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Availability and implementation: Warpgroup is an open source R package available on GitHub at github.com/nathaniel-mahieu/warpgroup. The package includes example data and XCMS compatibility wrappers for ease of use.

Historically, most chromatography/mass spectrometry experiments have been performed with reversed-phase chromatography. This well-established separation technique commonly generates Gaussian peak shapes and exhibits highly reproducible retention times. A simple retention mechanism based primarily on compound polarity also minimizes compound specific drift (Kele and Guiochon, 2000). One drawback to reversed-phase separation is a lack of retention for the highly polar compounds such as sugars and organic acids commonly of interest in metabolomic studies. As a result, many new separation chemistries have emerged under the umbrella term hydrophilic interaction liquid chromatography (HILIC), which aim to achieve separation of polar molecules (Buszewski and Noga, 2012). Unfortunately, analytes measured by HILIC separation exhibit a wide range of non-Gaussian peak shapes as well as larger, compound-specific retention time drift (Fuhrer and Zamboni, 2015). Current informatic approaches for metabolomics were primarily developed by using reversed-phase C18 chromatography, and even today most new advances are benchmarked solely on reversed-phase datasets (Tautenhahn et al., 2008). Thus, the performance of these algorithms degrades when applied to HILIC datasets.

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Detection of features and selection of integration regions is an initial and critical step of the informatic workflow (Cappadona et al., 2012). In cases where peak shapes are simple and peaks exhibit large signal-to-noise ratios, the detection and integration of peaks is reproducible. Complex metabolomic datasets, however, contain a high proportion of poorly resolved and low-abundance peaks (Nikolskiy et al., 2013). Additionally, the non-Gaussian peak shapes exhibited by a large portion of HILIC features impede the robust selection of integration bounds. These factors complicate peak detection and result in undetected features as well as integration bounds which describe different regions of a peak in each sample (Fig. 1A and B).

These failings of the current informatic workflow motivated our development of the Warpgroup algorithm. Warpgroup is an algorithm which utilizes dynamic time warping (DTW) and network graph decomposition. Herein we achieve five goals: (i) accurate grouping of features between samples even in the case of deviation from the global retention time drift, (ii) splitting of peak subregions into distinct groups, (iii) determination of consensus integration bounds within each group such that each group represents a similar chromatographic region, (iv) detection of the appropriate integration region in samples where no peak was detected, and (v) reporting of several parameters which allow filtering of noise groups.

Standard error of peak quantitation comparison. The CV for all peak groups which shared more than 6 centWave peaks from 11 replicate injections was monitored before (pink) and after (blue) warpgroup. The conventional workflow generates a large number of inconsistent peak groups for various reasons; upon warpgrouping these are corrected, resulting in a much lower CV for the replicates

Although Warpgroup was presented here in the context of LC/MS data, the input and output of the algorithm are of a general form (multiple time series and regions within those time series.) As such, the method is generalizable and can find consensus regions within any time-series data. An example of Warpgrouping on echocardiogram data (Goldberger et al., 2000; Penzel et al., 2000) can be found in Supplementary Figure S9.

Study design: Pancreaticoduodenectomy patients (high-risk patients excluded) were enrolled in an IRB-approved, prospective, randomized controlled trial (NCT02517268) comparing a 5-day Whipple accelerated recovery pathway (WARP) with our traditional 7-day pathway (control). Whipple accelerated recovery pathway interventions included early discharge planning, shortened ICU stay, modified postoperative dietary and drain management algorithm, rigorous physical therapy with in-hospital gym visit, standardized rectal suppository administration, and close telehealth follow-up post discharge. The trial was powered to detect an increase in postoperative day 5 discharge from 10% to 30% (80% power, = 0.05, 2-sided Fisher's exact test, target accrual: 142 patients).

Results: Seventy-six patients (37 WARP, 39 control) were randomized from June 2015 to September 2017. A planned interim analysis was conducted at 50% trial accrual resulting in mandatory early stoppage, as the predefined efficacy end point was met. Demographic variables between groups were similar. The WARP significantly increased the number of patients discharged to home by postoperative day 5 compared with controls (75.7% vs 12.8%; p < 0.001) without increasing readmission rates (8.1% vs 10.3%; p = 1.0). Overall complication rates did not differ between groups (29.7% vs 43.6%; p = 0.24), but the WARP significantly reduced the time from operation to adjuvant therapy initiation (51 days vs 66 days; p = 0.005) and hospital cost ($26,563 vs $31,845; p = 0.011).

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Another factor is Rash riding. Do not increase the ride speed drastically, increase the speed very gradually and slowly. This uses more battery charge. You can literally see this factor on the screen when you ride. The screen turns RED when you ride rash (i.e. either suddenly increasing the speed, or when going uphill with a higher speed etc). You can always try to keep the screen from going RED and keep it GREEN as always as possible. Sometimes the screen goes from GREEN to BROWNish color when you gradually increase the speed towards the top speed, do not worry as this is the right way to increase and if you get BROWNish instead of RED, you are good. When you get RED on the screen, slowly decrease the speed until RED vanishes and try to maintain constant speed wherever possible.

The true range is calculated based on the riding data of users of the 450 and the 450X. It is entirely possible to ride in Warp mode and get < 30 Wh/km efficiency if you take it easy on the throttle, avoid harsh braking and acceleration, and maintain good tyre pressure.

Deep convolutional neural networks have performed remarkably well on many Computer Vision tasks. However, these networks are heavily reliant on big data to avoid overfitting. Overfitting refers to the phenomenon when a network learns a function with very high variance such as to perfectly model the training data. Unfortunately, many application domains do not have access to big data, such as medical image analysis. This survey focuses on Data Augmentation, a data-space solution to the problem of limited data. Data Augmentation encompasses a suite of techniques that enhance the size and quality of training datasets such that better Deep Learning models can be built using them. The image augmentation algorithms discussed in this survey include geometric transformations, color space augmentations, kernel filters, mixing images, random erasing, feature space augmentation, adversarial training, generative adversarial networks, neural style transfer, and meta-learning. The application of augmentation methods based on GANs are heavily covered in this survey. In addition to augmentation techniques, this paper will briefly discuss other characteristics of Data Augmentation such as test-time augmentation, resolution impact, final dataset size, and curriculum learning. This survey will present existing methods for Data Augmentation, promising developments, and meta-level decisions for implementing Data Augmentation. Readers will understand how Data Augmentation can improve the performance of their models and expand limited datasets to take advantage of the capabilities of big data.

Deep Learning models have made incredible progress in discriminative tasks. This has been fueled by the advancement of deep network architectures, powerful computation, and access to big data. Deep neural networks have been successfully applied to Computer Vision tasks such as image classification, object detection, and image segmentation thanks to the development of convolutional neural networks (CNNs). These neural networks utilize parameterized, sparsely connected kernels which preserve the spatial characteristics of images. Convolutional layers sequentially downsample the spatial resolution of images while expanding the depth of their feature maps. This series of convolutional transformations can create much lower-dimensional and more useful representations of images than what could possibly be hand-crafted. The success of CNNs has spiked interest and optimism in applying Deep Learning to Computer Vision tasks.

There are many branches of study that hope to improve current benchmarks by applying deep convolutional networks to Computer Vision tasks. Improving the generalization ability of these models is one of the most difficult challenges. Generalizability refers to the performance difference of a model when evaluated on previously seen data (training data) versus data it has never seen before (testing data). Models with poor generalizability have overfitted the training data. One way to discover overfitting is to plot the training and validation accuracy at each epoch during training. The graph below depicts what overfitting might look like when visualizing these accuracies over training epochs (Fig. 1).

The plot on the left shows an inflection point where the validation error starts to increase as the training rate continues to decrease. The increased training has caused the model to overfit to the training data and perform poorly on the testing set relative to the training set. In contrast, the plot on the right shows a model with the desired relationship between training and testing error 006ab0faaa