Status of disinfection byproducts research in India.

India faces high incidents of waterborne disease outbreaks owing to their limited access to safe drinking water. In many ways, the effort to improve the quality of drinking water is performed, and it has been keenly monitored. Among those, the disinfection of drinking water is considered a necessary and important step as it controls the microbial population. Chlorination is the most practiced (greater than 80%) disinfection process in India, and it is known to generate various disinfection byproducts (DBPs). Although the toxicity and trend of DBPs are regularly monitored and investigated in most countries, still in India, the research is at the toddler level. This review summarizes i) the status of drinking water disinfection in India, ii) types of disinfection processes in centralized water treatment plants, iii) concentrations and occurrence patterns of DBPs in a different region of India, iv) a literature survey on the toxicity of DBPs, and v) removal methodologies or alternative technologies to mitigate the DBPs formation. Overall, this review may act as a roadmap to understand the trend of disinfection practices in India and their impacts on securing the goal of safe drinking water for all.

A deep learning approach to detect blood vessels in basal cell carcinoma.

PURPOSE: Blood vessels called telangiectasia are visible in skin lesions with the aid of dermoscopy. Telangiectasia are a pivotal identifying feature of basal cell carcinoma. These vessels appear thready, serpiginous, and may also appear arborizing, that is, wide vessels branch into successively thinner vessels. Due to these intricacies, their detection is not an easy task, neither with manual annotation nor with computerized techniques. In this study, we automate the segmentation of telangiectasia in dermoscopic images with a deep learning U-Net approach. METHODS: We apply a combination of image processing techniques and a deep learning-based U-Net approach to detect telangiectasia in digital basal cell carcinoma skin cancer images. We compare loss functions and optimize the performance by using a combination loss function to manage class imbalance of skin versus vessel pixels. RESULTS: We establish a baseline method for pixel-based telangiectasia detection in skin cancer lesion images. An analysis and comparison for human observer variability in annotation is also presented. CONCLUSION: Our approach yields Jaccard score within the variation of human observers as it addresses a new aspect of the rapidly evolving field of deep learning: automatic identification of cancer-specific structures. Further application of DL techniques to detect dermoscopic structures and handle noisy labels is warranted.

Random lasing from optical fibers with phase separated glass cores.

A novel random laser, integrating a passive optical fiber with a phase separated aluminosilicate core-silica cladding as the feedback medium, is proposed and presented. The core exhibits greatly enhanced Rayleigh scattering, therefore requiring a significantly reduced length of scattering fiber (4 m) for lasing. With a Yb-doped fiber as the gain medium, the fiber laser operates at 1050 nm with low threshold power and possesses an output that can be amplified through conventional means. Furthermore, the laser was found to have a high degree of spatial coherence, spectral broadening with increasing input power, and temporal spectral variation. The facile setup and results herein pave the way for further study and applications based on low threshold random fiber lasers.

Direct Error-Driven Learning for Deep Neural Networks With Applications to Big Data.

In this brief, heterogeneity and noise in big data are shown to increase the generalization error for a traditional learning regime utilized for deep neural networks (deep NNs). To reduce this error, while overcoming the issue of vanishing gradients, a direct error-driven learning (EDL) scheme is proposed. First, to reduce the impact of heterogeneity and data noise, the concept of a neighborhood is introduced. Using this neighborhood, an approximation of generalization error is obtained and an overall error, comprised of learning and the approximate generalization errors, is defined. A novel NN weight-tuning law is obtained through a layer-wise performance measure enabling the direct use of overall error for learning. Additional constraints are introduced into the layer-wise performance measure to guide and improve the learning process in the presence of noisy dimensions. The proposed direct EDL scheme effectively addresses the issue of heterogeneity and noise while mitigating vanishing gradients and noisy dimensions. A comprehensive simulation study is presented where the proposed approach is shown to mitigate the vanishing gradient problem while improving generalization by 6%.

Pharmacogenomics and chemical library screens reveal a novel SCFSKP2 inhibitor that overcomes Bortezomib resistance in multiple myeloma.

While clinical benefit of the proteasome inhibitor (PI) bortezomib (BTZ) for multiple myeloma (MM) patients remains unchallenged, dose-limiting toxicities and drug resistance limit the long-term utility. The E3 ubiquitin ligase Skp1-Cullin-1-Skp2 (SCFSkp2) promotes proteasomal degradation of the cell cycle inhibitor p27 to enhance tumor growth. Increased SKP2 expression and reduced p27 levels are frequent in human cancers and are associated with therapeutic resistance. SCFSkp2 activity is increased by the Cullin-1-binding protein Commd1 and the Skp2-binding protein Cks1B. Here we observed higher CUL1, COMMD1 and SKP2 mRNA levels in CD138+ cells isolated from BTZ-resistant MM patients. Higher CUL1, COMMD1, SKP2 and CKS1B mRNA levels in patient CD138+ cells correlated with decreased progression-free and overall survival. Genetic knockdown of CUL1, COMMD1 or SKP2 disrupted the SCFSkp2 complex, stabilized p27 and increased the number of annexin-V-positive cells after BTZ treatment. Chemical library screens identified a novel compound, designated DT204, that reduced Skp2 binding to Cullin-1 and Commd1, and synergistically enhanced BTZ-induced apoptosis. DT204 co-treatment with BTZ overcame drug resistance and reduced the in vivo growth of myeloma tumors in murine models with survival benefit. Taken together, the results provide proof of concept for rationally designed drug combinations that incorporate SCFSkp2 inhibitors to treat BTZ resistant disease.

Pharmacologic screens reveal metformin that suppresses GRP78-dependent autophagy to enhance the anti-myeloma effect of bortezomib.

Although the therapeutic benefit of proteasome inhibition in multiple myeloma remains unchallenged, drug resistance inevitably emerges through mechanisms that remain elusive. Bortezomib provokes unwanted protein accumulation and the endoplasmic reticulum stress to activate the unfolded protein response (UPR) and autophagy as compensatory mechanisms that restore protein homeostasis. High-throughput screens to detect pharmacologics that modulated autophagy to enhance the anti-myeloma effect of bortezomib revealed metformin, a widely used antidiabetic agent with proven efficacy and limited adverse effects. Metformin co-treatment with bortezomib suppressed induction of the critical UPR effector glucose-regulated protein 78 (GRP78) to impair autophagosome formation and enhance apoptosis. Gene expression profiling of newly diagnosed myeloma patient tumors further correlated the hyperexpression of GRP78-encoding HSPA5 with reduced clinical response to bortezomib. The effect of bortezomib was enhanced with metformin co-treatment using myeloma patient tumor cells and the chemoresistant, stem cell-like side population that may contribute to disease recurrence. The relevance of the findings was confirmed in vivo as shown by metformin co-treatment with bortezomib that delayed the growth of myeloma xenotransplants. Taken together, our results suggest that metformin suppresses GRP78, a key driver of bortezomib-induced autophagy, and support the pharmacologic repositioning of metformin to enhance the anti-myeloma benefit of bortezomib.

MiR-29b replacement inhibits proteasomes and disrupts aggresome+autophagosome formation to enhance the antimyeloma benefit of bortezomib.

Evading apoptosis is a cancer hallmark that remains a serious obstacle in current treatment approaches. Although proteasome inhibitors (PIs) have transformed management of multiple myeloma (MM), drug resistance emerges through induction of the aggresome+autophagy pathway as a compensatory protein clearance mechanism. Genome-wide profiling identified microRNAs (miRs) differentially expressed in bortezomib-resistant myeloma cells compared with drug-naive cells. The effect of individual miRs on proteasomal degradation of short-lived fluorescent reporter proteins was then determined in live cells. MiR-29b was significantly reduced in bortezomib-resistant cells as well as in cells resistant to second-generation PIs carfilzomib and ixazomib. Luciferase reporter assays demonstrated that miR-29b targeted PSME4 that encodes the proteasome activator PA200. Synthetically engineered miR-29b replacements impaired the growth of myeloma cells, patient tumor cells and xenotransplants. MiR-29b replacements also decreased PA200 association with proteasomes, reduced the proteasome's peptidase activity and inhibited ornithine decarboxylase turnover, a proteasome substrate degraded through ubiquitin-independent mechanisms. Immunofluorescence studies revealed that miR-29b replacements enhanced the bortezomib-induced accumulation of ubiquitinated proteins but did not reveal aggresome or autophagosome formation. Taken together, our study identifies miR-29b replacements as the first-in-class miR-based PIs that also disrupt the autophagy pathway and highlight their potential to synergistically enhance the antimyeloma effect of bortezomib.

MicroRNA theragnostics for the clinical management of multiple myeloma.

Theragnostics represent cutting-edge, multi-disciplinary strategies that combine diagnostics with therapeutics in order to generate personalized therapies that improve patient outcome. In oncology, the approach is aimed at more accurate diagnosis of cancer, optimization of patient selection to identify those most likely to benefit from a specific therapy and to generate effective therapeutics that enhance patient survival. MicroRNAs (miRNAs) are master regulators of the human genome that orchestrate myriad cellular pathways to control growth during physiologic and pathologic conditions. Compelling evidence shows that miRNA deregulation promotes events linked to tumor initiation, metastasis and drug resistance as seen in multiple myeloma (MM), an invariably fatal hematologic malignancy. miRNAs are readily detected in body fluids, for example, serum, plasma, urine, as well as circulating tumor cells to demonstrate their potential as readily accessible, non-invasive diagnostic and prognostic biomarkers and potential therapeutics. Specific miRNAs are aberrantly expressed early in myelomagenesis and may more readily detect high-risk disease than current methods. Although only recently discovered miRNAs have rapidly advanced from preclinical studies to evaluation in human clinical trials. The development of miRNA theragnostics should provide widely applicable tools for the targeted delivery of personalized medicines to improve the outcome of patients with MM.

Zero-sum two-player game theoretic formulation of affine nonlinear discrete-time systems using neural networks.

In this paper, the nearly optimal solution for discrete-time (DT) affine nonlinear control systems in the presence of partially unknown internal system dynamics and disturbances is considered. The approach is based on successive approximate solution of the Hamilton-Jacobi-Isaacs (HJI) equation, which appears in optimal control. Successive approximation approach for updating control and disturbance inputs for DT nonlinear affine systems are proposed. Moreover, sufficient conditions for the convergence of the approximate HJI solution to the saddle point are derived, and an iterative approach to approximate the HJI equation using a neural network (NN) is presented. Then, the requirement of full knowledge of the internal dynamics of the nonlinear DT system is relaxed by using a second NN online approximator. The result is a closed-loop optimal NN controller via offline learning. A numerical example is provided illustrating the effectiveness of the approach.

A model-based fault-detection and prediction scheme for nonlinear multivariable discrete-time systems with asymptotic stability guarantees.

In this paper, a novel, unified model-based fault-detection and prediction (FDP) scheme is developed for nonlinear multiple-input-multiple-output (MIMO) discrete-time systems. The proposed scheme addresses both state and output faults by considering separate time profiles. The faults, which could be incipient or abrupt, are modeled using input and output signals of the system. The fault-detection (FD) scheme comprises online approximator in discrete time (OLAD) with a robust adaptive term. An output residual is generated by comparing the FD estimator output with that of the measured system output. A fault is detected when this output residual exceeds a predefined threshold. Upon detecting the fault, the robust adaptive terms and the OLADs are initiated wherein the OLAD approximates the unknown fault dynamics online while the robust adaptive terms help in ensuring asymptotic stability of the FD design. Using the OLAD outputs, a fault diagnosis scheme is introduced. A stable parameter update law is developed not only to tune the OLAD parameters but also to estimate the time to failure (TTF), which is considered as a first step for prognostics. The asymptotic stability of the FDP scheme enhances the detection and TTF accuracy. The effectiveness of the proposed approach is demonstrated using a fourth-order MIMO satellite system.

Optimal control of unknown affine nonlinear discrete-time systems using offline-trained neural networks with proof of convergence.

The optimal control of linear systems accompanied by quadratic cost functions can be achieved by solving the well-known Riccati equation. However, the optimal control of nonlinear discrete-time systems is a much more challenging task that often requires solving the nonlinear Hamilton-Jacobi-Bellman (HJB) equation. In the recent literature, discrete-time approximate dynamic programming (ADP) techniques have been widely used to determine the optimal or near optimal control policies for affine nonlinear discrete-time systems. However, an inherent assumption of ADP requires the value of the controlled system one step ahead and at least partial knowledge of the system dynamics to be known. In this work, the need of the partial knowledge of the nonlinear system dynamics is relaxed in the development of a novel approach to ADP using a two part process: online system identification and offline optimal control training. First, in the system identification process, a neural network (NN) is tuned online using novel tuning laws to learn the complete plant dynamics so that a local asymptotic stability of the identification error can be shown. Then, using only the learned NN system model, offline ADP is attempted resulting in a novel optimal control law. The proposed scheme does not require explicit knowledge of the system dynamics as only the learned NN model is needed. The proof of convergence is demonstrated. Simulation results verify theoretical conjecture.