Dynamic Testing System For Rocks Under In Situ Stresses

Rocks may be subjected to dynamic disturbances while under high in situ stresses. When disturbed by dynamic loads from blasting, seismicity or rockbursts, the underground structures would be vulnerable to failure. Depending on the distance from the underground opening, the in situ stress states change from hydrostatic in the far-field, to triaxial in the intermediate distance, and to the pre-tension nearby the opening. Thus, SHPB testing system is further adjusted with confining pressure system into dynamic testing system of rocks under different in situ states. In the experiment with this dynamic testing system, the Brazilian disc rock specimens are first subjected to pre-stresses simulating in-situ stresses underground (including pre-tension, hydrostatic confinement, and triaxial confinement) and then loaded dynamically using the modified SHPB system. The dependence of dynamic tensile strength of the rock material on the static pre-stress and loading rate is investigated. These experimental results will be of great importance in the design and safety of underground rock engineering projects

Overexpression of BplERD15 enhances drought tolerance in Betula platyphylla Suk

In this study, we report the cloning and functional characterization of an early responsive gene, BplERD15, from Betula platyphylla Suk to dehydration. BplERD15 is located in the same branch as Morus indica Linnaeus ERD15 and Arabidopsis Heynh ERD15 in the phylogenetic tree built with ERD family protein sequences. The tissue-specific expression patterns of BplERD15 were characterized using qRT-PCR and the results showed that the transcript levels of BplERD15 in six tissues were ranked from the highest to the lowest levels as the following: mature leaves (ML) \u3e young leaves (YL) \u3e roots (R) \u3ebuds (B) \u3eyoung stems (YS) \u3emature stems (MS). Multiple drought experiments were simulated by adding various osmotica including polyethylene glycol, mannitol, and NaCl to the growth media to decrease their water potentials, and the results showed that the expression of BplERD15 could be induced to 12, 9, and 10 folds, respectively, within a 48 h period. However, the expression level of BplERD15 was inhibited by the plant hormone abscisic acid in the early response and then restored to the level of control. The BplERD15 overexpression (OE) transgenic birch lines were developed and they did not exhibit any phenotypic anomalies and growth deficiency under normal condition. Under drought condition, BplERD15-OE1, 3, and 4 all displayed some drought tolerant characteristics and survived from the drought while the wild type (WT) plants withered and then died. Analysis showed that all BplERD15-OE lines had significant lower electrolyte leakage levels as compared to WT. Our study suggests that BplERD15 is a drought-responsive gene that can reduce mortality under stress condition

A systems biology approach identifies a regulator, BplERF1, of cold tolerance in Betula platyphylla

Cold is an abiotic stress that can greatly affect the growth and survival of plants. Here, we reported that an AP2/ERF family gene, BplERF1, isolated from Betula platyphylla played a contributing role in cold stress tolerance. Overexpression of BplERF1 in B. platyphylla transgenic lines enhanced cold stress tolerance by increasing the scavenging capability and reducing H2O2 and malondialdehyde (MDA) content in transgenic plants. Construction of BplERF-mediated multilayered hierarchical gene regulatory network (ML-hGRN), using Top-down GGM algorithm and the transcriptomic data of BplERF1 overexpression lines, led to the identification of five candidate target genes of BplERF1 which include MPK20, ERF9, WRKY53, WRKY70, and GIA1. All of them were then verified to be the true target genes of BplERF1 by chromatin-immunoprecipitation PCR (ChIP-PCR) assay. Our results indicate that BplERF1 is a positive regulator of cold tolerance and is capable of exerting regulation on the expression of cold signaling and regulatory genes, causing mitigation of reactive oxygen species

An Adaptive Incremental Gradient Method With Support for Non-Euclidean Norms

Stochastic variance reduced methods have shown strong performance in solving finite-sum problems. However, these methods usually require the users to manually tune the step-size, which is time-consuming or even infeasible for some large-scale optimization tasks. To overcome the problem, we propose and analyze several novel adaptive variants of the popular SAGA algorithm. Eventually, we design a variant of Barzilai-Borwein step-size which is tailored for the incremental gradient method to ensure memory efficiency and fast convergence. We establish its convergence guarantees under general settings that allow non-Euclidean norms in the definition of smoothness and the composite objectives, which cover a broad range of applications in machine learning. We improve the analysis of SAGA to support non-Euclidean norms, which fills the void of existing work. Numerical experiments on standard datasets demonstrate a competitive performance of the proposed algorithm compared with existing variance-reduced methods and their adaptive variants

Growth-regulating factor 5 (GRF5)-mediated gene regulatory network promotes leaf growth and expansion in poplar

Although polyploid plants have larger leaves than their diploid counterparts, the molecular mechanisms underlying this difference (or trait) remain elusive. Differentially expressed genes (DEGs) between triploid and full-sib diploid poplar trees were identified from two transcriptomic data sets followed by a gene association study among DEGs to identify key leaf growth regulators. Yeast one-hybrid system, electrophoretic mobility shift assay, and dual-luciferase assay were employed to substantiate that PpnGRF5-1 directly regulated PpnCKX1. The interactions between PpnGRF5-1 and growth-regulating factor (GRF)-interacting factors (GIFs) were experimentally validated and a multilayered hierarchical regulatory network (ML-hGRN)-mediated by PpnGRF5-1 was constructed with top-down graphic Gaussian model (GGM) algorithm by combining RNA-sequencing data from its overexpression lines and DAP-sequencing data. PpnGRF5-1 is a negative regulator of PpnCKX1. Overexpression of PpnGRF5-1 in diploid transgenic lines resulted in larger leaves resembling those of triploids, and significantly increased zeatin and isopentenyladenine in the apical buds and third leaves. PpnGRF5-1 also interacted with GIFs to increase its regulatory diversity and capacity. An ML-hGRN-mediated by PpnGRF5-1 was obtained and could largely elucidate larger leaves. PpnGRF5-1 and the ML-hGRN-mediated by PpnGRF5-1 were underlying the leaf growth and development

Understanding and Improving Feature Learning for Out-of-Distribution Generalization

A common explanation for the failure of out-of-distribution (OOD) generalization is that the model trained with empirical risk minimization (ERM) learns spurious features instead of invariant features. However, several recent studies challenged this explanation and found that deep networks may have already learned sufficiently good features for OOD generalization. Despite the contradictions at first glance, we theoretically show that ERM essentially learns both spurious and invariant features, while ERM tends to learn spurious features faster if the spurious correlation is stronger. Moreover, when fed the ERM learned features to the OOD objectives, the invariant feature learning quality significantly affects the final OOD performance, as OOD objectives rarely learn new features. Therefore, ERM feature learning can be a bottleneck to OOD generalization. To alleviate the reliance, we propose Feature Augmented Training (FeAT), to enforce the model to learn richer features ready for OOD generalization. FeAT iteratively augments the model to learn new features while retaining the already learned features. In each round, the retention and augmentation operations are performed on different subsets of the training data that capture distinct features. Extensive experiments show that FeAT effectively learns richer features thus boosting the performance of various OOD objectives.Comment: Yongqiang Chen, Wei Huang, and Kaiwen Zhou contributed equally; NeurIPS 2023, 55 pages, 64 figure

RLTF: Reinforcement Learning from Unit Test Feedback

The goal of program synthesis, or code generation, is to generate executable code based on given descriptions. Recently, there has been an increasing number of studies employing reinforcement learning (RL) to improve the performance of large language models (LLMs) for code. However, these RL methods have only used offline frameworks, limiting their exploration of new sample spaces. Additionally, current approaches that utilize unit test signals are rather simple, not accounting for specific error locations within the code. To address these issues, we proposed RLTF, i.e., Reinforcement Learning from Unit Test Feedback, a novel online RL framework with unit test feedback of multi-granularity for refining code LLMs. Our approach generates data in real-time during training and simultaneously utilizes fine-grained feedback signals to guide the model towards producing higher-quality code. Extensive experiments show that RLTF achieves state-of-the-art performance on the APPS and the MBPP benchmarks. Our code can be found at: https://github.com/Zyq-scut/RLTF

PPN: Parallel Pointer-based Network for Key Information Extraction with Complex Layouts

Key Information Extraction (KIE) is a challenging multimodal task that aims to extract structured value semantic entities from visually rich documents. Although significant progress has been made, there are still two major challenges that need to be addressed. Firstly, the layout of existing datasets is relatively fixed and limited in the number of semantic entity categories, creating a significant gap between these datasets and the complex real-world scenarios. Secondly, existing methods follow a two-stage pipeline strategy, which may lead to the error propagation problem. Additionally, they are difficult to apply in situations where unseen semantic entity categories emerge. To address the first challenge, we propose a new large-scale human-annotated dataset named Complex Layout form for key information EXtraction (CLEX), which consists of 5,860 images with 1,162 semantic entity categories. To solve the second challenge, we introduce Parallel Pointer-based Network (PPN), an end-to-end model that can be applied in zero-shot and few-shot scenarios. PPN leverages the implicit clues between semantic entities to assist extracting, and its parallel extraction mechanism allows it to extract multiple results simultaneously and efficiently. Experiments on the CLEX dataset demonstrate that PPN outperforms existing state-of-the-art methods while also offering a much faster inference speed

Dynamic Mode Ⅱ fracture behavior of rocks under hydrostatic pressure using the short core in compression (SCC) method

The shear failure of rocks under both a static triaxial stress and a dynamic disturbance is common in deep underground engineering and it is therefore essential for the design of underground engineering to quantitively estimate the dynamic Mode Ⅱ fracture toughness KⅡC of rocks under a triaxial stress state. However, the method for determining the dynamic KⅡC of rocks under a triaxial stress has not been developed yet. With an optimal sample preparation, the short core in compression (SCC) method was designed and verified in this study to measure the dynamic KⅡC of Fangshan marble (FM) subjected to different hydrostatic pressures through a triaxial dynamic testing system. The formula for calculating the dynamic KⅡC of the rock SCC specimen under hydrostatic pressures was obtained by using the finite element method in combination with secondary cracks. The experimental results indicate that the failure mode of the rock SCC specimen under a hydrostatic pressure is the shear fracture and the KⅡC of FM increases as the loading rate. In addition, at a given loading rate the dynamic rock KⅡC is barely affected by hydrostatic pressures. Another important observation is that the dynamic fracture energy of FM enhances with loading rates and hydrostatic pressures.publishedVersionPeer reviewe

Digoxin protects against intervertebral disc degeneration via TNF/NF-κB and LRP4 signaling

BackgroundIntervertebral disc degeneration (IVDD) is a leading cause of low back pain (LBP). The pathological process of IVDD is associated with inflammatory reactions and extracellular matrix (ECM) disorders. Digoxin is widely used for treating heart failure, and it has been reported to have anti-inflammatory effects.ObjectiveThis study is to investigate the role of digoxin in the pathogenesis of intervertebral disc degeneration as well as the involved molecular mechanism, particularly the potential target protein.MethodsWe exploited a rat needle model to investigate digoxin’s role in intervertebral disc degeneration in vivo. Safranin O staining was used to measure cartilaginous tissue in the intervertebral disc. The morphological changes of intervertebral discs in animal models were determined by Hematoxylin-Eosin (H&E) staining and the pathological score. Primary nucleus pulposus cells (NP cells) from intervertebral discs of patients and murine were used in the present study. Western-Blotting assay, Real-time PCR assay, immunofluorescence staining, and immunochemistry were used to detect the role of digoxin in anti-TNF-α-induced inflammatory effects in vitro. Transfection of siRNA was used to regulate low-density lipoprotein receptor-related protein 4 (LRP4) expression in NP cells to investigate the potential protein target of digoxin.ResultsDigoxin protected against intervertebral disc degeneration in rat needle models. Digoxin was found to exert its disc-protective effects through at least three different pathways by a) suppressing TNF-α-induced inflammation, b) attenuating ECM destruction, c) significantly promoting ECM anabolism. Additionally, LRP4 was found to be the downstream molecule of digoxin in NP cells for anti-inflammation and regulation of ECM metabolism. The knockdown of LRP4 downregulated the protective effect of digoxin in NP cells.ConclusionThese findings suggest that digoxin may be a potential therapeutic agent for intervertebral disc degeneration through anti-catabolism and pro-anabolism. Digoxin might also work as an alternative for other inflammation-related diseases