Alterations in urinary microbiota composition in urolithiasis patients: insights from 16S rRNA gene sequencing

ObjectivesTo investigate the urinary microbiota composition in urolithiasis patients compared to healthy controls and to identify potential microbial markers and their association with clinical parameters.MethodsA total of 66 samples, comprising 45 from urolithiasis patients and 21 from healthy controls, were analyzed. 16S rRNA gene sequencing was employed to determine the microbiota composition. Various statistical and bioinformatics tools, including ANOVA, PCoA, and LEfSe, were utilized to analyze the sequencing data and identify significant differences in microbial abundance.ResultsNo significant demographic differences were observed between the two groups. Post-quality control, clean tags ranged from 60,979 to 68,736. Significant differences in α-diversity were observed between the two groups. β-diversity analysis revealed distinct clustering of the urinary microbiota in urolithiasis patients and controls. Notably, Ruminococcaceae was predominant in urolithiasis samples, while Proteobacteria was more prevalent in healthy samples. Lactobacillus was significantly overrepresented in samples from healthy females.ConclusionThe urinary microbiota composition in urolithiasis patients is distinct from that of healthy controls. Specific microbial taxa, such as Ruminococcaceae and Proteobacteria, could serve as potential biomarkers for urolithiasis. The findings pave the way for further exploration of the role of microbiota in urolithiasis and the development of microbiome-based therapeutic strategies

INarIG: Iterative Non-autoregressive Instruct Generation Model For Word-Level Auto Completion

Computer-aided translation (CAT) aims to enhance human translation efficiency and is still important in scenarios where machine translation cannot meet quality requirements. One fundamental task within this field is Word-Level Auto Completion (WLAC). WLAC predicts a target word given a source sentence, translation context, and a human typed character sequence. Previous works either employ word classification models to exploit contextual information from both sides of the target word or directly disregarded the dependencies from the right-side context. Furthermore, the key information, i.e. human typed sequences, is only used as prefix constraints in the decoding module. In this paper, we propose the INarIG (Iterative Non-autoregressive Instruct Generation) model, which constructs the human typed sequence into Instruction Unit and employs iterative decoding with subwords to fully utilize input information given in the task. Our model is more competent in dealing with low-frequency words (core scenario of this task), and achieves state-of-the-art results on the WMT22 and benchmark datasets, with a maximum increase of over 10% prediction accuracy.Comment: EMNLP202

Text Style Transfer Back-Translation

Back Translation (BT) is widely used in the field of machine translation, as it has been proved effective for enhancing translation quality. However, BT mainly improves the translation of inputs that share a similar style (to be more specific, translation-like inputs), since the source side of BT data is machine-translated. For natural inputs, BT brings only slight improvements and sometimes even adverse effects. To address this issue, we propose Text Style Transfer Back Translation (TST BT), which uses a style transfer model to modify the source side of BT data. By making the style of source-side text more natural, we aim to improve the translation of natural inputs. Our experiments on various language pairs, including both high-resource and low-resource ones, demonstrate that TST BT significantly improves translation performance against popular BT benchmarks. In addition, TST BT is proved to be effective in domain adaptation so this strategy can be regarded as a general data augmentation method. Our training code and text style transfer model are open-sourced.Comment: acl2023, 14 pages, 4 figures, 19 table

R-BI: Regularized Batched Inputs enhance Incremental Decoding Framework for Low-Latency Simultaneous Speech Translation

Incremental Decoding is an effective framework that enables the use of an offline model in a simultaneous setting without modifying the original model, making it suitable for Low-Latency Simultaneous Speech Translation. However, this framework may introduce errors when the system outputs from incomplete input. To reduce these output errors, several strategies such as Hold-$n$, LA-$n$, and SP-$n$ can be employed, but the hyper-parameter $n$ needs to be carefully selected for optimal performance. Moreover, these strategies are more suitable for end-to-end systems than cascade systems. In our paper, we propose a new adaptable and efficient policy named "Regularized Batched Inputs". Our method stands out by enhancing input diversity to mitigate output errors. We suggest particular regularization techniques for both end-to-end and cascade systems. We conducted experiments on IWSLT Simultaneous Speech Translation (SimulST) tasks, which demonstrate that our approach achieves low latency while maintaining no more than 2 BLEU points loss compared to offline systems. Furthermore, our SimulST systems attained several new state-of-the-art results in various language directions.Comment: Preprin

UCorrect: An Unsupervised Framework for Automatic Speech Recognition Error Correction

Error correction techniques have been used to refine the output sentences from automatic speech recognition (ASR) models and achieve a lower word error rate (WER). Previous works usually adopt end-to-end models and has strong dependency on Pseudo Paired Data and Original Paired Data. But when only pre-training on Pseudo Paired Data, previous models have negative effect on correction. While fine-tuning on Original Paired Data, the source side data must be transcribed by a well-trained ASR model, which takes a lot of time and not universal. In this paper, we propose UCorrect, an unsupervised Detector-Generator-Selector framework for ASR Error Correction. UCorrect has no dependency on the training data mentioned before. The whole procedure is first to detect whether the character is erroneous, then to generate some candidate characters and finally to select the most confident one to replace the error character. Experiments on the public AISHELL-1 dataset and WenetSpeech dataset show the effectiveness of UCorrect for ASR error correction: 1) it achieves significant WER reduction, achieves 6.83\% even without fine-tuning and 14.29\% after fine-tuning; 2) it outperforms the popular NAR correction models by a large margin with a competitive low latency; and 3) it is an universal method, as it reduces all WERs of the ASR model with different decoding strategies and reduces all WERs of ASR models trained on different scale datasets.Comment: Accepted in ICASSP 202

Association between body roundness index and prevalence of kidney stone in the U.S: a study based on the NHANES database

Abstract Objective This study aimed to evaluate the potential association between the body roundness index (BRI) and kidney stone prevalence in adults in the United States. Methods A cohort of participants from the National Health and Nutrition Examination Survey (NHANES) database spanning 2007–2018 were gathered for analysis. Logistic regression analyses, subgroup assessments, and calculations were employed to examine the potential link between BRI and kidney stone prevalence. Results The study included 30,990 participants aged > 20 years, of which 2,891 declared a kidney stone history. After modulating all relevant confounding factors, each unit increase in the BRI was linked to a 65% increase in kidney stone prevalence (OR = 1.65, 95% CI: 1.47, 1.85). Sensitivity analyses conducted by categorizing the BRI into three groups revealed a 59% increase in kidney stone prevalence in the highest tertile BRI group compared to the lowest one (OR = 1.59, 95% CI: 1.42, 1.79). Furthermore, dose-response curves depicted a positive near-linear correlation between the BRI and the risk of kidney stone prevalence. Conclusion These findings suggest a clinically noteworthy positive correlation between higher BRI values and kidney stone prevalence among the studied US adult population. However, it is essential to acknowledge that the observed relationship does not establish a causal link

A Hydrogen Production System Based on Ammonia Combustion Heat: Graded Decomposition and Parameter Analysis

This study introduces a hydrogen production system that utilizes the heat from ammonia combustion for graded decomposition. About 17% of the ammonia undergoes combustion to provide heat for the subsequent decomposition of the remaining ammonia. To enhance economic efficiency and reduce costs, a design combining precious and non-precious metals is employed, aiming to decrease the usage of precious metal catalysts without compromising decomposition efficiency. Considering the required temperatures for the two catalysts, high and low-temperature decomposers are established to achieve cascaded energy utilization in the flue gas. The preheating temperature of ammonia before entering the decomposer plays a crucial role in both ammonia decomposition efficiency and the system’s fuel consumption. Following optimization, the system yields 493.6kg/h of hydrogen with an inlet ammonia flow rate of 4000kg/h. Concurrently, the discharge temperature of the flue gas decreases to 378.59K, effectively utilizing a substantial portion of the energy. This study introduces a novel approach to designing an ammonia decomposition system using ammonia combustion as a heat source, and it serves as a reference for subsequent optimization

Overexpression of DDX49 in prostate cancer is associated with poor prognosis

Abstract Background There is increasing evidence that DEAD-box helicases (DDX) can act either as promoters or suppressors in various cancer types. Nevertheless, the function of DDX49 in prostate cancer (PCa) is unknown. This study reveals the prognostic and predictive value of DDX49 in PCa. Methods First, we evaluated the expression of DDX49 between PCa and normal tissues based on TCGA and GEO databases. Univariate and multivariate regression analyses were conducted to reveal the risk factors for PCa recurrence. A K–M curve was employed to assess the relationship between DDX49 and recurrence-free survival. In vitro, DDX49 expression was evaluated in PCa and normal prostate cell lines. Furthermore, we constructed a shDDX49 lentivirus to knock down the expression of DDX49. Celigo® Image Cytometer and MTT assay were performed to analyse cell proliferation in PC-3 cells. Cell cycle distribution was detected with flow cytometry analysis. Apoptosis affected by the lack of DDX49 was metred with the PathScan® Stress and Apoptosis Signalling Antibody Array Kit. Results This study shows a high increase in DDX49 in PCa tissues in comparison with normal tissues and that increased DDX49 indicates a poor prognosis among PCa patients. Meanwhile, DDX49 knockdown suppressed the proliferation and migration of PC-3 cells, causing cell cycle arrest in the G1 phase. Stress and apoptosis pathway analysis revealed that the phosphorylation of HSP27, p53, and SAPK/JNK was reduced in the DDX49 knockdown group compared with the control group. Conclusions In summary, these results suggest that high expression of DDX49 predicts a poor prognosis among PCa patients. Downregulation of DDX49 can suppress cell proliferation, block the cell cycle, and facilitate cell apoptosis. Therefore, knockdown of DDX49 is a promising novel therapy for treating patients with PCa

Association Between Twelve Polymorphisms in Five X-ray Repair Cross-complementing Genes and the Risk of Urological Neoplasms: A Systematic Review and Meta-Analysis

Polymorphisms in X-ray repair cross-complementing (XRCC) genes have been implicated in altering the risk of various urological cancers. However, the results of reported studies are controversial. To ascertain whether polymorphisms in XRCC genes are associated with the risk of urological neoplasms, we conducted present updated meta-analysis and systematic review. Summary odds ratios (ORs) and corresponding 95% confidence intervals (CIs) were used to estimate the association. Finally, 54 publications comprising 129 case-control studies for twelve polymorphisms in five XRCC genes were enrolled. We identified that XRCC1-rs25489 polymorphism was associated with an increased risk of urological neoplasms in heterozygote and dominant models. Moreover, in the subgroup analysis by cancer type, we found that XRCC1-rs25489 polymorphism was associated with an increased risk of bladder cancer (BC) in heterozygote model. Although overall analyses suggested a null result for XRCC1-rs25487 polymorphism, in the stratified analysis by ethnicity, an increased risk of urological neoplasms for Asians in allelic and homozygote models was identified. While for other polymorphisms in XRCC genes, no significant association was uncovered. To sum up, our results indicated that XRCC1-rs25489 polymorphism is a risk factor for urological neoplasms, particularly for BC. Further studies with large sample size are needed to validate these findings