for differences in the level of EPHA2 activation. Consequently, A859D mutant cells exhibited increased proliferation as well as cell migration compared to controls and T647M mutant. Kinomics analysis demonstrated that the STAT3 and PDGF pathways were upregulated whereas signaling through CBL was suppressed. Considered together, the present work has uncovered the oncogenic characteristics of EPHA2 mutations in SSC and MPM reinstating the dynamics of different roles of EPHA2 in cancer. This study also suggests that a combination of doxazosin and other EPHA2 inhibitors directed to inhibit the pertinent signaling components may be a novel therapeutic strategy for MPM and Non-small cell lung cancer patients who have either EPHA2 or CBL alterations

Development of a cytology-based multivariate analytical risk index for oral cancer

Objectives The diagnosis and management of oral cavity cancers are often complicated by the uncertainty of which patients will undergo malignant transformation, obligating close surveillance over time. However, serial biopsies are undesirable, highly invasive, and subject to inherent issues with poor inter-pathologist agreement and unpredictability as a surrogate for malignant transformation and clinical outcomes. The goal of this study was to develop and evaluate a Multivariate Analytical Risk Index for Oral Cancer (MARIO) with potential to provide non-invasive, sensitive, and quantitative risk assessments for monitoring lesion progression. Materials and methods A series of predictive models were developed and validated using previously recorded single-cell data from oral cytology samples resulting in a “continuous risk score”. Model development consisted of: (1) training base classification models for each diagnostic class pair, (2) pairwise coupling to obtain diagnostic class probabilities, and (3) a weighted aggregation resulting in a continuous MARIO. Results and conclusions Diagnostic accuracy based on optimized cut-points for the test dataset ranged from 76.0% for Benign, to 82.4% for Dysplastic, 89.6% for Malignant, and 97.6% for Normal controls for an overall MARIO accuracy of 72.8%. Furthermore, a strong positive relationship with diagnostic severity was demonstrated (Pearson’s coefficient = 0.805 for test dataset) as well as the ability of the MARIO to respond to subtle changes in cell composition. The development of a continuous MARIO for PMOL is presented, resulting in a sensitive, accurate, and non-invasive method with potential for enabling monitoring disease progression, recurrence, and the need for therapeutic intervention of these lesions

Repression of tumor necrosis factor-related apoptosis-inducing ligand (TRAIL) but not its receptors during oral cancer progression

BACKGROUND: TRAIL plays an important role in host immunosurveillance against tumor progression, as it induces apoptosis of tumor cells but not normal cells, and thus has great therapeutic potential for cancer treatment. TRAIL binds to two cell-death-inducing (DR4 and DR5) and two decoy (DcR1, and DcR2) receptors. Here, we compare the expression levels of TRAIL and its receptors in normal oral mucosa (NOM), oral premalignancies (OPM), and primary and metastatic oral squamous cell carcinomas (OSCC) in order to characterize the changes in their expression patterns during OSCC initiation and progression. METHODS: DNA microarray, immunoblotting and immunohistochemical analyses were used to examine the expression levels of TRAIL and its receptors in oral epithelial cell lines and in archival tissues of NOM, OPM, primary and metastatic OSCC. Apoptotic rates of tumor cells and tumor-infiltrating lymphocytes (TIL) in OSCC specimens were determined by cleaved caspase 3 immunohistochemistry. RESULTS: Normal oral epithelia constitutively expressed TRAIL, but expression was progressively lost in OPM and OSCC. Reduction in DcR2 expression levels was noted frequently in OPM and OSCC compared to respective patient-matched uninvolved oral mucosa. OSCC frequently expressed DR4, DR5 and DcR1 but less frequently DcR2. Expression levels of DR4, DR5 and DcR1 receptors were not significantly altered in OPM, primary OSCC and metastatic OSCC compared to patient-matched normal oral mucosa. Expression of proapoptotic TRAIL-receptors DR4 and DR5 in OSCC seemed to depend, at least in part, on whether or not these receptors were expressed in their parental oral epithelia. High DR5 expression in primary OSCC correlated significantly with larger tumor size. There was no significant association between TRAIL-R expression and OSSC histology grade, nodal status or apoptosis rates of tumor cells and TIL. CONCLUSION: Loss of TRAIL expression is an early event during oral carcinogenesis and may be involved in dysregulation of apoptosis and contribute to the molecular carcinogenesis of OSCC. Differential expressions of TRAIL receptors in OSCC do not appear to play a crucial role in their apoptotic rate or metastatic progression

Feature generation for long-tail classification

The visual world naturally exhibits an imbalance in the number of object or scene instances resulting in a long-tailed distribution. This imbalance poses significant challenges for classification models based on deep learning. Oversampling instances of the tail classes attempts to solve this imbalance. However, the limited visual diversity results in a network with poor representation ability. A simple counter to this is decoupling the representation and classifier networks and using oversampling only to train the classifier. In this paper, instead of repeatedly re-sampling the same image (and thereby features), we explore a direction that attempts to generate meaningful features by estimating the tail category's distribution. Inspired by ideas from recent work on few-shot learning [53], we create calibrated distributions to sample additional features that are subsequently used to train the classifier. Through several experiments on the CIFAR-100-LT (long-tail) dataset with varying imbalance factors and on mini-ImageNet-LT (long-tail), we show the efficacy of our approach and establish a new state-of-the-art. We also present a qualitative analysis of generated features using t-SNE visualizations and analyze the nearest neighbors used to calibrate the tail class distributions. Our code is available at https://github.com/rahulvigneswaran/TailCalibX. © 2021 ACM

Feature generation for long-tail classification

The visual world naturally exhibits an imbalance in the number of object or scene instances resulting in a long-tailed distribution. This imbalance poses significant challenges for classification models based on deep learning. Oversampling instances of the tail classes attempts to solve this imbalance. However, the limited visual diversity results in a network with poor representation ability. A simple counter to this is decoupling the representation and classifier networks and using oversampling only to train the classifier. In this paper, instead of repeatedly re-sampling the same image (and thereby features), we explore a direction that attempts to generate meaningful features by estimating the tail category's distribution. Inspired by ideas from recent work on few-shot learning [53], we create calibrated distributions to sample additional features that are subsequently used to train the classifier. Through several experiments on the CIFAR-100-LT (long-tail) dataset with varying imbalance factors and on mini-ImageNet-LT (long-tail), we show the efficacy of our approach and establish a new state-of-the-art. We also present a qualitative analysis of generated features using t-SNE visualizations and analyze the nearest neighbors used to calibrate the tail class distributions. Our code is available at https://github.com/rahulvigneswaran/TailCalibX. © 2021 ACM

Microfluidic Microchannel (Size And Shape) for Single Cell Analysis by Numerical Optimization: Lateral Trapping Method

The primary objective of this work is to show simulation outputs from the developed model of cell flow within a microfluidic device. This work is essential because it requires computational models to offer compact sized biomedical equipment that involves microfluidics technology. Microfluidics has become a common technology for life science applications in latest years. The purpose is to learn the effect of various microchannel size and shape with lateral traps for single cell analysis and to arrive at an optimum design based on a simulation study using COMSOL Multiphysics software. Thus in order to develop software model of various microchannels which execute fluid flow in the microelectronic device. This research provides numerical alternatives from finite element analysissimulation using the software COMSOL-Multiphysics to characterize the shape and size of the microchannel initializing the fluid flow. Optimized design analysis and operating conditions for efficient single cell trap is reported

MET and PI3K/mTOR as a Potential Combinatorial Therapeutic Target in Malignant Pleural Mesothelioma

Malignant pleural mesothelioma (MPM) is an aggressive disease with a poor prognosis. Studies have shown that both MET and its key downstream intracellular signaling partners, PI3K and mTOR, are overexpressed in MPM. Here we determined the combinatorial therapeutic efficacy of a new generation small molecule inhibitor of MET, ARQ 197, and dual PI3K/mTOR inhibitors NVP-BEZ235 and GDC-0980 in mesothelioma cell and mouse xenograft models. Cell viability results show that mesothelioma cell lines were sensitive to ARQ 197, NVP-BEZ235 and GDC-0980 inhibitors. The combined use of ARQ 197 with either NVP-BEZ235 or GDC-0980, was synergistic (CI<1). Significant delay in wound healing was observed with ARQ 197 (p<0.001) with no added advantage of combining it with either NVP-BEZ235 or GDC-0980. ARQ 197 alone mainly induced apoptosis (20±2.36%) that was preceded by suppression of MAPK activity, while all the three suppressed cell cycle progression. Both GDC-0980 and NVP-BEZ235 strongly inhibited activities of PI3K and mTOR as evidenced from the phosphorylation status of AKT and S6 kinase. The above observation was further substantiated by the finding that a majority of the MPM archival samples tested revealed highly active AKT. While the single use of ARQ 197 and GDC-0980 inhibited significantly the growth of MPM xenografts (p<0.05, p<0.001 respectively) in mice, the combination of the above two drugs was highly synergistic (p<0.001). Our results suggest that the combined use of ARQ 197/NVP-BEZ235 and ARQ 197/GDC-0980 is far more effective than the use of the drugs singly in suppressing MPM tumor growth and motility and therefore merit further translational studies