Towards Open-World Product Attribute Mining: A Lightly-Supervised Approach

We present a new task setting for attribute mining on e-commerce products, serving as a practical solution to extract open-world attributes without extensive human intervention. Our supervision comes from a high-quality seed attribute set bootstrapped from existing resources, and we aim to expand the attribute vocabulary of existing seed types, and also to discover any new attribute types automatically. A new dataset is created to support our setting, and our approach Amacer is proposed specifically to tackle the limited supervision. Especially, given that no direct supervision is available for those unseen new attributes, our novel formulation exploits self-supervised heuristic and unsupervised latent attributes, which attains implicit semantic signals as additional supervision by leveraging product context. Experiments suggest that our approach surpasses various baselines by 12 F1, expanding attributes of existing types significantly by up to 12 times, and discovering values from 39% new types.Comment: Accepted to ACL 202

PV2TEA: Patching Visual Modality to Textual-Established Information Extraction

Information extraction, e.g., attribute value extraction, has been extensively studied and formulated based only on text. However, many attributes can benefit from image-based extraction, like color, shape, pattern, among others. The visual modality has long been underutilized, mainly due to multimodal annotation difficulty. In this paper, we aim to patch the visual modality to the textual-established attribute information extractor. The cross-modality integration faces several unique challenges: (C1) images and textual descriptions are loosely paired intra-sample and inter-samples; (C2) images usually contain rich backgrounds that can mislead the prediction; (C3) weakly supervised labels from textual-established extractors are biased for multimodal training. We present PV2TEA, an encoder-decoder architecture equipped with three bias reduction schemes: (S1) Augmented label-smoothed contrast to improve the cross-modality alignment for loosely-paired image and text; (S2) Attention-pruning that adaptively distinguishes the visual foreground; (S3) Two-level neighborhood regularization that mitigates the label textual bias via reliability estimation. Empirical results on real-world e-Commerce datasets demonstrate up to 11.74% absolute (20.97% relatively) F1 increase over unimodal baselines.Comment: ACL 2023 Finding

Model-based analysis uncovers mutations altering autophagy selectivity in human cancer

Autophagy can selectively target protein aggregates, pathogens, and dysfunctional organelles for the lysosomal degradation. Aberrant regulation of autophagy promotes tumorigenesis, while it is far less clear whether and how tumor-specific alterations result in autophagic aberrance. To form a link between aberrant autophagy selectivity and human cancer, we establish a computational pipeline and prioritize 222 potential LIR (LC3-interacting region) motif-associated mutations (LAMs) in 148 proteins. We validate LAMs in multiple proteins including ATG4B, STBD1, EHMT2 and BRAF that impair their interactions with LC3 and autophagy activities. Using a combination of transcriptomic, metabolomic and additional experimental assays, we show that STBD1, a poorly-characterized protein, inhibits tumor growth via modulating glycogen autophagy, while a patient-derived W203C mutation on LIR abolishes its cancer inhibitory function. This work suggests that altered autophagy selectivity is a frequently-used mechanism by cancer cells to survive during various stresses, and provides a framework to discover additional autophagy-related pathways that influence carcinogenesis

Radiometric Correction of Multispectral Field Images Captured under Changing Ambient Light Conditions and Applications in Crop Monitoring

Applications of unmanned aerial vehicle (UAV) spectral systems in precision agriculture require raw image data to be converted to reflectance to produce time-consistent, atmosphere-independent images. Complex light environments, such as those caused by varying weather conditions, affect the accuracy of reflectance conversion. An experiment was conducted here to compare the accuracy of several target radiance correction methods, namely pre-calibration reference panel (pre-CRP), downwelling light sensor (DLS), and a novel method, real-time reflectance calibration reference panel (real-time CRP), in monitoring crop reflectance under variable weather conditions. Real-time CRP used simultaneous acquisition of target and CRP images and immediate correction of each image. These methods were validated with manually collected maize indictors. The results showed that real-time CRP had more robust stability and accuracy than DLS and pre-CRP under various conditions. Validation with maize data showed that the correlation between aboveground biomass and vegetation indices had the least variation under different light conditions (correlation all around 0.74), whereas leaf area index (correlation from 0.89 in sunny conditions to 0.82 in cloudy days) and canopy chlorophyll content (correlation from 0.74 in sunny conditions to 0.67 in cloudy days) had higher variation. The values of vegetation indices TVI and EVI varied little, and the model slopes of NDVI, OSAVI, MSR, RVI, NDRE, and CI with manually measured maize indicators were essentially constant under different weather conditions. These results serve as a reference for the application of UAV remote sensing technology in precision agriculture and accurate acquisition of crop phenotype data

Identification of FOXP1 as a favorable prognostic biomarker and tumor suppressor in intrahepatic cholangiocarcinoma

Abstract Background Forkhead-box protein P1 (FOXP1) has been proposed to have both oncogenic and tumor-suppressive properties, depending on tumor heterogeneity. However, the role of FOXP1 in intrahepatic cholangiocarcinoma (ICC) has not been previously reported. Methods Immunohistochemistry was performed to detect FOXP1 expression in ICC and normal liver tissues. The relationship between FOXP1 levels and the clinicopathological characteristics of patients with ICC was evaluated. Finally, in vitro and in vivo experiments were conducted to examine the regulatory role of FOXP1 in ICC cells. Results FOXP1 was significantly downregulated in the ICC compared to their peritumoral tissues (p < 0.01). The positive rates of FOXP1 were significantly lower in patients with poor differentiation, lymph node metastasis, invasion into surrounding organs, and advanced stages (p < 0.05). Notably, patients with FOXP1 positivity had better outcomes (overall survival) than those with FOXP1 negativity (p < 0.05), as revealed by Kaplan–Meier survival analysis. Moreover, Cox multivariate analysis showed that negative FOXP1 expression, advanced TNM stages, invasion, and lymph node metastasis were independent prognostic risk factors in patients with ICC. Lastly, overexpression of FOXP1 inhibited the proliferation, migration, and invasion of ICC cells and promoted apoptosis, whereas knockdown of FOXP1 had the opposite role. Conclusion Our findings suggest that FOXP1 may serve as a novel outcome predictor for ICC as well as a tumor suppressor that may contribute to cancer treatment

Airborne Microplastic Concentrations in Five Megacities of Northern and Southeast China

Airborne microplastics (MPs) are receiving increasing attention due to their ubiquitous nature and the potential human health consequences resulting from inhalation. The limited data for airborne MP concentrations vary widely among studies (∼4 orders of magnitude), but comparisons are tenuous due to the inconsistent collection and detection/enumeration methodologies among studies. Herein, we used uniform methodologies to obtain comparable airborne MP concentration data to assess MP exposure intensity in five Chinese megacities. Airborne MP concentrations in northern cities (358 ± 132 items/m3) were higher than those in southeast cities (230 ± 94 items/m3) but of a similar order of magnitude, unlike previous studies. The majority (94.7%) of MPs found in air samples were smaller than 100 μm, and the main shape of airborne MPs was fragments (88.2%). Polyethylene, polyester, and polystyrene were the dominant polymers comprising airborne MPs. No consistent relationships were detected between airborne MP concentration and typical socioeconomic indices, and the spatial and diurnal patterns for airborne MPs were different from various components of air quality indices (PM2.5, PM10, etc.). These findings reflect the contrasting source/generation dynamics between airborne MPs and other airborne pollutants. Maximum annual exposure of humans to airborne MPs was estimated in the range of 1-2 million/year in these megacities, highlighting the need for additional research examining the human health risks from the inhalation of airborne MPs

Microplastic ingestion from atmospheric deposition during dining/drinking activities.

Human-health risks from microplastics have attracted considerable attention, but little is known about human-exposure pathways and intensities. Recent studies posited that inhalation of atmospheric microplastics was the dominant human-exposure pathway. Herein, our study identified that atmospheric microplastics ingested from deposition during routine dining/drinking activities represent another important exposure pathway. We measured abundances of atmospheric-deposited microplastics of up to 105 items m-2 d-1 in dining/drinking venues, with 90% smaller than 100&nbsp;µm and a dominance of amorphous fragments rather than fibers. Typical work-life scenarios projected an annual ingestion of 1.9&nbsp;×&nbsp;105 to 1.3&nbsp;×&nbsp;106 microplastics through atmospheric deposition on diet, with higher exposure rates for indoor versus outdoor dining/drinking settings. Ingestion of atmospheric-deposited microplastics through diet was similar in magnitude to presumed inhalation exposure, but 2-3 orders of magnitude greater than direct ingestion from food sources. Simple mitigation strategies (e.g., covering and rinsing dishware) can substantially reduce the exposure of atmospheric deposition microplastics through diet

The crosstalk between cell death and pregnancy related diseases: A narrative review

Programmed cell death is intricately linked to various physiological phenomena such as growth, development, and metabolism, as well as the proper function of the pancreatic β cell and the migration and invasion of trophoblast cells in the placenta during pregnancy. Traditional and recently identified programmed cell death include apoptosis, autophagy, pyroptosis, necroptosis, and ferroptosis. In addition to cancer and degenerative diseases, abnormal activation of cell death has also been implicated in pregnancy related diseases like preeclampsia, gestational diabetes mellitus, intrahepatic cholestasis of pregnancy, fetal growth restriction, and recurrent miscarriage. Excessive or insufficient cell death and pregnancy related diseases may be mutually determined, ultimately resulting in adverse pregnancy outcomes. In this review, we systematically describe the characteristics and mechanisms underlying several types of cell death and their roles in pregnancy related diseases. Moreover, we discuss potential therapeutic strategies that target cell death signaling pathways for pregnancy related diseases, hoping that more meaningful treatments will be applied in clinical practice in the future