A Provable Smoothing Approach for High Dimensional Generalized Regression with Applications in Genomics

In many applications, linear models fit the data poorly. This article studies an appealing alternative, the generalized regression model. This model only assumes that there exists an unknown monotonically increasing link function connecting the response $Y$ to a single index $X^T\beta^*$ of explanatory variables $X\in\mathbb{R}^d$. The generalized regression model is flexible and covers many widely used statistical models. It fits the data generating mechanisms well in many real problems, which makes it useful in a variety of applications where regression models are regularly employed. In low dimensions, rank-based M-estimators are recommended to deal with the generalized regression model, giving root-$n$ consistent estimators of $\beta^*$. Applications of these estimators to high dimensional data, however, are questionable. This article studies, both theoretically and practically, a simple yet powerful smoothing approach to handle the high dimensional generalized regression model. Theoretically, a family of smoothing functions is provided, and the amount of smoothing necessary for efficient inference is carefully calculated. Practically, our study is motivated by an important and challenging scientific problem: decoding gene regulation by predicting transcription factors that bind to cis-regulatory elements. Applying our proposed method to this problem shows substantial improvement over the state-of-the-art alternative in real data.Comment: 53 page

Topology-aware Debiased Self-supervised Graph Learning for Recommendation

In recommendation, graph-based Collaborative Filtering (CF) methods mitigate the data sparsity by introducing Graph Contrastive Learning (GCL). However, the random negative sampling strategy in these GCL-based CF models neglects the semantic structure of users (items), which not only introduces false negatives (negatives that are similar to anchor user (item)) but also ignores the potential positive samples. To tackle the above issues, we propose Topology-aware Debiased Self-supervised Graph Learning (TDSGL) for recommendation, which constructs contrastive pairs according to the semantic similarity between users (items). Specifically, since the original user-item interaction data commendably reflects the purchasing intent of users and certain characteristics of items, we calculate the semantic similarity between users (items) on interaction data. Then, given a user (item), we construct its negative pairs by selecting users (items) which embed different semantic structures to ensure the semantic difference between the given user (item) and its negatives. Moreover, for a user (item), we design a feature extraction module that converts other semantically similar users (items) into an auxiliary positive sample to acquire a more informative representation. Experimental results show that the proposed model outperforms the state-of-the-art models significantly on three public datasets. Our model implementation codes are available at https://github.com/malajikuai/TDSGL.Comment: 6 pages,8 figure

Denoising Distantly Supervised Named Entity Recognition via a Hypergeometric Probabilistic Model

Denoising is the essential step for distant supervision based named entity recognition. Previous denoising methods are mostly based on instance-level confidence statistics, which ignore the variety of the underlying noise distribution on different datasets and entity types. This makes them difficult to be adapted to high noise rate settings. In this paper, we propose Hypergeometric Learning (HGL), a denoising algorithm for distantly supervised NER that takes both noise distribution and instance-level confidence into consideration. Specifically, during neural network training, we naturally model the noise samples in each batch following a hypergeometric distribution parameterized by the noise-rate. Then each instance in the batch is regarded as either correct or noisy one according to its label confidence derived from previous training step, as well as the noise distribution in this sampled batch. Experiments show that HGL can effectively denoise the weakly-labeled data retrieved from distant supervision, and therefore results in significant improvements on the trained models.Comment: Accepted to AAAI202

Prompt-enhanced Hierarchical Transformer Elevating Cardiopulmonary Resuscitation Instruction via Temporal Action Segmentation

The vast majority of people who suffer unexpected cardiac arrest are performed cardiopulmonary resuscitation (CPR) by passersby in a desperate attempt to restore life, but endeavors turn out to be fruitless on account of disqualification. Fortunately, many pieces of research manifest that disciplined training will help to elevate the success rate of resuscitation, which constantly desires a seamless combination of novel techniques to yield further advancement. To this end, we collect a custom CPR video dataset in which trainees make efforts to behave resuscitation on mannequins independently in adherence to approved guidelines, thereby devising an auxiliary toolbox to assist supervision and rectification of intermediate potential issues via modern deep learning methodologies. Our research empirically views this problem as a temporal action segmentation (TAS) task in computer vision, which aims to segment an untrimmed video at a frame-wise level. Here, we propose a Prompt-enhanced hierarchical Transformer (PhiTrans) that integrates three indispensable modules, including a textual prompt-based Video Features Extractor (VFE), a transformer-based Action Segmentation Executor (ASE), and a regression-based Prediction Refinement Calibrator (PRC). The backbone of the model preferentially derives from applications in three approved public datasets (GTEA, 50Salads, and Breakfast) collected for TAS tasks, which accounts for the excavation of the segmentation pipeline on the CPR dataset. In general, we unprecedentedly probe into a feasible pipeline that genuinely elevates the CPR instruction qualification via action segmentation in conjunction with cutting-edge deep learning techniques. Associated experiments advocate our implementation with multiple metrics surpassing 91.0%.Comment: Transformer for Cardiopulmonary Resuscitatio

Evaluation of β2-microglobulin in the condition and prognosis of psoriasis patients.

BACKGROUND Numerous studies have linked the inflammatory pathway in psoriasis and metabolic disease, while no specific marker defined it. It is worth exploring the association of β2-microglobulin (β2M) in psoriasis severity and comorbidities. OBJECTIVES To investigate the correlation between blood β2M level and psoriasis severity, to explore the inflammatory factors influencing the occurrence of psoriasis comorbidities such as arthritis, diabetes, and hypertension. METHODS Ninety-seven psoriasis patients were analyzed in the cohort retrospective study during 12 weeks. RESULTS Significantly higher levels of blood β2M and ESR were observed in the group that patients' PASI ≥10 than in the group that PASI <10. Blood β2M level had strong significantly positive correlations with the PASI in Pearson's correlation analysis. In the model that systemic inflammatory factors to find psoriasis comorbidity risk factors, logistic regression analysis showed that blood β2M level was the significant risk factor associated with diabetes and hypertension. High-sensitivity C-reactive protein (hsCRP) was the significant risk factor associated with arthritis. CONCLUSIONS Patients with a severer psoriasis tended to have higher blood β2M levels and severer inflammatory state. In the systemic inflammation indexes, the level of blood β2M affected the risk of hypertension and diabetes, and hsCRP affected the risk of arthritis in patients with psoriasis

Tumor-Intrinsic Sirpa Promotes Sensitivity to Checkpoint Inhibition Immunotherapy in Melanoma

Checkpoint inhibition immunotherapy has revolutionized cancer treatment, but many patients show resistance. Here we perform integrative transcriptomic and proteomic analyses on emerging immuno-oncology targets across multiple clinical cohorts of melanoma under anti-PD-1 treatment, on both bulk and single-cell levels. We reveal a surprising role of tumor-intrinsic SIRPA in enhancing antitumor immunity, in contrast to its well-established role as a major inhibitory immune modulator in macrophages. The loss of SIRPA expression is a marker of melanoma dedifferentiation, a key phenotype linked to immunotherapy efficacy. Inhibition of SIRPA in melanoma cells abrogates tumor killing by activated CD

MTHFR Gene Polymorphism Association With Psoriatic Arthritis Risk and the Efficacy and Hepatotoxicity of Methotrexate in Psoriasis.

Aims To assess whether MTHFR rs1801131 and rs1801133 SNPs are associated with concomitant psoriatic arthritis (PsA) and investigate the efficacy and hepatotoxicity of MTX in patients with psoriasis in the Han Chinese population. Methods This prospective, single-arm, interventional study recruited a total of 309 patients with psoriasis, 163 with psoriatic arthritis and 146 without psoriatic arthritis, who completed a 12-week MTX treatment and 1,031 healthy controls. Patients' characteristics including age, gender, disease duration, height, weight, smoking status, alcohol consumption, medical history, disease severity and liver function test results were accessed and recorded. Single nucleotide polymorphism (SNP) genotyping of rs1801131 and rs1801133 in the MTHFR gene was performed. Results The rs1801133 CC genotype was more frequent in patients with PsA than those with PsO and healthy controls (42.3% vs. 28.8% vs. 33.1%, p < 0.05). The 90% reduction from baseline PASI score (PASI 90) response rates to MTX were significantly higher in patients with the rs1801133 TT genotype than those with the CT and CC genotype (33.96% vs. 19.31% vs. 14.41%, OR = 2.76, p = 0.006). The rs1801133 CT+TT genotype was more frequent in PsA patients with abnormal liver function than in those with normal liver function (p < 0.05). In addition, patients with the rs1801131 CT genotype had lower PASI 75 response rates to MTX (OR = 0.49, p = 0.01), and lower risk of ALT elevation (OR = 0.46, p = 0.04). Conclusions This study provided some evidence for MTHFR polymorphism association with the risk of PsA and the efficacy and hepatotoxicity of the low-dose MTX in the Chinese population. Given the relatively small sample size and potentially missed diagnosis of PsA, the results from this study warrant further investigation

A pan-cancer analysis of enhancer expression in nearly 9000 patient samples

The role of enhancers, a key class of non-coding regulatory DNA elements, in cancer development has increasingly been appreciated. Here, we present the detection and characterization of a large number of expressed enhancers in a genome-wide analysis of 8928 tumor samples across 33 cancer types using TCGA RNA-seq data. Compared with matched normal tissues, global enhancer activation was observed in most cancers. Across cancer types, global enhancer activity was positively associated with aneuploidy, but not mutation load, suggesting a hypothesis centered on “chromatin-state” to explain their interplay. Integrating eQTL, mRNA co-expression, and Hi-C data analysis, we developed a computational method to infer causal enhancer-gene interactions, revealing enhancers of clinically actionable genes. Having identified an enhancer ∼140 kb downstream of PD-L1, a major immunotherapy target, we validated it experimentally. This study provides a systematic view of enhancer activity in diverse tumor contexts and suggests the clinical implications of enhancers