Deep Item-based Collaborative Filtering for Top-N Recommendation

Item-based Collaborative Filtering(short for ICF) has been widely adopted in recommender systems in industry, owing to its strength in user interest modeling and ease in online personalization. By constructing a user's profile with the items that the user has consumed, ICF recommends items that are similar to the user's profile. With the prevalence of machine learning in recent years, significant processes have been made for ICF by learning item similarity (or representation) from data. Nevertheless, we argue that most existing works have only considered linear and shallow relationship between items, which are insufficient to capture the complicated decision-making process of users. In this work, we propose a more expressive ICF solution by accounting for the nonlinear and higher-order relationship among items. Going beyond modeling only the second-order interaction (e.g. similarity) between two items, we additionally consider the interaction among all interacted item pairs by using nonlinear neural networks. Through this way, we can effectively model the higher-order relationship among items, capturing more complicated effects in user decision-making. For example, it can differentiate which historical itemsets in a user's profile are more important in affecting the user to make a purchase decision on an item. We treat this solution as a deep variant of ICF, thus term it as DeepICF. To justify our proposal, we perform empirical studies on two public datasets from MovieLens and Pinterest. Extensive experiments verify the highly positive effect of higher-order item interaction modeling with nonlinear neural networks. Moreover, we demonstrate that by more fine-grained second-order interaction modeling with attention network, the performance of our DeepICF method can be further improved.Comment: 25 pages, submitted to TOI

Research on the Demand Forecasting Method of Sichuan Social Logistics Based on Positive Weight Combination

The macro-social logistics demand forecast is of great strategic significance to optimize the national or regional economic structure, improve the investment environment and improve the overall competitiveness of regional economy. In this study, the total amount of social logistics in Sichuan province was selected to reflect the social logistics demand, the factors influencing the social logistics demand in Sichuan province were analyzed, and eight economic indicators were summarized. This study first USES the time series prediction model (including the time response model GM (1, 1)), an exponential smoothing model, causal relation model (including multidimensional prediction model GM (1, n) and BP neural network model), to build four methods combination model, weight given solution of linear programming each forecast model, the forecasting result of combination forecast model deviation is minimal. The posterior difference test was applied to the above five models to compare the prediction results of each prediction method

Detection of incoherent broadband terahertz light using antenna-coupled high-electron-mobility field-effect transistors

The sensitivity of direct terahertz detectors based on self-mixing of terahertz electromagnetic wave in field-effect transistors is being improved with noise-equivalent power close to that of Schottky-barrier-diode detectors. Here we report such detectors based on AlGaN/GaN two-dimensional electron gas at 77~K are able to sense broadband and incoherent terahertz radiation. The measured photocurrent as a function of the gate voltage agrees well with the self-mixing model and the spectral response is mainly determined by the antenna. A Fourier-transform spectrometer equipped with detectors designed for 340, 650 and 900~GHz bands allows for terahertz spectroscopy in a frequency range from 0.1 to 2.0~THz. The 900~GHz detector at 77~K offers an optical sensitivity about $1~\mathrm{pW/\sqrt{Hz}}$ being comparable to a commercial silicon bolometer at 4.2~K. By further improving the sensitivity, room-temperature detectors would find applications in active/passive terahertz imaging and terahertz spectroscopy.Comment: 4.5 pages, 5 figure

Cost analysis of improving emergency care for aged care residents under a Hospital in the Nursing Home program in Australia

Background This study aims to examine the costs associated with a Hospital in the Nursing Home (HiNH) program in Queensland Australia directed at patients from residential aged care facilities (RACFs) with emergency care needs. Methods A cost analysis was undertaken comparing the costs under the HiNH program and the current practice, in parallel with a pre-post controlled study design. The study was conducted in two Queensland public hospitals: the Royal Brisbane and Women’s Hospital (intervention hospital) and the Logan Hospital (control hospital). Main outcome measures were the associated incremental costs or savings concerning the HiNH program provision and the acute hospital care utilisation over one year after intervention. Results The initial deterministic analysis calculated the total induced mean costs associated with providing the HiNH program over one year as AU$488,116, and the total induced savings relating to acute hospital care service utilisation of AU$8,659,788. The total net costs to the health service providers were thus calculated at -AU$8,171,671 per annum. Results from the probabilistic sensitivity analysis (based on 10,000 simulations) showed the mean and median annual net costs associated with the HiNH program implementation were -AU$8,444,512 and–AU$8,202,676, and a standard deviation of 2,955,346. There was 95$15,018,055 to -AU$3,358,820. Conclusions The costs relating to implementing the HiNH program appear to be much less than the savings in terms of associated decreases in acute hospital service utilisation. The HiNH service model is likely to have the cost-saving potential while improving the emergency care provision for RACF residents

A five-collagen-based risk model in lung adenocarcinoma: prognostic significance and immune landscape

As part of the tumor microenvironment (TME), collagen plays a significant role in cancer fibrosis formation. However, the collagen family expression profile and clinical features in lung adenocarcinoma (LUAD) are poorly understood. The objective of the present work was to investigate the expression pattern of genes from the collagen family in LUAD and to develop a predictive signature based on collagen family. The Cancer Genome Atlas (TCGA) samples were used as the training set, and five additional cohort samples obtained from the Gene Expression Omnibus (GEO) database were used as the validation set. A predictive model based on five collagen genes, including COL1A1, COL4A3, COL5A1, COL11A1, and COL22A1, was created by analyzing samples from the TCGA cohort using LASSO Cox analysis and univariate/multivariable Cox regression. Using Collagen-Risk scores, LUAD patients were then divided into high- and low-risk groups. KM survival analysis showed that collagen signature presented a robust prognostic power. GO and KEGG analyses confirmed that collagen signature was associated with extracellular matrix organization, ECM-receptor interaction, PI3K-Akts and AGE-RAGE signaling activation. High-risk patients exhibited a considerable activation of the p53 pathway and cell cycle, according to GSEA analysis. The Collage-Risk model showed unique features in immune cell infiltration and tumor-associated macrophage (TAM) polarization of the TME. Additionally, we deeply revealed the association of collagen signature with immune checkpoints (ICPs), tumor mutation burden (TMB), and tumor purity. We first constructed a reliable prognostic model based on TME principal component—collagen, which would enable clinicians to treat patients with LUAD more individually

Genetic analysis of chikungunya viruses imported to mainland China in 2008

<p>Abstract</p> <p>Background</p> <p>Chikungunya virus (CHIKV) has caused large outbreaks worldwide in recent years, especially on the islands of the Indian Ocean and India. The virus is transmitted by mosquitoes (<it>Aedes aegypti</it>), which are widespread in China, with an especially high population density in southern China. Analyses of full-length viral sequences revealed the acquisition of a single adaptive mutation providing a selective advantage for the transmission of CHIKV by this species. No outbreaks due to the local transmission of CHIKV have been reported in China, and no cases of importation were detected on mainland China before 2008. We followed the spread of imported CHIKV in southern China and analyzed the genetic character of the detected viruses to evaluate their potential for evolution.</p> <p>Results</p> <p>The importation of CHIKV to mainland China was first detected in 2008. The genomic sequences of four of the imported viruses were identified, and phylogenetic analysis demonstrated that the sequences were clustered in the Indian Ocean group; however, seven amino acid changes were detected in the nonstructural protein-coding region, and five amino acid changes were noted in the structural protein-coding regions. In particular, a novel substitution in E2 was detected (K252Q), which may impact the neurovirulence of CHIKV. The adaptive mutation A226V in E1 was observed in two imported cases of chikungunya disease.</p> <p>Conclusions</p> <p>Laboratory-confirmed CHIKV infections among travelers visiting China in 2008 were presented, new mutations in the viral nucleic acids and proteins may represent adaptive mutations for human or mosquito hosts.</p

How does the recurrence-related morphology characteristics of the Pcom aneurysms correlated with hemodynamics?

IntroductionPosterior communicating artery (Pcom) aneurysm has unique morphological characteristics and a high recurrence risk after coil embolization. This study aimed to evaluate the relationship between the recurrence-related morphology characteristics and hemodynamics.MethodA total of 20 patients with 22 Pcom aneurysms from 2019 to 2022 were retrospectively enrolled. The recurrence-related morphology parameters were measured. The hemodynamic parameters were simulated based on finite element analysis and computational fluid dynamics. The hemodynamic differences before and after treatment caused by different morphological features and the correlation between these parameters were analyzed.ResultSignificant greater postoperative inflow rate at the neck (Qinflow), relative Qinflow, inflow concentration index (ICI), and residual flow volume (RFV) were reported in the aneurysms with wide neck (&gt;4 mm). Significant greater postoperative RFV were reported in the aneurysms with large size (&gt;7 mm). Significant greater postoperative Qinflow, relative Qinflow, and ICI were reported in the aneurysms located on the larteral side of the curve. The bending angle of the internal carotid artery at the initiation of Pcom (αICA@PCOM) and neck diameter had moderate positive correlations with Qinflow, relative Qinflow, ICI, and RFV.ConclusionThe morphological factors, including aneurysm size, neck diameter, and αICA@PCOM, are correlated with the recurrence-inducing hemodynamic characteristics even after fully packing. This provides a theoretical basis for evaluating the risk of aneurysm recurrence and a reference for selecting a surgical plan

Evaluation of associations between genetically predicted circulating protein biomarkers and breast cancer risk.

A small number of circulating proteins have been reported to be associated with breast cancer risk, with inconsistent results. Herein, we attempted to identify novel protein biomarkers for breast cancer via the integration of genomics and proteomics data. In the Breast Cancer Association Consortium (BCAC), with 122,977 cases and 105,974 controls of European descendants, we evaluated the associations of the genetically predicted concentrations of >1,400 circulating proteins with breast cancer risk. We used data from a large-scale protein quantitative trait loci (pQTL) analysis as our study instrument. Summary statistics for these pQTL variants related to breast cancer risk were obtained from the BCAC and used to estimate odds ratios (OR) for each protein using the inverse-variance weighted method. We identified 56 proteins significantly associated with breast cancer risk by instrumental analysis (false discovery rate <0.05). Of these, the concentrations of 32 were influenced by variants close to a breast cancer susceptibility locus (ABO, 9q34.2). Many of these proteins, such as insulin receptor, insulin-like growth factor receptor 1 and other membrane receptors (OR: 0.82-1.18, p values: 6.96 × 10-4 -3.28 × 10-8 ), are linked to insulin resistance and estrogen receptor signaling pathways. Proteins identified at other loci include those involved in biological processes such as alcohol and lipid metabolism, proteolysis, apoptosis, immune regulation and cell motility and proliferation. Consistent associations were observed for 22 proteins in the UK Biobank data (p < 0.05). The study identifies potential novel biomarkers for breast cancer, but further investigation is needed to replicate our findings.Includes CRUK and FP7