LightSAGE: Graph Neural Networks for Large Scale Item Retrieval in Shopee's Advertisement Recommendation

Graph Neural Network (GNN) is the trending solution for item retrieval in recommendation problems. Most recent reports, however, focus heavily on new model architectures. This may bring some gaps when applying GNN in the industrial setup, where, besides the model, constructing the graph and handling data sparsity also play critical roles in the overall success of the project. In this work, we report how GNN is applied for large-scale e-commerce item retrieval at Shopee. We introduce our simple yet novel and impactful techniques in graph construction, modeling, and handling data skewness. Specifically, we construct high-quality item graphs by combining strong-signal user behaviors with high-precision collaborative filtering (CF) algorithm. We then develop a new GNN architecture named LightSAGE to produce high-quality items' embeddings for vector search. Finally, we design multiple strategies to handle cold-start and long-tail items, which are critical in an advertisement (ads) system. Our models bring improvement in offline evaluations, online A/B tests, and are deployed to the main traffic of Shopee's Recommendation Advertisement system

TaskMatrix.AI: Completing Tasks by Connecting Foundation Models with Millions of APIs

Artificial Intelligence (AI) has made incredible progress recently. On the one hand, advanced foundation models like ChatGPT can offer powerful conversation, in-context learning and code generation abilities on a broad range of open-domain tasks. They can also generate high-level solution outlines for domain-specific tasks based on the common sense knowledge they have acquired. However, they still face difficulties with some specialized tasks because they lack enough domain-specific data during pre-training or they often have errors in their neural network computations on those tasks that need accurate executions. On the other hand, there are also many existing models and systems (symbolic-based or neural-based) that can do some domain-specific tasks very well. However, due to the different implementation or working mechanisms, they are not easily accessible or compatible with foundation models. Therefore, there is a clear and pressing need for a mechanism that can leverage foundation models to propose task solution outlines and then automatically match some of the sub-tasks in the outlines to the off-the-shelf models and systems with special functionalities to complete them. Inspired by this, we introduce TaskMatrix.AI as a new AI ecosystem that connects foundation models with millions of APIs for task completion. Unlike most previous work that aimed to improve a single AI model, TaskMatrix.AI focuses more on using existing foundation models (as a brain-like central system) and APIs of other AI models and systems (as sub-task solvers) to achieve diversified tasks in both digital and physical domains. As a position paper, we will present our vision of how to build such an ecosystem, explain each key component, and use study cases to illustrate both the feasibility of this vision and the main challenges we need to address next

Robust estimation of bacterial cell count from optical density

Optical density (OD) is widely used to estimate the density of cells in liquid culture, but cannot be compared between instruments without a standardized calibration protocol and is challenging to relate to actual cell count. We address this with an interlaboratory study comparing three simple, low-cost, and highly accessible OD calibration protocols across 244 laboratories, applied to eight strains of constitutive GFP-expressing E. coli. Based on our results, we recommend calibrating OD to estimated cell count using serial dilution of silica microspheres, which produces highly precise calibration (95.5% of residuals <1.2-fold), is easily assessed for quality control, also assesses instrument effective linear range, and can be combined with fluorescence calibration to obtain units of Molecules of Equivalent Fluorescein (MEFL) per cell, allowing direct comparison and data fusion with flow cytometry measurements: in our study, fluorescence per cell measurements showed only a 1.07-fold mean difference between plate reader and flow cytometry data

Fluid mobility calculation based on improved window parameters optimized S-transform for reservoir delineation

Time-frequency analysis is an important tool for seismic signal analysis and time-frequency resolution is the key to high-precision reservoir prediction. The time-frequency method of conventional S-transform is difficult to meet the requirements of high precision reservoir prediction at present. In this paper, an improved window-parameter optimization S-transform (IWPOST) method is proposed. In the IWPOST method, the scale parameters of the window function can be adaptively obtained according to the amplitude spectrum of the actual signal, and a new optimization adjustment parameter is introduced to further improve the window parameters. The comparison analysis of the synthetic signals shows that the IWPOST method has better time-frequency aggregation and can maintain high resolution at both high-frequency and low-frequency. Finally, the proposed method is used to extract the reservoir fluid mobility attribute from the real seismic data and the results indicate that the fluid mobility has a higher time resolution and exhibits better performance for reservoir delineation. The proposed method is conducive to delineate the reservoir range and provides reference for the exploration and development of the oil and gas reservoirs in this area

Neratinib for HER2-positive breast cancer with an overlooked option

Abstract Positive human epidermal growth factor receptor 2 (HER2) expression is associated with an increased risk of metastases especially those to the brain in patients with advanced breast cancer (BC). Neratinib as a tyrosine kinase inhibitor can prevent the transduction of HER1, HER2 and HER4 signaling pathways thus playing an anticancer effect. Moreover, neratinib has a certain efficacy to reverse drug resistance in patients with BC with previous HER2 monoclonal antibody or targeted drug resistance. Neratinib, as monotherapy and in combination with other therapies, has been tested in the neoadjuvant, adjuvant, and metastatic settings. Neratinib with high anticancer activity is indicated for the prolonged adjuvant treatment of HER2-positive early BC, or in combination with other drugs including trastuzumab, capecitabine, and paclitaxel for the treatment of advanced HER2-positive BC especially cancers with central nervous system (CNS) metastasis to reduce the risk of BC recurrence. This article reviewed the pharmacological profiles, efficacy, safety, tolerability, and current clinical trials pertaining to neratinib, with a particular focus on the use of neratinib in patients with metastatic breast cancer (MBC) involving the CNS. We further discussed the use of neratinib for HER2-negative and HER2-mutant breast cancers, and mechanisms of resistance to neratinib. The current evidence suggests that neratinib has promising efficacy in patients with BC which is at least non-inferior compared to previous therapeutic regimens. The most common AE was diarrhea, and the incidence, severity and duration of neratinib-related grade 3 diarrhea can be reduced with loperamide. Of note, neratinib has the potential to effectively control and prevent brain metastasis in patients with advanced BC, providing a therapeutic strategy for HER2-positive BC

Impact of High Lipoprotein(a) on Long‐Term Survival Following Coronary Artery Bypass Grafting

Background Lipoprotein(a) is a possible causal risk factor for atherosclerosis and related complications. The distribution and prognostic implication of lipoprotein(a) in patients undergoing coronary artery bypass grafting remain unknown. This study aimed to assess the impact of high lipoprotein(a) on the long‐term prognosis of patients undergoing coronary artery bypass grafting. Methods and Results Consecutive patients with stable coronary artery disease who underwent isolated coronary artery bypass grafting from January 2013 to December 2018 from a single‐center cohort were included. The primary outcome was all‐cause death. The secondary outcome was a composite of major adverse cardiovascular and cerebrovascular events. Of the 18 544 patients, 4072 (22.0%) were identified as the high‐lipoprotein(a) group (≥50 mg/dL). During a median follow‐up of 3.2 years, primary outcomes occurred in 587 patients. High lipoprotein(a) was associated with increased risk of all‐cause death (high lipoprotein(a) versus low lipoprotein(a): adjusted hazard ratio [aHR], 1.31 [95% CI, 1.09–1.59]; P=0.005; lipoprotein(a) per 1‐mg/dL increase: aHR, 1.003 [95% CI, 1.001–1.006]; P=0.011) and major adverse cardiovascular and cerebrovascular events (high lipoprotein(a) versus low lipoprotein(a): aHR, 1.18 [95% CI, 1.06–1.33]; P=0.004; lipoprotein(a) per 1‐mg/dL increase: aHR, 1.002 [95% CI, 1.001–1.004]; P=0.002). The lipoprotein(a)‐related risk was greater in patients with European System for Cardiac Operative Risk Evaluation <3, and tended to attenuate in patients receiving arterial grafts. Conclusions More than 1 in 5 patients with stable coronary artery disease who underwent coronary artery bypass grafting were exposed to high lipoprotein(a), which is associated with higher risks of death and major adverse cardiovascular and cerebrovascular events. The adverse effects of lipoprotein(a) were more pronounced in patients with clinically low‐risk profiles or not receiving arterial grafts

P21-activated kinase 7 mediates cisplatin-resistance of esophageal squamous carcinoma cells with Aurora-A overexpression.

Aurora-A overexpression is common in various types of cancers and has been shown to be involved in tumorigenesis through different signaling pathways, yet how the deregulation affects cancer therapeutics remains elusive. Here we showed that overexpression of Aurora-A rendered esophageal cancer cells resistance to cisplatin (CDDP) by inhibiting apoptosis. By using an apoptosis array, we identified a downstream gene, p21-activated kinase 7 (PAK7). PAK7 was upregulated by Aurora-A overexpression at both mRNA and protein levels. Importantly, the expression levels of Aurora-A and PAK7 were correlated in ESCC primary samples. Chromatin immunoprecipitation (ChIP) assay revealed that binding of E2F1 to the promoter of PAK7 was significantly enhanced upon Aurora-A activation, and knockdown of transcription factor E2F1 decreased PAK7 expression, suggesting that Aurora-A regulated PAK7 through E2F1. Furthermore, we demonstrated that PAK7 knockdown led to increased apoptosis, and Aurora-A-induced resistance to CDDP was reversed by downregulation of PAK7, suggesting PAK7 was a downstream player of Aurora-A that mediated chemoresistance of ESCC cells to CDDP. Our data suggest that PAK7 may serve as an attractive candidate for therapeutics in ESCC patients with Aurora-A abnormality