Scalable and Effective Generative Information Retrieval

Recent research has shown that transformer networks can be used as differentiable search indexes by representing each document as a sequences of document ID tokens. These generative retrieval models cast the retrieval problem to a document ID generation problem for each given query. Despite their elegant design, existing generative retrieval models only perform well on artificially-constructed and small-scale collections. This has led to serious skepticism in the research community on their real-world impact. This paper represents an important milestone in generative retrieval research by showing, for the first time, that generative retrieval models can be trained to perform effectively on large-scale standard retrieval benchmarks. For doing so, we propose RIPOR- an optimization framework for generative retrieval that can be adopted by any encoder-decoder architecture. RIPOR is designed based on two often-overlooked fundamental design considerations in generative retrieval. First, given the sequential decoding nature of document ID generation, assigning accurate relevance scores to documents based on the whole document ID sequence is not sufficient. To address this issue, RIPOR introduces a novel prefix-oriented ranking optimization algorithm. Second, initial document IDs should be constructed based on relevance associations between queries and documents, instead of the syntactic and semantic information in the documents. RIPOR addresses this issue using a relevance-based document ID construction approach that quantizes relevance-based representations learned for documents. Evaluation on MSMARCO and TREC Deep Learning Track reveals that RIPOR surpasses state-of-the-art generative retrieval models by a large margin (e.g., 30.5% MRR improvements on MS MARCO Dev Set), and perform better on par with popular dense retrieval models

Risk and prognosis of second cutaneous melanoma after radiotherapy for breast cancer: A population-based analysis

Radiation therapy (RT), a primary treatment for breast cancer (BC), may be associated with increased non-BC tumor risk. We aimed to examine second cutaneous melanoma (SCM) risk in BC patients who underwent RT and to assess their survival outcomes. Data from 520,977 BC patients diagnosed between 1973–2018 were collected from the Surveillance, Epidemiology, and End Results (SEER) database. Cumulative SCM incidence was estimated using the Fine–Gray competing risk model. Poisson regression analysis was conducted to calculate the standardized incidence ratio (SIR) and estimate the SCM relative risk in patients who underwent RT compared to those who did not. Overall survival (OS) and cancer-specific survival (CSS) were assessed using the Kaplan‒Meier method. Among the 520,977 BC patients, 243,676 (46.8%) underwent surgery and RT, while 277,301 (53.2%) only underwent surgery. Our results suggest that BC patients receiving RT had a higher SCM risk than those who did not (hazard ratio [HR] 1.40; 95% confidence interval [CI] 1.30-1.51; P < 0.001). SCM incidence was also higher in BC patients treated with RT than in the general US population (SIR 1.12; 95% CI 1.05-1.19; P < 0.05). However, SCM patients who received RT had a significantly higher 10-year survival rate than those who did not receive RT (14.90% vs 5.94%; P < 0.001). No significant difference was found in 10-year OS or 5-year CSS between SCM following RT and only primary cutaneous melanoma (OPCM), but SCM patients who did not receive RT had a significantly lower 10-year OS, with no significant difference in CSS. This study suggests an increased SCM likelihood in BC patients due to RT, although the overall risk is minimal

A MicroRNA-7 Binding Site Polymorphism in HOXB5 Leads to Differential Gene Expression in Bladder Cancer

PURPOSE: To investigate the biological function of HOXB5 in human bladder cancer and explore whether the HOXB5 3'-UTR SNP (1010A/G), which is located within the microRNA-7 binding site, was correlated with clinical features of bladder cancer. METHODS: Expression of HOXB5 in 35 human bladder cancer tissues and 8 cell lines were examined using real-time PCR and immunohistochemistry. Next, we explored the biological function of HOXB5 in vitro using cell proliferation, migration and colony formation assays. Using bioinformatics, a SNP (1010A/G) was found located within the microRNA-7 binding site in the 3'-UTR of HOXB5. Real-time PCR was used to test HOXB5 expression affected by different alleles. Finally, multivariate logistic regression analysis was used to determine the relationship between SNP (1010A/G) frequency and clinical features in 391 cases. RESULTS: HOXB5 was frequently over-expressed both in bladder cancer tissues and cell lines. Inhibition of HOXB5 suppressed the oncogenic function of cancer cells. Next, we demonstrated that a SNP (1010A/G), located within the microRNA-7 binding site in the 3'-UTR of HOXB5, could affect HOXB5 expression in bladder cancer mainly by differential binding activity of microRNA-7 and SNP-related mRNA stability. Finally, we also showed the frequency of 1010G genotype was higher in cancer group compared to normal controls and correlated with the risk of high grade and high stage. CONCLUSION: HOXB5 is overexpressed in bladder cancer. A miRNA-binding SNP (1010A/G) located within 3'-UTR of HOXB5 is associated with gene expression and may be a promising prognostic factor for bladder cancer

Using Principal Component Analysis and Least Squares Support Vector Machine to Predict the Silicon Content in Blast Furnace System

Blast furnace system is a typical example of complex industrial system. The silicon ([Si]) content in blast furnace system is an important index to reflect the temperature of furnace. Therefore, it is significant to carry out an accurate predictive control of furnace temperature. In this paper a composite model combining Principal Component Analysis (PCA) and Least Squares Support Vector Machine (LSSVM) is established to predict the furnace temperature. At the very beginning, in order to avoid redundancy and excessive noise pollution, PCA method is applied to reduce the dimensionality of original input variables. Secondly, the dimension-reduced variables are introduced to predict the silicon content by applying the LSSVM model. Finally, the result is compared with direct multivariable LSSVM prediction. The simulation results show that the new algorithm has positive significance as it achieves more obvious prediction hit rate (more than 80%) than direct multivariable LSSVM (with rate lower than 75%)

The sorafenib resistance-related gene signature predicts prognosis and indicates immune activity in hepatocellular carcinoma

Hepatocellular carcinoma (HCC) is the second most common cause of cancer-related death worldwide. Most patients with advanced HCC acquire sorafenib resistance. Drug resistance reflects the heterogeneity of tumors and is the main cause of tumor recurrence and death.We identified and validated sorafenib resistance related-genes (SRGs) as prognostic biomarkers for HCC. We obtained SRGs from the Gene Expression Omnibus and selected four key SRGs using the least absolute shrinkage and selection operator, random forest, and Support Vector Machine-Recursive feature elimination machine learning algorithms. Samples from the The Cancer Genome Atlas (TCGA)-HCC were segregated into two groups by consensus clustering. Following difference analysis, 19 SRGs were obtained through univariate Cox regression analysis, and a sorafenib resistance model was constructed for risk stratification and prognosis prediction. In multivariate Cox regression analysis, the risk score was an independent predictor of overall survival (OS). Patients classified as high-risk were more sensitive to other chemotherapy drugs and showed a higher expression of the common immune checkpoints. Additionally, the expression of drug-resistance genes was verified in the International Cancer Genome Consortium cohort. A nomogram model with a risk score was established, and its prediction performance was verified by calibration chart analysis of the TCGA-HCC cohort. We conclude that there is a significant correlation between sorafenib resistance and the tumor immune microenvironment in HCC. The risk score could be used to identify a reliable prognostic biomarker to optimize the therapeutic benefits of chemotherapy and immunotherapy, which can be helpful in the clinical decision-making for HCC patients.</p

ZEB1-mediated biogenesis of circNIPBL sustains the metastasis of bladder cancer via Wnt/β-catenin pathway

Abstract Background Circular RNAs (circRNAs) circularized by back-splicing of pre-mRNA are widely expressed and affected the proliferation, invasion and metastasis of bladder cancer (BCa). However, the mechanism underlying circRNA biogenesis in mediating the distant metastasis of BCa still unexplored. Methods RNA sequencing data between BCa and normal adjacent tissues was applied to identify the differentially expressed circRNAs. The functions of circNIPBL in BCa were investigated via a series of biochemical experiments. The Clinical significance of circNIPBL was examined in a cohort of larger BCa tissues. Results In the present study, we identified a novel circRNA (hsa_circ_0001472), circNIPBL, which was significantly upregulated and had great influence on the poor prognosis of patients with BCa. Functionally, circNIPBL promotes BCa metastasis in vitro and in vivo. Mechanistically, circNIPBL upregulate the expression of Wnt5a and activated the Wnt/β-catenin signaling pathway via directly sponged miR-16-2-3p, leading to the upregulation of ZEB1, which triggers the EMT of BCa. Moreover, we revealed that ZEB1 interacted with the flanking introns of exons 2–9 on NIPBL pre-mRNA to trigger circNIPBL biogenesis, thus forming a positive feedback loop. Importantly, circNIPBL overexpression significantly facilitated the distant metastasis of BCa in the orthotopic bladder cancer model, while silencing ZEB1 remarkably blocked the effects of metastasis induced by circNIPBL overexpression. Conclusions Our study highlights that circNIPBL-induced Wnt signaling pathway activation triggers ZEB1-mediated circNIPBL biogenesis, which forms a positive feedback loop via the circNIPBL/miR-16-2-3p/Wnt5a/ZEB1 axis, supporting circNIPBL as a novel therapeutic target and potential biomarker for BCa patients. Graphical Abstrac

Development and Validation of Survival Nomograms in Patients with Primary Bladder Lymphoma

Background: The existing studies on primary bladder lymphoma (PBL) are retrospective analyses based on individual cases or small series studies, and the research on PBL is not unified and in-depth enough at present because of the scarcity of PBL and the lack of relevant literature. This study is designed to develop and validate nomograms for overall survival (OS) and cancer-specific survival (CSS) prediction in patients with PBL. Methods: According to the Surveillance, Epidemiology, and End Results (SEER) database, 405 patients diagnosed with PBL from 1975 to 2016 were collected and randomly assigned to training (n = 283) and validation (n = 122) cohort. After the multivariable Cox regression, the OS and CSS nomograms were developed. The discrimination, calibration and clinical usefulness of the nomograms were assessed and validated, respectively, by the training and validation cohort. Furthermore, all of the patients were reclassified into high- and low-risk groups and their survival were compared through Kaplan-Meier method and log-rank test. Results: Age, subtype, Ann Arbor stage, radiation and chemotherapy were identified as independent prognostic factors for OS and age, sex, and subtype for CSS, then corresponding nomograms predicting the 3- and 5-year survival were constructed. The presented nomograms demonstrated good discrimination and calibration, which the C-index in the training and validation cohort were 0.744 (95% confidence interval [CI], 0.705–0.783) and 0.675 (95% CI, 0.603–0.747) for OS nomogram and 0.692 (95% CI, 0.632–0.752) and 0.646 (95% CI, 0.549–0.743) for CSS nomogram, respectively. Furthermore, the nomograms can be used to effectively distinguish Patients with PBL at high risk of death. The clinical usefulness of the nomograms was visually displayed by decision curve analysis. Conclusion: We updated the baseline characteristics of patients with PBL and constructed OS and CSS nomograms to predict their 3- and 5-year survival. Using these nomograms, it would be convenient to individually predict the prognosis of patients with PBL and provide guidance for clinical treatment

13-Methyltetradecanoic Acid Exhibits Anti-Tumor Activity on T-Cell Lymphomas <i>In Vitro</i> and <i>In Vivo</i> by Down-Regulating p-AKT and Activating Caspase-3

<div><p>13-Methyltetradecanoic acid (13-MTD), a saturated branched-chain fatty acid purified from soy fermentation products, induces apoptosis in human cancer cells. We investigated the inhibitory effects and mechanism of action of 13-MTD on T-cell non-Hodgkin’s lymphoma (T-NHL) cell lines both <i>in vitro</i> and <i>in vivo</i>. Growth inhibition in response to 13-MTD was evaluated by the cell counting kit-8 (CCK-8) assay in three T-NHL cell lines (Jurkat, Hut78, EL4 cells). Flow cytometry analyses were used to monitor the cell cycle and apoptosis. Proteins involved in 13-MTD-induced apoptosis were examined in Jurkat cells by western blotting. We found that 13-MTD inhibited proliferation and induced the apoptosis of T-NHL cell lines. 13-MTD treatment also induced a concentration-dependent arrest of Jurkat cells in the G₁-phase. During 13-MTD-induced apoptosis in Jurkat cells, the cleavage of caspase-3 and poly ADP-ribose polymerase (PARP, a caspase enzymolysis product) were detected after incubation for 2 h, and increased after extending the incubation time. However, there was no change in the expression of Bcl-2 or c-myc proteins. The appearance of apoptotic Jurkat cells was accompanied by the inhibition of AKT and nuclear factor-kappa B (NF-κB) phosphorylation. In addition, 13-MTD could also effectively inhibit the growth of T-NHL tumors <i>in vivo</i> in a xenograft model. The tumor inhibition rate in the experimental group was 40%. These data indicate that 13-MTD inhibits proliferation and induces apoptosis through the down-regulation of AKT phosphorylation followed by caspase activation, which may provide a new approach for treating T-cell lymphomas.</p></div