Improving Text Matching in E-Commerce Search with A Rationalizable, Intervenable and Fast Entity-Based Relevance Model

Discovering the intended items of user queries from a massive repository of items is one of the main goals of an e-commerce search system. Relevance prediction is essential to the search system since it helps improve performance. When online serving a relevance model, the model is required to perform fast and accurate inference. Currently, the widely used models such as Bi-encoder and Cross-encoder have their limitations in accuracy or inference speed respectively. In this work, we propose a novel model called the Entity-Based Relevance Model (EBRM). We identify the entities contained in an item and decompose the QI (query-item) relevance problem into multiple QE (query-entity) relevance problems; we then aggregate their results to form the QI prediction using a soft logic formulation. The decomposition allows us to use a Cross-encoder QE relevance module for high accuracy as well as cache QE predictions for fast online inference. Utilizing soft logic makes the prediction procedure interpretable and intervenable. We also show that pretraining the QE module with auto-generated QE data from user logs can further improve the overall performance. The proposed method is evaluated on labeled data from e-commerce websites. Empirical results show that it achieves promising improvements with computation efficiency

Global Retinoblastoma Presentation and Analysis by National Income Level.

Importance: Early diagnosis of retinoblastoma, the most common intraocular cancer, can save both a child's life and vision. However, anecdotal evidence suggests that many children across the world are diagnosed late. To our knowledge, the clinical presentation of retinoblastoma has never been assessed on a global scale. Objectives: To report the retinoblastoma stage at diagnosis in patients across the world during a single year, to investigate associations between clinical variables and national income level, and to investigate risk factors for advanced disease at diagnosis. Design, Setting, and Participants: A total of 278 retinoblastoma treatment centers were recruited from June 2017 through December 2018 to participate in a cross-sectional analysis of treatment-naive patients with retinoblastoma who were diagnosed in 2017. Main Outcomes and Measures: Age at presentation, proportion of familial history of retinoblastoma, and tumor stage and metastasis. Results: The cohort included 4351 new patients from 153 countries; the median age at diagnosis was 30.5 (interquartile range, 18.3-45.9) months, and 1976 patients (45.4%) were female. Most patients (n = 3685 [84.7%]) were from low- and middle-income countries (LMICs). Globally, the most common indication for referral was leukocoria (n = 2638 [62.8%]), followed by strabismus (n = 429 [10.2%]) and proptosis (n = 309 [7.4%]). Patients from high-income countries (HICs) were diagnosed at a median age of 14.1 months, with 656 of 666 (98.5%) patients having intraocular retinoblastoma and 2 (0.3%) having metastasis. Patients from low-income countries were diagnosed at a median age of 30.5 months, with 256 of 521 (49.1%) having extraocular retinoblastoma and 94 of 498 (18.9%) having metastasis. Lower national income level was associated with older presentation age, higher proportion of locally advanced disease and distant metastasis, and smaller proportion of familial history of retinoblastoma. Advanced disease at diagnosis was more common in LMICs even after adjusting for age (odds ratio for low-income countries vs upper-middle-income countries and HICs, 17.92 [95% CI, 12.94-24.80], and for lower-middle-income countries vs upper-middle-income countries and HICs, 5.74 [95% CI, 4.30-7.68]). Conclusions and Relevance: This study is estimated to have included more than half of all new retinoblastoma cases worldwide in 2017. Children from LMICs, where the main global retinoblastoma burden lies, presented at an older age with more advanced disease and demonstrated a smaller proportion of familial history of retinoblastoma, likely because many do not reach a childbearing age. Given that retinoblastoma is curable, these data are concerning and mandate intervention at national and international levels. Further studies are needed to investigate factors, other than age at presentation, that may be associated with advanced disease in LMICs

Retrieval of Chlorophyll a Concentration Using GOCI Data in Sediment-Laden Turbid Waters of Hangzhou Bay and Adjacent Coastal Waters

The Geostationary Ocean Color Imager (GOCI) provided images at hourly intervals up to 8 times per day with a spatial resolution of 500 m from 2011 to 2021. However, in the typical sediment-laden turbid water of Hangzhou Bay, valid ocean color parameters in operational data products have been extensively missing due to failures in atmospheric correction (AC) and bio-optical retrieval procedures. In this study, the seasonal variations in chlorophyll a (Chl-a) concentrations in Hangzhou Bay derived using GOCI data in 2020 were presented. First, valid remote sensing reflectance data were obtained by transferring neighboring aerosol properties of less to more turbid water pixels. Then, we improved a regionally empirical Chl-a retrieval algorithm in extremely turbid waters using GOCI-derived surface reflectance and field Chl-a measurements and proposed a combined Chl-a retrieval scheme for both moderately and extremely turbid water in Hangzhou Bay. Finally, the seasonal variation in Chl-a was obtained by the GOCI, which was better than operational products and in good agreement with the buoy data. The method in this study can be effectively applied to the inversion of Chl-a concentration in Hangzhou Bay and adjacent sea areas. We also presented its seasonal variations, offering insight into the spatial and seasonal variation of Chl-a in Hangzhou Bay using the GOCI

TSG-6 released from adipose stem cells-derived small extracellular vesicle protects against spinal cord ischemia reperfusion injury by inhibiting endoplasmic reticulum stress

Abstract Background Spinal cord ischemia reperfusion injury (SCIRI) is a complication of aortic aneurysm repair or spinal cord surgery that is associated with permanent neurological deficits. Mesenchymal stem cell (MSC)-derived small extracellular vesicles (sEVs) have been shown to be potential therapeutic options for improving motor functions after SCIRI. Due to their easy access and multi-directional differentiation potential, adipose‐derived stem cells (ADSCs) are preferable for this application. However, the effects of ADSC-derived sEVs (ADSC-sEVs) on SCIRI have not been reported. Results We found that ADSC-sEVs inhibited SCIRI-induced neuronal apoptosis, degradation of tight junction proteins and suppressed endoplasmic reticulum (ER) stress. However, in the presence of the ER stress inducer, tunicamycin, its anti-apoptotic and blood–spinal cord barrier (BSCB) protective effects were significantly reversed. We found that ADSC-sEVs contain tumor necrosis factor (TNF)-stimulated gene-6 (TSG-6) whose overexpression inhibited ER stress in vivo by modulating the PI3K/AKT pathway. Conclusions ADSC-sEVs inhibit neuronal apoptosis and BSCB disruption in SCIRI by transmitting TSG-6, which suppresses ER stress by modulating the PI3K/AKT pathway

An ultrathin rechargeable solid-state zinc ion fiber battery for electronic textiles.

Electronic textiles (e-textiles), having the capability of interacting with the human body and surroundings, are changing our everyday life in fundamental and meaningful ways. Yet, the expansion of the field of e-textiles is still limited by the lack of stable and biocompatible power sources with aesthetic designs. Here, we report a rechargeable solid-state Zn/MnO2 fiber battery with stable cyclic performance exceeding 500 hours while maintaining 98.0% capacity after more than 1000 charging/recharging cycles. The mechanism of the high electrical and mechanical performance due to the graphene oxide–embedded polyvinyl alcohol hydrogel electrolytes was rationalized by Monte Carlo simulation and finite element analysis. With a collection of key features including thin, light weight, economic, and biocompatible as well as high energy density, the Zn/MnO2 fiber battery could seamlessly be integrated into a multifunctional on-body e-textile, which provides a stable power unit for continuous and simultaneous heart rate, temperature, humidity, and altitude monitoring

An ultrathin rechargeable solid-state zinc ion fiber battery for electronic textiles

Electronic textiles (e-textiles), having the capability of interacting with the human body and surroundings, are changing our everyday life in fundamental and meaningful ways. Yet, the expansion of the field of e-textiles is still limited by the lack of stable and biocompatible power sources with aesthetic designs. Here, we report a rechargeable solid-state Zn/MnO2 fiber battery with stable cyclic performance exceeding 500 hours while maintaining 98.0% capacity after more than 1000 charging/recharging cycles. The mechanism of the high electrical and mechanical performance due to the graphene oxide–embedded polyvinyl alcohol hydrogel electrolytes was rationalized by Monte Carlo simulation and finite element analysis. With a collection of key features including thin, light weight, economic, and biocompatible as well as high energy density, the Zn/MnO2 fiber battery could seamlessly be integrated into a multifunctional on-body e-textile, which provides a stable power unit for continuous and simultaneous heart rate, temperature, humidity, and altitude monitoring

Tracing the Flu Symptom Progression via a Smart Face Mask

Respiration and body temperature are largely influenced by the highly contagious influenza virus, which poses persistent global public health challenges. Here, we present a wireless all-in-one sensory face mask (WISE mask) made of ultrasensitive fibrous temperature sensors. The WISE mask shows exceptional thermosensitivity, excellent breathability, and wearing comfort. It offers highly sensitive body temperature monitoring and respiratory detection capabilities. Capitalizing on the advances in the Internet of Things and artificial intelligence, the WISE mask is further demonstrated by customized flexible circuitry, deep learning algorithms, and a user-friendly interface to continuously recognize the abnormalities of both the respiration and body temperature. The WISE mask represents a compelling approach to tracing flu symptom progression in a cost-effective and convenient manner, serving as a powerful solution for personalized health monitoring and point-of-care systems in the face of ongoing influenza-related public health concerns

Tracing the Flu Symptom Progression via a Smart Face Mask

Respiration and body temperature are largely influenced by the highly contagious influenza virus, which poses persistent global public health challenges. Here, we present a wireless all-in-one sensory face mask (WISE mask) made of ultrasensitive fibrous temperature sensors. The WISE mask shows exceptional thermosensitivity, excellent breathability, and wearing comfort. It offers highly sensitive body temperature monitoring and respiratory detection capabilities. Capitalizing on the advances in the Internet of Things and artificial intelligence, the WISE mask is further demonstrated by customized flexible circuitry, deep learning algorithms, and a user-friendly interface to continuously recognize the abnormalities of both the respiration and body temperature. The WISE mask represents a compelling approach to tracing flu symptom progression in a cost-effective and convenient manner, serving as a powerful solution for personalized health monitoring and point-of-care systems in the face of ongoing influenza-related public health concerns