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We report the results of the non-invasive photonic system AUM for remote detection and characterization of different pathogenic bacterial strains and mixtures. AUM applies the concepts of elastic light scattering, statistical mechanics, artificial intelligence, and machine learning to identify, classify and quantify various microbes in the scattering volume in real-time and, therefore, can become a potential tool in controlling and managing diseases caused by pathogenic microbes.

Summary: Neuroinflammation is a hallmark of neurological disorders and is accompanied by the production of neurotoxic agents such as nitric oxide. We used stem cell-based phenotypic screening and identified small molecules that directly protected neurons from neuroinflammation-induced degeneration. We demonstrate that inhibition of CDK5 is involved in, but not sufficient for, neuroprotection. Instead, additional inhibition of GSK3β is required to enhance the neuroprotective effects of CDK5 inhibition, which was confirmed using short hairpin RNA-mediated knockdown of CDK5 and GSK3β. Quantitative phosphoproteomics and high-content imaging demonstrate that neurite degeneration is mediated by aberrant phosphorylation of multiple microtubule-associated proteins. Finally, we show that our hit compound protects neurons in vivo in zebrafish models of motor neuron degeneration and Alzheimer's disease. Thus, we demonstrate an overlap of CDK5 and GSK3β in mediating the regulation of the neuronal cytoskeleton and that our hit compound LDC8 represents a promising starting point for neuroprotective drugs. : Neuroinflammation is a hallmark of many neurological disorders and can be neurotoxic. Sterneckert and colleagues perform a phenotypic screening campaign identifying compounds protecting motor neurons from inflammation. Compound profiling demonstrates that dual inhibition of CDK5 and GSK3β mediates neuroprotection. Quantitative phosphoproteomics demonstrates that neuroprotection is linked to alterations in microtubule dynamics mediated by phosphorylation of microtubule-associated proteins. Keywords: neurodegeneration, neuroinflammation, stem cell-based phenotypic screening, CDK5, GSK3β, induced pluripotent stem cells, ALS

Air is the most essential constituent for the sustenance of life on earth. The air we inhale has a tremendous impact on our health and well-being. Hence, it is always advisable to monitor the quality of air in our environment. To forecast the air quality index (AQI), artificial neural networks (ANNs) trained with conjugate gradient descent (CGD), such as multilayer perceptron (MLP), cascade forward neural network, Elman neural network, radial basis function (RBF) neural network, and nonlinear autoregressive model with exogenous input (NARX) along with regression models such as multiple linear regression (MLR) consisting of batch gradient descent (BGD), stochastic gradient descent (SGD), mini-BGD (MBGD) and CGD algorithms, and support vector regression (SVR), are implemented. In these models, the AQI is the dependent variable and the concentrations of NO2, CO, O3, PM2.5, SO2, and PM10 for the years 2010–2016 in Houston and Los Angeles are the independent variables. For the final forecast, several ensemble models of individual neural network predictors and individual regression predictors are presented. This proposed approach performs with the highest efficiency in terms of forecasting air quality index.

In this paper, a brief overview of real time implementation of next generation Robust, Tracking, Disturbance rejecting, Aggressive (RTDA) controller and Model Predictive Control (MPC) is provided. The control algorithm is implemented through MATLAB. The plant model used in controller design is obtained using system identification tool and integral response method. The controller model is developed in Simulink using jMPC tool, which will be executed in real time. The outputs obtained are tested for various constraint values to obtain the desirable results. The implementation of Hardware in Loop is done by interfacing it with MATLAB using Arduino as data acquisition unit. The performance of RTDA is compared with those of MPC and Proportional Integral controller.

Background: Automated perimetry in neurologically disabled patients is a challenge. We have devised a patient-friendly virtual reality perimeter, the C3 field analyzer (CFA). We aim to assess the utility of this as a visual field-testing device in neuro-ophthalmic patients for screening and monitoring. Methods: Neuro-ophthalmic patients and controls were selected to participate in the study between September and December 2018. They randomly underwent either the CFA or automated field analyzer (HFA) first followed by the other in an undilated state. The CFA results were compared with the HFA, and the correlation of the pattern of the field defect was assessed by an independent masked physician. Results: In total, 59 eyes of 33 neuro-ophthalmic patients (cases) and another 95 normal individuals (controls) were enrolled. CFA was found to have greater proportion of reliable fields (81.4%) than HFA (59.3%) (P = 0.009). There were less false negatives (P < 0.001) and more false positives in CFA (P < 0.001) among neuro-ophthalmic patients compared with controls. Among neuro-ophthalmology patients, the number of fixation losses was greater with CFA (P < 0.001), whereas false negatives were greater in HFA (P < 0.001). On assessing the pattern of the field defects, we found that there was almost 70% correlation of CFA with HFA. Moreover, in classical neurological fields such as hemianopia, the correlation was 87.5%. Conclusions: The CFA seems to correlate well with HFA in classic neurological fields such as hemianopias and may serve as an alternative in patients unable to perform a standard automated perimetry. Further developments are currently underway to incorporate threshold testing. [ABSTRACT FROM AUTHOR]

The RTD-A (robust, tracking, disturbance rejection and aggressiveness) controller is a novel control scheme that substitutes the classical proportional integral derivative (PID) controller. This novel controller’s performance depends on the four controller tuning parameters (θR, θT, θD and θA). The tuning of RTD-A controller is more transparent than classic PID controllers. The RTD-A tuning parameters values lies between ZERO and ONE. Availability of a tool to design optimal parameters for this controller and evaluating the performance on a given system is necessary for the researchers. In this paper, the new simulation tool is presented to deal with the RTD-A control scheme. There are four graphical user interface tools included in the proposed tool and working of each tool is explained in detail. To demonstrate the proposed tool, two examples, which involve a liquid level control application and an air pressure control application, are presented in this work. The performance of the RTD-A controller is compared with PID controller. RTD-A controllers are tuned using optimization algorithms and their performances are observed and analyzed in both cases under deterministic and uncertain conditions.

Wind power production has been under the main focus for the past decade in power production and tremendous amount of research work is going on renewable energy, specifically on wind power extraction. Wind power provides an eco-friendly power generation and helps to meet the national energy demand when there is a diminishing trend in terms of non-renewable resources. This paper reviews the modeling of Wind Energy Conversion Systems (WECS), control strategies of controllers and various Maximum Power Point Tracking (MPPT) technologies that are being proposed for efficient production of wind energy from the available resource.

Modern day information age is enriched with the advanced network communication expertise but unfortunately at the same time encounters infinite security issues when dealing with secret and/or private information. The storage and transmission of the secret information become highly essential and have led to a deluge of research in this field. In this paper, an optimistic effort has been taken to combine graceful graph along with integer wavelet transform (IWT) to implement random image steganography for secure communication. The implementation part begins with the conversion of cover image into wavelet coefficients through IWT and is followed by embedding secret image in the randomly selected coefficients through graph theory. Finally stegoimage is obtained by applying inverse IWT. This method provides a maximum of 44 dB peak signal to noise ratio (PSNR) for 266646 bits. Thus, the proposed method gives high imperceptibility through high PSNR value and high embedding capacity in the cover image due to adaptive embedding scheme and high robustness against blind attack through graph theoretic random selection of coefficients.

In the title chalcone, C13H11NOS, derived from the condensation of p-tolualdehyde and 1-(1,3-thiazol-2-yl)ethanone, the olefine group has a trans configuration. No classical hydrogen bonding is present in the crystal structure.

Abstract We describe a system for the automated diagnosis of diabetic retinopathy and glaucoma using fundus and optical coherence tomography (OCT) images. Automatic screening will help the doctors to quickly identify the condition of the patient in a more accurate way. The macular abnormalities caused due to diabetic retinopathy can be detected by applying morphological operations, filters and thresholds on the fundus images of the patient. Early detection of glaucoma is done by estimating the Retinal Nerve Fiber Layer (RNFL) thickness from the OCT images of the patient. The RNFL thickness estimation involves the use of active contours based deformable snake algorithm for segmentation of the anterior and posterior boundaries of the retinal nerve fiber layer. The algorithm was tested on a set of 89 fundus images of which 85 were found to have at least mild retinopathy and OCT images of 31 patients out of which 13 were found to be glaucomatous. The accuracy for optical disk detection is found to be 97.75%. The proposed system therefore is accurate, reliable and robust and can be realized.