Holstmyers9034

To showcase the advantages of such a modularized workflow, we describe a simple yet reliable method for estimating reusability of pretrained modules as well as task transferability in a transfer learning setting. At practically no computation overhead, it precisely described the task space structure of 15 binary classification tasks from CIFAR-10.Recent studies have shown that in-depth studies on epi-transcriptomic patterns of N6-methyladenosine (m6A) may be helpful to understand its complex functions and co-regulatory mechanisms. Since most biclustering algorithms are developed in scenarios of gene expression analysis, which does not share the same characteristics with m6A methylation profile, we propose a weighted Plaid bi-clustering model (FBCwPlaid) based on Lagrange multiplier method to discover the potential functional patterns. The model seeks for one bi-cluster each time. Thus, the goal of each time turns into a binary classification problem. It initializes model parameters by k-means clustering, and then updates the parameters of the Plaid model. To address the issue that site expression level determines methylation level confidence, it uses RNA expression levels of each site as weights to make lower expressed sites less confident. FBCwPlaid also allows overlapping bi-clusters, indicating some sites may participate in multiple biological functions. FBCwPlaid was then applied on MeRIP-seq data of 69,446 methylation sites under 32 experimental conditions. Finally, 3 patterns were discovered, and further pathway analysis and enzyme specificity test showed that sites involved in each pattern are highly relevant to m6A methyltransferases. Further detailed analyses even showed that some patterns are condition relevant.A central challenge in protein modeling research and protein structure prediction in particular is known as decoy selection. The problem refers to selecting biologically-active/native tertiary structures among a multitude of physically-realistic structures generated by template-free protein structure prediction methods. TGFbeta inhibitor Research on decoy selection is active. Clustering-based methods are popular, but they fail to identify good/near-native decoys on datasets where near-native decoys are severely under-sampled by a protein structure prediction method. Reasonable progress is reported by methods that additionally take into account the internal energy of a structure and employ it to identify basins in the energy landscape organizing the multitude of decoys. These methods, however, incur significant time costs for extracting basins from the landscape. In this paper, we propose a novel decoy selection method based on non-negative matrix factorization. We demonstrate that our method outperforms energy landscape-based methods. In particular, the proposed method addresses both the time cost issue and the challenge of identifying good decoys in a sparse dataset, successfully recognizing near-native decoys for both easy and hard protein targets.This paper describes a semi-powered ankle prosthesis and corresponding unified controller that provides biomimetic behavior for level and sloped walking without requiring identification of ground slope or modulation of control parameters. The controller is based on the observation that healthy individuals maintain an invariant external quasi-stiffness (spring like behavior between the shank and ground) when walking on level and sloped terrain. Emulating an invariant external quasi-stiffness requires an ankle that can vary the set-point (i.e., equilibrium angle) of the ankle stiffness. A semi-powered ankle prosthesis that incorporates a novel constant-volume power-asymmetric actuator was developed to provide this behavior, and the unified controller was implemented on it. The device and unified controller were assessed on three subjects with transtibial amputations while walking on inclines, level ground, and declines. Experimental results suggest that the prosthesis and accompanying controller can provide a consistent external quasi-stiffness similar to healthy subjects across all tested ground slopes.Paralysis of the upper extremity severely restricts independence and quality of life after spinal cord injury. Regaining control of hand and arm movements is the highest treatment priority for people with paralysis, 6-fold higher than restoring walking ability. Nevertheless, current approaches to improve upper extremity function typically do not restore independence. Spinal cord stimulation is an emerging neuromodulation strategy to restore motor function. Recent studies using surgically implanted electrodes demonstrate impressive improvements in voluntary control of standing and stepping. Here we show that transcutaneous electrical stimulation of the spinal cord leads to rapid and sustained recovery of hand and arm function, even after complete paralysis. Notably, the magnitude of these improvements matched or exceeded previously reported results from surgically implanted stimulation. Additionally, muscle spasticity was reduced and autonomic functions including heart rate, thermoregulation, and bladder function improved. Perhaps most striking is that all six participants maintained their gains for at least three to six months beyond stimulation, indicating functional recovery mediated by long-term neuroplasticity. Several participants resumed their hobbies that require fine motor control, such as playing the guitar and oil painting, for the first time in up to 12 years since their injuries. Our findings demonstrate that non-invasive transcutaneous electrical stimulation of the spinal networks restores movement and function of the hands and arm for people with both complete paralysis and long-term spinal cord injury.We propose a novel framework to produce cartoon videos by fetching the color information from two input keyframes while following the animated motion guided by a user sketch. The key idea of the proposed approach is to estimate the dense cross-domain correspondence between the sketch and cartoon video frames, and employ a blending module with occlusion estimation to synthesize the middle frame guided by the sketch. After that, the input frames and the synthetic frame equipped with established correspondence are fed into an arbitrary-time frame interpolation pipeline to generate and refine additional inbetween frames. Finally, a module to preserve temporal consistency is employed. Compared to common frame interpolation methods, our approach can address frames with relatively large motion and also has the flexibility to enable users to control the generated video sequences by editing the sketch guidance. By explicitly considering the correspondence between frames and the sketch, we can achieve higher quality results than other image synthesis methods.