Higher-Order Orthogonal Causal Learning for Treatment Effect

Most existing studies on the double/debiased machine learning method concentrate on the causal parameter estimation recovering from the first-order orthogonal score function. In this paper, we will construct the $k^{\mathrm{th}}$-order orthogonal score function for estimating the average treatment effect (ATE) and present an algorithm that enables us to obtain the debiased estimator recovered from the score function. Such a higher-order orthogonal estimator is more robust to the misspecification of the propensity score than the first-order one does. Besides, it has the merit of being applicable with many machine learning methodologies such as Lasso, Random Forests, Neural Nets, etc. We also undergo comprehensive experiments to test the power of the estimator we construct from the score function using both the simulated datasets and the real datasets

The Causal Learning of Retail Delinquency

This paper focuses on the expected difference in borrower's repayment when there is a change in the lender's credit decisions. Classical estimators overlook the confounding effects and hence the estimation error can be magnificent. As such, we propose another approach to construct the estimators such that the error can be greatly reduced. The proposed estimators are shown to be unbiased, consistent, and robust through a combination of theoretical analysis and numerical testing. Moreover, we compare the power of estimating the causal quantities between the classical estimators and the proposed estimators. The comparison is tested across a wide range of models, including linear regression models, tree-based models, and neural network-based models, under different simulated datasets that exhibit different levels of causality, different degrees of nonlinearity, and different distributional properties. Most importantly, we apply our approaches to a large observational dataset provided by a global technology firm that operates in both the e-commerce and the lending business. We find that the relative reduction of estimation error is strikingly substantial if the causal effects are accounted for correctly.Comment: This paper was accepted and will be published in the Thirty-Fifth AAAI Conference on Artificial Intelligence (AAAI-21

Natural Language Interfaces for Tabular Data Querying and Visualization: A Survey

The emergence of natural language processing has revolutionized the way users interact with tabular data, enabling a shift from traditional query languages and manual plotting to more intuitive, language-based interfaces. The rise of large language models (LLMs) such as ChatGPT and its successors has further advanced this field, opening new avenues for natural language processing techniques. This survey presents a comprehensive overview of natural language interfaces for tabular data querying and visualization, which allow users to interact with data using natural language queries. We introduce the fundamental concepts and techniques underlying these interfaces with a particular emphasis on semantic parsing, the key technology facilitating the translation from natural language to SQL queries or data visualization commands. We then delve into the recent advancements in Text-to-SQL and Text-to-Vis problems from the perspectives of datasets, methodologies, metrics, and system designs. This includes a deep dive into the influence of LLMs, highlighting their strengths, limitations, and potential for future improvements. Through this survey, we aim to provide a roadmap for researchers and practitioners interested in developing and applying natural language interfaces for data interaction in the era of large language models.Comment: 20 pages, 4 figures, 5 tables. Submitted to IEEE TKD

ACSM6 overexpression indicates a non-inflammatory tumor microenvironment and predicts treatment response in bladder cancer: results from multiple real-world cohorts

Background: ACSMs play critical roles in lipid metabolism; however, their immunological function within the tumor microenvironment (TME) remains unclear, especially that of ACSM6. In this study, we investigate the latent effect of ACSM6 on bladder cancer (BLCA).Methods: Several real-world cohorts, including the Xiangya (in-house), The Cancer Genome Atlas (TCGA-BLCA), and IMvigor210 cohorts, with TCGA-BLCA cohort serving as the discovery cohort were compared. We investigated the potential immunological effects of ACSM6 in regulating the BLCA tumor microenvironment by analyzing its correlation with immunomodulators, anti-cancer immune cycles, immune checkpoints, tumor-infiltrating immune cells, and the T-cell inflamed score (TIS). Additionally, we assessed the precision of ACSM6 in predicting BLCA molecular subtypes and responses to several treatments using ROC analysis. To ensure the robustness of our findings, all results were confirmed in two independent external cohorts: the IMvigor210 and Xiangya cohorts.Results: ACSM6 expression was markedly upregulated in BLCA. Our analysis suggests that ACSM6 might have significant impact to promote the formation of a non-inflamed tumor microenvironment because of its negative correlation with immunomodulators, anticancer immune cycles, immune checkpoints, tumor-infiltrating immune cells, and the T-cell inflamed score (TIS). Additionally, high ACSM6 expression levels in BLCA may predict the luminal subtype, which is typically associated with resistance to chemotherapy, neoadjuvant chemotherapy, and radiotherapy. These findings were consistent across both the IMvigor210 and Xiangya cohorts.Conclusion: ACSM6 has the potential to serve as a valuable predictor of the tumor microenvironment phenotypes and treatment outcomes in BLCA, thereby contributing to more precise treatment

Mosar: Efficiently Characterizing Both Frequent and Rare Motifs in Large Graphs

Due to high computational costs, exploring motif statistics (such as motif frequencies) of a large graph can be challenging. This is useful for understanding complex networks such as social and biological networks. To address this challenge, many methods explore approximate algorithms using edge/path sampling techniques. However, state-of-the-art methods usually over-sample frequent motifs and under-sample rare motifs, and thus they fail in many real applications such as anomaly detection (i.e., finding rare patterns). Furthermore, it is not feasible to apply existing weighted sampling methods such as stratified sampling to solve this problem, because it is difficult to sample subgraphs from a large graph in a direct manner. In this paper, we observe that rare motifs of most real-world networks have “more edges” than frequent motifs, and motifs with more edges are sampled by random edge sampling with higher probabilities. Based on these two observations, we propose a novel motif sampling method, Mosar, to estimate motif frequencies. In particular, our Mosar method samples frequent and rare motifs with different probabilities, and tends to sample motifs with low frequencies. As a result, the new method greatly reduces the estimation errors of these rare motifs. Finally, we conducted extensive experiments on a variety of real-world datasets with different sizes, and our experimental results show that the Mosar method is two orders of magnitude more accurate than state-of-the-art methods

Mosar: Efficiently Characterizing Both Frequent and Rare Motifs in Large Graphs

Due to high computational costs, exploring motif statistics (such as motif frequencies) of a large graph can be challenging. This is useful for understanding complex networks such as social and biological networks. To address this challenge, many methods explore approximate algorithms using edge/path sampling techniques. However, state-of-the-art methods usually over-sample frequent motifs and under-sample rare motifs, and thus they fail in many real applications such as anomaly detection (i.e., finding rare patterns). Furthermore, it is not feasible to apply existing weighted sampling methods such as stratified sampling to solve this problem, because it is difficult to sample subgraphs from a large graph in a direct manner. In this paper, we observe that rare motifs of most real-world networks have “more edges” than frequent motifs, and motifs with more edges are sampled by random edge sampling with higher probabilities. Based on these two observations, we propose a novel motif sampling method, Mosar, to estimate motif frequencies. In particular, our Mosar method samples frequent and rare motifs with different probabilities, and tends to sample motifs with low frequencies. As a result, the new method greatly reduces the estimation errors of these rare motifs. Finally, we conducted extensive experiments on a variety of real-world datasets with different sizes, and our experimental results show that the Mosar method is two orders of magnitude more accurate than state-of-the-art methods

Mechanisms by which spinal cord stimulation intervenes in atrial fibrillation: The involvement of the endothelin-1 and nerve growth factor/p75NTR pathways

Can the spinal cord stimulation (SCS) regulate the autonomic nerves through the endothelin-1 (ET-1) and nerve growth factor (NGF)/p75NTR pathways and thus inhibit the occurrence of atrial fibrillation (AF)? In our research, 16 beagles were randomly divided into a rapid atrial pacing (RAP) group (n = 8) and a RAP + SCS group (n = 8), and the effective refractory period (ERP), ERP dispersion, AF induction rate, and AF vulnerability window (WOV) at baseline, 6 h of RAP, 6 h of RAP + SCS were measured. The atrial tissue was then taken for immunohistochemical analysis to determine the localization of ET-1, NGF, p75NTR, NF-kB p65, and other genes. Our results showed that SCS attenuated the shortening of ERP in all parts caused by RAP, and after 6 h of SCS, the probability of AF in dogs was reduced compared with that in the RAP group. Moreover, the expression of ET-1, NGF, and p75NTR in the atrial tissues of dogs in the RAP + SCS group was significantly increased, but the expression of NF-kB p65 was reduced. In conclusion, SCS promotes the positive remodeling of cardiac autonomic nerves by weakening NFκB p65-dependent pathways to interfere with the ET-1 and NGF/p75NTR pathways to resist the original negative remodeling and inhibit the occurrence of AF

Near-Infrared Light Triggered the Shape Memory Behavior of Polydopamine-Nanoparticle-Filled Epoxy Acrylate

Through the effective combination of photothermal conversion agent polydopamine (PDA) nanoparticles and epoxy acrylate polymer (EA), a new kind of near-infrared (NIR) light-triggered shape memory polymer (PDA/EA) is developed. Due to the outstanding photothermal effect of PDA, even with a very low concentration of PDA (0.1 wt.%), when exposed to an 808 nm NIR light with a power of 1 W/cm2, the temporary shapes can be fully light-responsive, recovered in 60 s. Based on dynamic thermomechanical analysis and thermal gravimetric analysis, it can be seen that the introduction of PDA is beneficial for improving dynamic mechanical properties and thermal resistance compared to EA. As an environmentally friendly and highly efficient photoactive SMP, PDA/EA has a great application prospect

Transplantation of D15A-Expressing Glial-Restricted-Precursor-Derived Astrocytes Improves Anatomical and Locomotor Recovery after Spinal Cord Injury

<p>The transplantation of neural stem/progenitor cells is a promising therapeutic strategy for spinal cord injury (SCI). In this study, we tested whether combination of neurotrophic factors and transplantation of glial-restricted precursor (GRPs)-derived astrocytes (GDAs) could decrease the injury and promote functional recovery after SCI. We developed a protocol to quickly produce a sufficiently large, homogenous population of young astrocytes from GRPs, the earliest arising progenitor cell population restricted to the generation of glia. GDAs expressed the axonal regeneration promoting substrates, laminin and fibronectin, but not the inhibitory chondroitin sulfate proteoglycans (CSPGs). Importantly, GDAs or its conditioned medium promoted the neurite outgrowth of dorsal root ganglion neurons in vitro. GDAs were infected with retroviruses expressing EGFP or multi-neurotrophin D15A and transplanted into the contused adult thoracic spinal cord at 8 days post-injury. Eight weeks after transplantation, the grafted GDAs survived and integrated into the injured spinal cord. Grafted GDAs expressed GFAP, suggesting they remained astrocyte lineage in the injured spinal cord. But it did not express CSPG. Robust axonal regeneration along the grafted GDAs was observed. Furthermore, transplantation of D15A-GDAs significantly increased the spared white matter and decreased the injury size compared to other control groups. More importantly, transplantation of D15A-GDAs significantly improved the locomotion function recovery shown by BBB locomotion scores and Tredscan footprint analyses. However, this combinatorial strategy did not enhance the aberrant synaptic connectivity of pain afferents, nor did it exacerbate posttraumatic neuropathic pain. These results demonstrate that transplantation of D15A-expressing GDAs promotes anatomical and locomotion recovery after SCI, suggesting it may be an effective therapeutic approach for SCI.</p