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The genome of a eukaryotic cell is often vulnerable to both intrinsic and extrinsic threats due to its constant exposure to a myriad of heterogeneous compounds.Despite the availability of innate DNA damage response pathways, some genomic lesions trigger cells for malignant transformation. Accurate prediction of carcinogens is an ever-challenging task due to the limited information about bona fide (non)carcinogens. We developed Metabokiller, an ensemble classifier that accurately recognizes carcinogens by quantitatively assessing their electrophilicity as well as their potential to induce proliferation, oxidative stress, genomic instability, alterations in the epigenome, and anti-apoptotic response. Concomitant with the carcinogenicity prediction, Metabokiller is fully interpretable since it reveals the contribution of the aforementioned biochemical properties in imparting carcinogenicity. Metabokiller outperforms existing best-practice methods for carcinogenicity prediction. We used Metabokiller to unravel cells' endogenous metabolic threats by screening a large pool of human metabolites and predicted a subset of these metabolites that could potentially trigger malignancy in normal cells. To cross-validate Metabokiller predictions, we performed a range of functional assays using Saccharomyces cerevisiae and human cells with two Metabokiller-flagged human metabolites namely 4-Nitrocatechol and 3,4-Dihydroxyphenylacetic acid and observed high synergy between Metabokiller predictions and experimental validations.

This paper compares the detection methods of static eccentricity in Interior Permanent Magnet Synchronous Machines (IPMSM). Four methods are discussed: The first method uses shift in the voltages in d–q plane to detect fault. The second method uses shift in peak of the incremental inductance curve for fault detection. The third method uses the combined information of harmonics present both in current and voltage to detect the fault. This makes the detection robust with respect to current controller bandwidth. Finally, the fourth method used for detection includes measuring vibrations using accelerometers. It is shown that all four methods detected static eccentricity. These methods are compared on the basis of utility of fault detection under online or offline conditions and under saturated conditions. For all four methods the machine was tested at healthy, 25% and 50% static eccentricity levels. Two-dimensional (2-D) Finite element analysis was used for simulating machine under healthy and faulty cases. The experiments were performed by controlling the machine using Labview Real-time.

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