NRPA: Neural Recommendation with Personalized Attention

Existing review-based recommendation methods usually use the same model to learn the representations of all users/items from reviews posted by users towards items. However, different users have different preference and different items have different characteristics. Thus, the same word or similar reviews may have different informativeness for different users and items. In this paper we propose a neural recommendation approach with personalized attention to learn personalized representations of users and items from reviews. We use a review encoder to learn representations of reviews from words, and a user/item encoder to learn representations of users or items from reviews. We propose a personalized attention model, and apply it to both review and user/item encoders to select different important words and reviews for different users/items. Experiments on five datasets validate our approach can effectively improve the performance of neural recommendation.Comment: 4 pages, 4 figure

PUNR: Pre-training with User Behavior Modeling for News Recommendation

News recommendation aims to predict click behaviors based on user behaviors. How to effectively model the user representations is the key to recommending preferred news. Existing works are mostly focused on improvements in the supervised fine-tuning stage. However, there is still a lack of PLM-based unsupervised pre-training methods optimized for user representations. In this work, we propose an unsupervised pre-training paradigm with two tasks, i.e. user behavior masking and user behavior generation, both towards effective user behavior modeling. Firstly, we introduce the user behavior masking pre-training task to recover the masked user behaviors based on their contextual behaviors. In this way, the model could capture a much stronger and more comprehensive user news reading pattern. Besides, we incorporate a novel auxiliary user behavior generation pre-training task to enhance the user representation vector derived from the user encoder. We use the above pre-trained user modeling encoder to obtain news and user representations in downstream fine-tuning. Evaluations on the real-world news benchmark show significant performance improvements over existing baselines.Comment: Accepted by Findings of EMNLP23. Github Repo: https://github.com/ma787639046/pun

Yield stability across sowing dates – how to pick a winner in variable seasons?

Take home messages • Match optimal flowering period to growing environment to maximise grain yield potential. • One variety doesn’t fit all; there are no commercially available varieties that are broadly adapted across a wide range of sowing times or growing environments. • Optimising variety phenology and sowing time combinations achieves grain yield stability across a wide sowing window. • Probability of sowing opportunities will influence variety choice and sowing time decisions

Baicalin-modified polyethylenimine for miR-34a efficient and safe delivery

The security and efficiency of gene delivery vectors are inseparable for the successful construction of a gene delivery vector. This work provides a practical method to construct a charge-regulated, hydrophobic-modified, and functionally modified polyethylenimine (PEI) with effective gene delivery and perfect transfection performance through a condensation reaction, named BA-PEI. The carrier was shown to possess a favorable compaction of miRNAs into positively charged nanoparticles with a hydrodynamic size of approximately 100 nm. Additionally, BA-PEI possesses perfect degradability, which benefits the release of miR-34a from the complexes. In A549 cells, the expression level of the miR-34a gene was checked by Western blotting, which reflects the transfection efficiency of BA-PEI/miR-34a. When miR-34a is delivered to the cell, the perfect anti-tumor ability of the BA-PEI/miR-34a complex was systematically evaluated with the suppressor tumor gene miR-34a system in vitro and in vivo. BA-PEI-mediated miR-34a gene transfection is more secure and effective than the commercial transfection reagent, thus providing a novel approach for miR-34a-based gene therapy

Crop Phenology Estimation in Rice Fields Using Sentinel-1 GRD SAR Data and Machine Learning-Aided Particle Filtering Approach

Monitoring crop phenology is essential for managing field disasters, protecting the environment, and making decisions about agricultural productivity. Because of its high timeliness, high resolution, great penetration, and sensitivity to specific structural elements, synthetic aperture radar (SAR) is a valuable technique for crop phenology estimation. Particle filtering (PF) belongs to the family of dynamical approach and has the ability to predict crop phenology with SAR data in real time. The observation equation is a key factor affecting the accuracy of particle filtering estimation and depends on fitting. Compared to the common polynomial fitting (POLY), machine learning methods can automatically learn features and handle complex data structures, offering greater flexibility and generalization capabilities. Therefore, incorporating two ensemble learning algorithms consisting of support vector machine regression (SVR), random forest regression (RFR), respectively, we proposed two machine learning-aided particle filtering approaches (PF-SVR, PF-RFR) to estimate crop phenology. One year of time-series Sentinel-1 GRD SAR data in 2017 covering rice fields in Sevilla region in Spain was used for establishing the observation and prediction equations, and the other year of data in 2018 was used for validating the prediction accuracy of PF methods. Four polarization features (VV, VH, VH/VV and Radar Vegetation Index (RVI)) were exploited as the observations in modeling. Experimental results reveals that the machine learning-aided methods are superior than the PF-POLY method. The PF-SVR exhibited better performance than the PF-RFR and PF-POLY methods. The optimal outcome from PF-SVR yielded a root-mean-square error (RMSE) of 7.79, compared to 7.94 for PF-RFR and 9.1 for PF-POLY. Moreover, the results suggest that the RVI is generally more sensitive than other features to crop phenology and the performance of polarization features presented consistent among all methods, i.e., RVI＞VV＞VH＞VH/VV. Our findings offer valuable references for real-time crop phenology monitoring with SAR data

CDCA2 Inhibits Apoptosis and Promotes Cell Proliferation in Prostate Cancer and Is Directly Regulated by HIF-1α Pathway.

Prostate cancer (PCa) is a major serious malignant tumor and is commonly diagnosed in older men. Identification of novel cancer-related genes in PCa is important for understanding its tumorigenesis mechanism and developing new therapies against PCa. Here, we used RNA sequencing to identify the specific genes, which are upregulated in PCa cell lines and tissues. The cell division cycle associated protein (CDCA) family, which plays a critical role in cell division and proliferation, is upregulated in the PCa cell lines of our RNA-Sequencing data. Moreover, we found that CDCA2 is overexpressed, and its protein level positively correlates with its histological grade, clinical stage, and Gleason Score. CDCA2 was further found to be upregulated and correlated with poor prognosis and patient survival in multiple cancer types in The Cancer Genome Atlas (TCGA) dataset. The functional study suggests that inhibition of CDCA2 will lead to apoptosis and lower proliferation in vitro. Silencing of CDCA2 also repressed tumor growth in vivo. Loss of CDCA2 affects several oncogenic pathways, including MAPK signaling. In addition, we further demonstrated that CDCA2 was induced in hypoxia and directly regulated by the HIF-1α/Smad3 complex. Thus, our data indicate that CDCA2 could act as an oncogene and is regulated by hypoxia and the HIF-1αpathway. CDCA2 may be a useful prognostic biomarker and potential therapeutic target for PCa