GCformer: An Efficient Framework for Accurate and Scalable Long-Term Multivariate Time Series Forecasting

Transformer-based models have emerged as promising tools for time series forecasting. However, these model cannot make accurate prediction for long input time series. On the one hand, they failed to capture global dependencies within time series data. On the other hand, the long input sequence usually leads to large model size and high time complexity. To address these limitations, we present GCformer, which combines a structured global convolutional branch for processing long input sequences with a local Transformer-based branch for capturing short, recent signals. A cohesive framework for a global convolution kernel has been introduced, utilizing three distinct parameterization methods. The selected structured convolutional kernel in the global branch has been specifically crafted with sublinear complexity, thereby allowing for the efficient and effective processing of lengthy and noisy input signals. Empirical studies on six benchmark datasets demonstrate that GCformer outperforms state-of-the-art methods, reducing MSE error in multivariate time series benchmarks by 4.38% and model parameters by 61.92%. In particular, the global convolutional branch can serve as a plug-in block to enhance the performance of other models, with an average improvement of 31.93\%, including various recently published Transformer-based models. Our code is publicly available at https://github.com/zyj-111/GCformer

FiLM: Frequency improved Legendre Memory Model for Long-term Time Series Forecasting

Recent studies have shown that deep learning models such as RNNs and Transformers have brought significant performance gains for long-term forecasting of time series because they effectively utilize historical information. We found, however, that there is still great room for improvement in how to preserve historical information in neural networks while avoiding overfitting to noise presented in the history. Addressing this allows better utilization of the capabilities of deep learning models. To this end, we design a \textbf{F}requency \textbf{i}mproved \textbf{L}egendre \textbf{M}emory model, or {\bf FiLM}: it applies Legendre Polynomials projections to approximate historical information, uses Fourier projection to remove noise, and adds a low-rank approximation to speed up computation. Our empirical studies show that the proposed FiLM significantly improves the accuracy of state-of-the-art models in multivariate and univariate long-term forecasting by (\textbf{20.3\%}, \textbf{22.6\%}), respectively. We also demonstrate that the representation module developed in this work can be used as a general plug-in to improve the long-term prediction performance of other deep learning modules. Code is available at https://github.com/tianzhou2011/FiLM/Comment: Accepted by The Thirty-Sixth Annual Conference on Neural Information Processing Systems (NeurIPS 2022

Systematic comparison of 2A peptides for cloning multi-genes in a polycistronic vector

Cloning of multiple genes in a single vector has greatly facilitated both basic and translational studies that require co-expression of multiple factors or multi-units of complex protein. Many strategies have been adopted, among which 2A "self-cleaving" peptides have garnered increased interest for their polycistronic nature, small size and high "cleavage" efficiency. However, broad application of 2 A peptides is limited by the lack of systematic comparison of different 2As alone or in combination. Here we characterized the effect of varying gene position and 2As on the expression of proteins encoded in bi-, tri-, or quad-cistronic constructs. Using direct cardiac reprogramming as an example, we further determined the effect of varied 2As on the efficiency of fluorescent cell labeling and cell fate conversion. We found that the expression of fluorophores decreased as it was moved towards the end of the construct while reprogramming was most efficient with the fluorophore at the second position. Moreover, quad-cistronic TPE2A constructs resulted in more efficient reprogramming than 3P2A or PTE2A constructs. We expect that the bi-, tri-, and quad-cistronic vectors constructed here and our results on protein expression ratios from different 2A constructs could serve to guide future utilization of 2A peptides in basic research and clinical applications

Development and validation of a patient-specific model to predict postoperative SIRS in older patients: A two-center study

IntroductionPostoperative systemic inflammatory response syndrome (SIRS) is common in surgical patients especially in older patients, and the geriatric population with SIRS is more susceptible to sepsis, MODS, and even death. We aimed to develop and validate a model for predicting postoperative SIRS in older patients.MethodsPatients aged ≥65 years who underwent general anesthesia in two centers of Third Affiliated Hospital of Sun Yat-sen University from January 2015 to September 2020 were included. The cohort was divided into training and validation cohorts. A simple nomogram was developed to predict the postoperative SIRS in the training cohort using two logistic regression models and the brute force algorithm. The discriminative performance of this model was determined by area under the receiver operating characteristics curve (AUC). The external validity of the nomogram was assessed in the validation cohort.ResultsA total of 5,904 patients spanning from January 2015 to December 2019 were enrolled in the training cohort and 1,105 patients from January 2020 to September 2020 comprised the temporal validation cohort, in which incidence rates of postoperative SIRS were 24.6 and 20.2%, respectively. Six feature variables were identified as valuable predictors to construct the nomogram, with high AUCs (0.800 [0.787, 0.813] and 0.822 [0.790, 0.854]) and relatively balanced sensitivity (0.718 and 0.739) as well as specificity (0.718 and 0.729) in both training and validation cohorts. An online risk calculator was established for clinical application.ConclusionWe developed a patient-specific model that may assist in predicting postoperative SIRS among the aged patients

Improved Generation of Induced Cardiomyocytes Using a Polycistronic Construct Expressing Optimal Ratio of Gata4, Mef2c and Tbx5

Direct conversion of cardiac fibroblasts (CFs) into induced cardiomyocytes (iCMs) holds great potential for regenerative medicine by offering alternative strategies for treatment of heart disease. This conversion has been achieved by forced expression of defined factors such as Gata4 (G), Mef2c (M) and Tbx5 (T). Traditionally, iCMs are generated by a cocktail of viruses expressing these individual factors. However, reprogramming efficiency is relatively low and most of the in vitro G,M,T-transduced fibroblasts do not become fully reprogrammed, making it difficult to study the reprogramming mechanisms. We recently have shown that the stoichiometry of G,M,T is crucial for efficient iCM reprogramming. An optimal stoichiometry of G,M,T with relative high level of M and low levels of G and T achieved by using our polycistronic MGT vector (hereafter referred to as MGT) significantly increased reprogramming efficiency and improved iCM quality in vitro. Here we provide a detailed description of the methodology used to generate iCMs with MGT construct from cardiac fibroblasts. Isolation of cardiac fibroblasts, generation of virus for reprogramming and evaluation of the reprogramming process are also included to provide a platform for efficient and reproducible generation of iCMs

Improving Generalization in Visual Reinforcement Learning via Conflict-aware Gradient Agreement Augmentation

Learning a policy with great generalization to unseen environments remains challenging but critical in visual reinforcement learning. Despite the success of augmentation combination in the supervised learning generalization, naively applying it to visual RL algorithms may damage the training efficiency, suffering from serve performance degradation. In this paper, we first conduct qualitative analysis and illuminate the main causes: (i) high-variance gradient magnitudes and (ii) gradient conflicts existed in various augmentation methods. To alleviate these issues, we propose a general policy gradient optimization framework, named Conflict-aware Gradient Agreement Augmentation (CG2A), and better integrate augmentation combination into visual RL algorithms to address the generalization bias. In particular, CG2A develops a Gradient Agreement Solver to adaptively balance the varying gradient magnitudes, and introduces a Soft Gradient Surgery strategy to alleviate the gradient conflicts. Extensive experiments demonstrate that CG2A significantly improves the generalization performance and sample efficiency of visual RL algorithms.Comment: accepted by iccv202

Bmi1 Is a Key Epigenetic Barrier to Direct Cardiac Reprogramming

Direct reprogramming of induced cardiomyocytes (iCMs) suffers from low efficiency and requires extensive epigenetic repatterning, although the underlying mechanisms are largely unknown. To address these issues, we screened for epigenetic regulators of iCM reprogramming and found that reducing levels of the polycomb complex gene Bmi1 significantly enhanced induction of beating iCMs from neonatal and adult mouse fibroblasts. The inhibitory role of Bmi1 in iCM reprogramming is mediated through direct interactions with regulatory regions of cardiogenic genes, rather than regulation of cell proliferation. Reduced Bmi1 expression corresponded with increased levels of the active histone mark H3K4me3 and reduced levels of repressive H2AK119ub at cardiogenic loci, and de-repression of cardiogenic gene expression during iCM conversion. Furthermore, Bmi1 deletion could substitute for Gata4 during iCM reprogramming. Thus, Bmi1 acts as a critical epigenetic barrier to iCM production. Bypassing this barrier simplifies iCM generation and increases yield, potentially streamlining iCM production for therapeutic purposes