Abstract: A multi wheeled vehicle includes: a body frame that includes a head pipe; a conversion assembly that includes an electric machine operatively connected to a transmission assembly; and a housing configured to accommodate at least one electronic component and at least one electrochemical cell, the housing being attached to the electric machine, the conversion assembly being mounted to a front lower portion of the body frame by means of one or more engine mounting brackets, and the conversion assembly being disposed below a fuel tank with at least a portion of the conversion assembly being overlapping a side cover of the multi wheeled vehicle in a vehicle side view.

Abstract: Provided herein are methods for simultaneously identifying genomic copy number variations (CNVs) and sequence variations in an enriched genomic sample and compositions, systems, and kits for performing such methods. In some aspects, the methods include: (a) obtaining a plurality of sequence reads from an enriched genomic sample that includes a plurality of genomic backbone regions and a plurality of genomic mutation regions of interest in a genomic locus of a subject; (b) obtaining a plurality of sequence reads from corresponding genomic backbone regions and genomic mutation regions of at least one reference genomic sample; (c) assembling the plurality sequence reads from the enriched genomic sample and the at least one reference genomic sample; and (d) determining, based on computational analysis of the assembly, whether the genomic locus has a copy number variation (CNV) and/or a sequence variation.

Abstract: Provided herein are methods for simultaneously identifying genomic copy number variations (CNVs) and sequence variations in an enriched genomic sample and compositions, systems, and kits for performing such methods. In some aspects, the methods include: (a) obtaining a plurality of sequence reads from an enriched genomic sample that includes a plurality of genomic backbone regions and a plurality of genomic mutation regions of interest in a genomic locus of a subject; (b) obtaining a plurality of sequence reads from corresponding genomic backbone regions and genomic mutation regions of at least one reference genomic sample; (c) assembling the plurality sequence reads from the enriched genomic sample and the at least one reference genomic sample; and (d) determining, based on computational analysis of the assembly, whether the genomic locus has a copy number variation (CNV) and/or a sequence variation.

Abstract: Provided herein, among other things, is a computer-implemented method for assigning a sequence read to a genomic location, the method including: a) accessing a file containing a sequence read, wherein the sequence read is obtained from a nucleic acid sample that has been enriched by hybridization to a plurality of capture sequences; and b) assigning the sequence read to a genomic location by: i) identifying a capture sequence as being a match with the sequence read if the sequence read contains one or more subsequences of the capture sequence; ii) calculating, using a computer, a score indicating the degree of sequence similarity between each of the matched capture sequences and the sequence read; and iii) assigning the sequence read to the genomic location if the calculated score for a matched capture sequence is above a threshold.

Abstract: Provided herein is a method for identifying a sequence variant in an enriched sample. In certain embodiments, this method may comprise: (a) obtaining: (i) a plurality of sequence reads from a sample that has been enriched for a genomic region and (ii) a reference sequence for the genomic region; (b) assembling the sequence reads to obtain a plurality of discrete sequence assemblies that correspond to potential variants; (c) determining which of the potential variants are true and which are artifacts by examining the sequence reads that make up each of the discrete sequence assemblies; (d) optionally determining whether each of the true potential variants contains a mutation that is known to be associated with the reference sequence; and (e) outputting a report indicating whether the sample comprises a sequence variant.

Abstract: A network association mining algorithm and associated methods are presented which accepts data from biological and other experiments and automatically produces a network and attempts to explain the behavior of the biological or other system underlying the data using evolutionary techniques. The model and associated methods aim to identify the inter-relationships consistent with data and other prior knowledge supplied to the system. The network is represented in terms of coupled dynamical system. These dynamical systems are represented by differential or difference equations to educe these dynamical systems and coupling between them, an evolutionary algorithm is used. The output of the linkage finder could assist scientists to better understand the systems underlying and to guess at surrogate data.

Abstract: A network inference and validation engine is presented which combines data of different types into a network associations' inference and performs validation of existing networks based on constraints from several data sets or previously known linkages. The engine would assist scientists to integrate information from various sources into a network of association, validate previously known associations against the supplied constraint data sets and prune or modify existing connections based on subsequent experiments.