Item-Graph2vec: a Efficient and Effective Approach using Item Co-occurrence Graph Embedding for Collaborative Filtering

Current item-item collaborative filtering algorithms based on artificial neural network, such as Item2vec, have become ubiquitous and are widely applied in the modern recommender system. However, these approaches do not apply to the large-scale item-based recommendation system because of their extremely long training time. To overcome the shortcoming that current algorithms have high training time costs and poor stability when dealing with large-scale data sets, the item graph embedding algorithm Item-Graph2vec is described here. This algorithm transforms the users' shopping list into a item co-occurrence graph, obtains item sequences through randomly travelling on this co-occurrence graph and finally trains item vectors through sequence samples. We posit that because of the stable size of item, the size and density of the item co-occurrence graph change slightly with the increase in the training corpus. Therefore, Item-Graph2vec has a stable runtime on the large scale data set, and its performance advantage becomes more and more obvious with the growth of the training corpus. Extensive experiments conducted on real-world data sets demonstrate that Item-Graph2vec outperforms Item2vec by 3 times in terms of efficiency on douban data set, while the error generated by the random walk sampling is small

Towards Best Practice of Interpreting Deep Learning Models for EEG-based Brain Computer Interfaces

As deep learning has achieved state-of-the-art performance for many tasks of EEG-based BCI, many efforts have been made in recent years trying to understand what have been learned by the models. This is commonly done by generating a heatmap indicating to which extent each pixel of the input contributes to the final classification for a trained model. Despite the wide use, it is not yet understood to which extent the obtained interpretation results can be trusted and how accurate they can reflect the model decisions. In order to fill this research gap, we conduct a study to evaluate different deep interpretation techniques quantitatively on EEG datasets. The results reveal the importance of selecting a proper interpretation technique as the initial step. In addition, we also find that the quality of the interpretation results is inconsistent for individual samples despite when a method with an overall good performance is used. Many factors, including model structure and dataset types, could potentially affect the quality of the interpretation results. Based on the observations, we propose a set of procedures that allow the interpretation results to be presented in an understandable and trusted way. We illustrate the usefulness of our method for EEG-based BCI with instances selected from different scenarios

14-Year Outcome of Angle-Closure Prevention with Laser Iridotomy in the Zhongshan Angle Closure Prevention Study: Extended Follow-Up of a Randomized Controlled Trial

Purpose: This study aimed to evaluate the efficacy of laser peripheral iridotomy (LPI) prophylaxis for primary angle closure suspects (PACS) after 14 years and to identify risk factors for the conversion from PACS to primary angle closure (PAC)./ Design: An extended follow-up of Zhongshan Angle Closure Prevention (ZAP) study./ Participants: A total of 889 Chinese patients aged 50 to 70 years with bilateral PACS./ Methods: Each patient received LPI in one randomly selected eye, with the fellow untreated eye serving as a control. Since the risk of glaucoma was low and acute angle closure (AAC) only occurred in rare cases, the follow-up was extended to 14 years despite substantial benefits of LPI reported after the 6-year visit./ Main Outcome Measures: The primary outcome was incidence of PAC, a composite endpoint including peripheral anterior synechiae (PAS), intraocular pressure (IOP) > 24 mmHg, or AAC. Results During the 14 years, 390 LPI-treated eyes and 388 control eyes were lost to the follow-up. A total of 33 LPI-treated eyes and 105 control eyes reached primary endpoints (P <0.01). Within them, twelve eyes developed AAC or primary angle closure glaucoma (AAC: five control eyes and one LPI-treated eye; PACG: four control eyes and two LPI-treated eyes). The hazard ratio for progression to PAC was 0.31 (95% confidence interval, 0.21–0.46) in LPI-treated eyes compared with control eyes. At the 14-year visit, LPI-treated eyes had severer nuclear cataract, higher IOP, larger angle width and limbal anterior chamber depth (LACD) than control eyes. Higher IOP, shallower LACD, and central anterior chamber depth (CACD) were associated with an increased risk of developing endpoints in control eyes. In the treated group, eyes with higher IOP, shallower LACD, or less IOP elevation after dark room–prone provocative tests (DRPPT) were more likely to develop PAC after LPI./ Conclusions: Despite a two-third decrease in PAC incidence after LPI, the cumulative risk of PAC was relatively low in the community-based PACS population over 14 years. Apart from IOP, IOP elevation after DRPPT, CACD, and LACD, more risk factors are needed to achieve precise prediction of PAC occurrence and guide clinical practice

MSIsensor-ct: Microsatellite instability detection using cfDNA sequencing data

MOTIVATION: Microsatellite instability (MSI) is a promising biomarker for cancer prognosis and chemosensitivity. Techniques are rapidly evolving for the detection of MSI from tumor-normal paired or tumor-only sequencing data. However, tumor tissues are often insufficient, unavailable, or otherwise difficult to procure. Increasing clinical evidence indicates the enormous potential of plasma circulating cell-free DNA (cfNDA) technology as a noninvasive MSI detection approach. RESULTS: We developed MSIsensor-ct, a bioinformatics tool based on a machine learning protocol, dedicated to detecting MSI status using cfDNA sequencing data with a potential stable MSIscore threshold of 20%. Evaluation of MSIsensor-ct on independent testing datasets with various levels of circulating tumor DNA (ctDNA) and sequencing depth showed 100% accuracy within the limit of detection (LOD) of 0.05% ctDNA content. MSIsensor-ct requires only BAM files as input, rendering it user-friendly and readily integrated into next generation sequencing (NGS) analysis pipelines. AVAILABILITY: MSIsensor-ct is freely available at https://github.com/niu-lab/MSIsensor-ct. SUPPLEMENTARY INFORMATION: Supplementary data are available at Briefings in Bioinformatics online

Synergistic antitumor activity of regorafenib and rosuvastatin in colorectal cancer

Introduction: Colorectal cancer is one of the most prevalent life-threatening malignant tumors with high incidence and mortality. However, the efficacy of current therapeutic regimens is very limited. Regorafenib has been approved for second- or third-line treatment of patients who are refractory to standard chemotherapy diagnosed with metastatic colorectal cancer, but its clinical efficacy needs to be further improved. Accumulating evidence demonstrates that statins also possess potent anticancer activities. However, whether regorafenib and statins pose synergistic anticancer effects in colorectal cancer is still unclear.Methods: Sulforhodamine B (SRB) assays were applied to evaluate the anti-proliferative activity of regorafenib or/and rosuvastatin in vitro, and immunoblotting analysis were applied to detect the effects of regorafenib/rosuvastatin combined treatment on mitogen-activated protein kinase (MAPK) signaling and apoptosis-related proteins. MC38 tumors were applied to investigate the synergistic anticancer effects of regorafenib in combination with rosuvastatin in vivo.Results: We found that regorafenib in combination with rosuvastatin exerted significant synergistic inhibition against colorectal cancer growth in vitro and in vivo. Mechanistically, regorafenib and rosuvastatin combination synergistically suppressed MAPK signaling, a crucial signaling pathway promoting cell survival, as indicated by the reduction of phosphorylated MEK/ERK. In addition, regorafenib in combination with rosuvastatin synergistically induced the apoptosis of colorectal cancer in vitro and in vivo.Discussion: Our study demonstrated the synergistic anti-proliferative and pro-apoptotic effects of regorafenib/rosuvastatin combined treatment in colorectal cancer in vitro/vivo and might potentially be evaluated as a novel combination regimen for clinical treatment of colorectal cancer

Study on the Influencing Factors of Energy Consumption of Nearly Zero Energy Residential Buildings in Cold and Arid Regions of Northwest China

There are many factors influencing the energy consumption of buildings in complex working conditions. In order to study the factors influencing the energy consumption of residential buildings with nearly zero energy in cold and arid regions of northwest China, factors such as the roof heat transfer coefficient (KR), exterior wall heat transfer coefficient (KE), ground heat transfer coefficient (KG), exterior window heat transfer coefficient (KEW), north window wall ratio (WWRN), south window wall ratio (WWRS), east west window wall ratio (WWRWE), building orientation (BO), and ventilation times (VT) are taken as the influencing factors in this paper. Using the orthogonal test, 135 building energy consumption calculation models were built in DeST, and the influence of 9 factors on building energy consumption in 5 types of regions (severe cold region A (1A), severe cold region B (1B), severe cold region C (1C), cold region A (2A), and cold region B (2B)) were analyzed. The conclusions are as follows: in the process of realizing nearly zero energy of residential buildings in the cold and arid regions of northwest China, the KR, KE, KG, KEW, WWRN, WWEWE should be reduced as much as possible in the five regions. The 1A,1B,1C regions should increase WWEWE and VT, with BO of about 15° east of due north and VT of about 5, 8, and 10 times per hour, respectively. The WWES, BO and VT for the 2A region should be set at round 0.45, north-south, and about 10 times per hour, respectively. For the 2B region, WWES should be set at around 0.45, BO around 15° east of due north, and VT as low as possible within the scope of the ‘technical standard for nearly zero energy buildings’

An enhanced ensemble deep random vector functional link network for driver fatigue recognition

This work investigated the use of an ensemble deep random vector functional link (edRVFL) network for electroencephalogram (EEG)-based driver fatigue recognition. Against the low feature learning capability of the edRVFL network from raw EEG signals, two strategies were exploited in this work. Specifically, the first one was to exploit the advantages of the feature extractor module in CNNs, i.e., use CNN features as the input of the edRVFL network. The second one was to improve the feature learning capability of the edRVFL network. An enhanced edRFVL network named FGloWD-edRVFL was proposed, in which four enhancements were implemented, including random forest-based Feature selection, Global output layer, Weighting and entropy-based Dynamic ensemble. The proposed FGloWD-edRVFL network was evaluated on the challenging cross-subject driver fatigue recognition tasks. The results indicated that the proposed model could boost the recognition performance, significantly outperforming all strong baselines. The step-wise analysis further demonstrated the effectiveness of the proposed enhancements in the edRVFL network. 2023 The Author(s)Open Access funding provided by the Qatar National Library.Scopu