BIO regulates the ex vivo expansion and function of hematopoietic stem cells by inhibiting GSK-3β

Hematopoietic stem cells (HSCs) have been applied in clinic settings for treating hematologic diseases, including leukemic disorders, immune deficiencies, and hemoglobinopathies. Umbilical cord blood(UCB) is an important source of HSCs. However, the low frequency of HSCs per unit of UCB remains a big hurdle to their wider applications. Wnt/β-catenin pathway plays important roles in the self-renewal of HSCs in vivo, but the roles of Wnt/β-catenin signaling on ex vivo expansion of HSCs remains controversial. GSK3β is the major regulator of Wnt pathway. Here, we evaluate the effects of 6-bromoindirubin-3’-oxime (BIO), a GSK3β inhibitor, on ex vivo expansion characteristics and regenerative potential of (UCB)-derived CD34+ cells. Please click Additional Files below to see the full abstract

TreeGen: A Tree-Based Transformer Architecture for Code Generation

A code generation system generates programming language code based on an input natural language description. State-of-the-art approaches rely on neural networks for code generation. However, these code generators suffer from two problems. One is the long dependency problem, where a code element often depends on another far-away code element. A variable reference, for example, depends on its definition, which may appear quite a few lines before. The other problem is structure modeling, as programs contain rich structural information. In this paper, we propose a novel tree-based neural architecture, TreeGen, for code generation. TreeGen uses the attention mechanism of Transformers to alleviate the long-dependency problem, and introduces a novel AST reader (encoder) to incorporate grammar rules and AST structures into the network. We evaluated TreeGen on a Python benchmark, HearthStone, and two semantic parsing benchmarks, ATIS and GEO. TreeGen outperformed the previous state-of-the-art approach by 4.5 percentage points on HearthStone, and achieved the best accuracy among neural network-based approaches on ATIS (89.1%) and GEO (89.6%). We also conducted an ablation test to better understand each component of our model

CupCleaner: A Data Cleaning Approach for Comment Updating

Recently, deep learning-based techniques have shown promising performance on various tasks related to software engineering. For these learning-based approaches to perform well, obtaining high-quality data is one fundamental and crucial issue. The comment updating task is an emerging software engineering task aiming at automatically updating the corresponding comments based on changes in source code. However, datasets for the comment updating tasks are usually crawled from committed versions in open source software repositories such as GitHub, where there is lack of quality control of comments. In this paper, we focus on cleaning existing comment updating datasets with considering some properties of the comment updating process in software development. We propose a semantic and overlapping-aware approach named CupCleaner (Comment UPdating's CLEANER) to achieve this purpose. Specifically, we calculate a score based on semantics and overlapping information of the code and comments. Based on the distribution of the scores, we filter out the data with low scores in the tail of the distribution to get rid of possible unclean data. We first conducted a human evaluation on the noise data and high-quality data identified by CupCleaner. The results show that the human ratings of the noise data identified by CupCleaner are significantly lower. Then, we applied our data cleaning approach to the training and validation sets of three existing comment updating datasets while keeping the test set unchanged. Our experimental results show that even after filtering out over 30\% of the data using CupCleaner, there is still an improvement in all performance metrics. The experimental results on the cleaned test set also suggest that CupCleaner may provide help for constructing datasets for updating-related tasks

Generalized Equivariance and Preferential Labeling for GNN Node Classification

Existing graph neural networks (GNNs) largely rely on node embeddings, which represent a node as a vector by its identity, type, or content. However, graphs with unattributed nodes widely exist in real-world applications (e.g., anonymized social networks). Previous GNNs either assign random labels to nodes (which introduces artefacts to the GNN) or assign one embedding to all nodes (which fails to explicitly distinguish one node from another). Further, when these GNNs are applied to unattributed node classification problems, they have an undesired equivariance property, which are fundamentally unable to address the data with multiple possible outputs. In this paper, we analyze the limitation of existing approaches to node classification problems. Inspired by our analysis, we propose a generalized equivariance property and a Preferential Labeling technique that satisfies the desired property asymptotically. Experimental results show that we achieve high performance in several unattributed node classification tasks

Biomass Straw Based Activated Porous Carbon Materials for High-Performance Supercapacitors

Biomass straws are often regarding as agricultural waste, usually burned off in rural areas, which results in severe resource waste and air pollution. In this work, biomass-based porous carbon material with a lamellar microstructure is obtained via simple hydrothermal and subsequent KOH activation, the optimum activate process is determined by the proportion of activator. Scanning electron microscopy (SEM) and nitrogen adsorption techniques are conducted to investigate the physical properties of the materials. Cyclic voltammetry and constant current discharge/charge in the three-electrode system and symmetrical double-layer capacitors results indicate the best electrochemical performance of SCA-1.5 electrode material, with a capacity of 250.0 F g-1 at 1.0 A g-1. And notably, high recycling stability at a high cycling rate of 1.0 A g-1 after 18,000 cycles

Tailoring Intermolecular Interactions Towards High‐Performance Thermoelectric Ionogels at Low Humidity

Development of ionic thermoelectric (iTE) materials is of immense interest for efficient heat-to-electricity conversion due to their giant ionic Seebeck coefficient (Si), but challenges remain in terms of relatively small Si at low humidity, poor stretchability, and ambiguous interaction mechanism in ionogels. Herein, a novel ionogel is reported consisting of polyethylene oxide (PEO), polyethylene oxide-polypropylene oxide-polyethylene oxide (P123), and 1-ethyl-3-methylimidazolium acetate (Emim:OAC). By delicately designing the interactions between ions and polymers, the migration of anions is restricted due to their strong binding with the hydroxyl groups of polymers, while the transport of cations is facilitated through segmental motions due to the increased amorphous regions, thereby leading to enlarged diffusion difference between the cations and anions. Moreover, the plasticizing effect of P123 and Emim:OAC can increase the elongation at break. As a consequence, the ionogel exhibits excellent properties including high Si (18 mV K−1 at relative humidity of 60%), good ionic conductivity (1.1 mS cm−1), superior stretchability (787%), and high stability (over 80% retention after 600 h). These findings show a promising strategy to obtain multifunctional iTE materials by engineering the intermolecular interactions and demonstrate the great potential of ionogels for harvesting low-grade heat in human-comfortable humidity environments

WuYun: Exploring hierarchical skeleton-guided melody generation using knowledge-enhanced deep learning

Although deep learning has revolutionized music generation, existing methods for structured melody generation follow an end-to-end left-to-right note-by-note generative paradigm and treat each note equally. Here, we present WuYun, a knowledge-enhanced deep learning architecture for improving the structure of generated melodies, which first generates the most structurally important notes to construct a melodic skeleton and subsequently infills it with dynamically decorative notes into a full-fledged melody. Specifically, we use music domain knowledge to extract melodic skeletons and employ sequence learning to reconstruct them, which serve as additional knowledge to provide auxiliary guidance for the melody generation process. We demonstrate that WuYun can generate melodies with better long-term structure and musicality and outperforms other state-of-the-art methods by 0.51 on average on all subjective evaluation metrics. Our study provides a multidisciplinary lens to design melodic hierarchical structures and bridge the gap between data-driven and knowledge-based approaches for numerous music generation tasks