Explore the vast range of titles available.

The options for first-line treatment of advanced oesophageal squamous cell carcinoma are scarce, and the outcomes remain poor. The anti-PD-1 antibody, tislelizumab, has shown antitumour activity in previously treated patients with advanced oesophageal squamous cell carcinoma. We report interim analysis results from the RATIONALE-306 study, which aimed to assess tislelizumab plus chemotherapy versus placebo plus chemotherapy as first-line treatment for advanced or metastatic oesophageal squamous cell carcinoma. This global, randomised, double-blind, parallel-arm, placebo-controlled, phase 3 study was conducted at 162 medical centres across Asia, Europe, Oceania, and North America. Patients (aged ≥18 years) with unresectable, locally advanced, recurrent or metastatic oesophageal squamous cell carcinoma (regardless of PD-L1 expression), Eastern Cooperative Oncology Group performance status of 0–1, and measurable or evaluable disease per Response Evaluation Criteria in Solid Tumours (version 1.1) were recruited. Patients were randomly assigned (1:1), using permuted block randomisation (block size of four) and stratified by investigator-chosen chemotherapy, region, and previous definitive therapy, to tislelizumab 200 mg or placebo intravenously every 3 weeks on day 1, together with an investigator-chosen chemotherapy doublet, comprising a platinum agent (cisplatin 60–80 mg/m2 intravenously on day 1 or oxaliplatin 130 mg/m2 intravenously on day 1) plus a fluoropyrimidine (fluorouracil [750–800 mg/m2 intravenously on days 1–5] or capecitabine [1000 mg/m2 orally twice daily on days 1–14]) or paclitaxel (175 mg/m2 intravenously on day 1). Treatment was continued until disease progression or unacceptable toxicity. Investigators, patients, and sponsor staff or designees were masked to treatment. The primary endpoint was overall survival. The efficacy analysis was done in the intention-to-treat population (ie, all randomly assigned patients) and safety was assessed in all patients who received at least one dose of study treatment. The trial is registered with ClinicalTrials.gov, NCT03783442. Between Dec 12, 2018, and Nov 24, 2020, 869 patients were screened, of whom 649 were randomly assigned to tislelizumab plus chemotherapy (n=326) or placebo plus chemotherapy (n=323). Median age was 64·0 years (IQR 59·0–69·0), 563 (87%) of 649 participants were male, 86 (13%) were female, 486 (75%) were Asian, and 155 (24%) were White. 324 (99%) of 326 patients in the tislelizumab group and 321 (99%) of 323 in the placebo group received at least one dose of the study drug. As of data cutoff (Feb 28, 2022), median follow-up was 16·3 months (IQR 8·6–21·8) in the tislelizumab group and 9·8 months (IQR 5·8–19·0) in the placebo group, and 196 (60%) of 326 patients in the tislelizumab group versus 226 (70%) of 323 in the placebo group had died. Median overall survival in the tislelizumab group was 17·2 months (95% CI 15·8–20·1) and in the placebo group was 10·6 months (9·3–12·1; stratified hazard ratio 0·66 [95% CI 0·54–0·80]; one-sided p<0·0001). 313 (97%) of 324 patients in the tislelizumab group and 309 (96%) of 321 in the placebo group had treatment-related treatment-emergent adverse events. The most common grade 3 or 4 treatment-related treatment-emergent adverse events were decreased neutrophil count (99 [31%] in the tislelizumab group vs 105 [33%] in the placebo group), decreased white blood cell count (35 [11%] vs 50 [16%]), and anaemia (47 [15%] vs 41 [13%]). Six deaths in the tislelizumab group (gastrointestinal and upper gastrointestinal haemorrhage [n=2], myocarditis [n=1], pulmonary tuberculosis [n=1], electrolyte imbalance [n=1], and respiratory failure [n=1]) and four deaths in the placebo group (pneumonia [n=1], septic shock [n=1], and unspecified death [n=2]) were determined to be treatment-related. Tislelizumab plus chemotherapy as a first-line treatment for advanced or metastatic oesophageal squamous cell carcinoma provided superior overall survival with a manageable safety profile versus placebo plus chemotherapy. Given that the interim analysis met its superiority boundary for the primary endpoint, as confirmed by the independent data monitoring committee, this Article represents the primary study analysis. BeiGene.

Water resources are vital to the development of human society, and mastering the law of runoff changes is the basis for achieving sustainable use of water resources. To study the impact of reservoir construction on the changes of downstream river runoff, this paper decomposes the runoff before and after reservoir construction using the CEEMDAN method based on the runoff data from the Huayuankou hydrological station. The fluctuation characteristics of each decomposition series of runoff before and after reservoir construction and the intra-annual variation pattern of runoff are also analyzed by combining multi-time information entropy and coefficient of variation. The results show that after the operation of the Xiaolangdi Reservoir, the annual runoff variation cycle tends to be flat, and the monthly runoff cycle is significantly reduced. After reservoir construction, the entropy values of each IMF and Res of runoff become larger, the complexity and randomness of runoff changes increase, and predictability decreases. Before and after the operation of the Xiaolangdi Reservoir, the coefficient of variation of runoff were 0.28–1 and 0.38–0.83, the distribution of runoff was more uniform, and the percentage of runoff in the flood season was reduced from 51.51 to 39.89%.

Background Bacterial small non-coding RNAs (sRNAs) have emerged as important elements in diverse physiological processes, including growth, development, cell proliferation, differentiation, metabolic reactions and carbon metabolism, and attract great attention. Accurate prediction of sRNAs is important and challenging, and helps to explore functions and mechanism of sRNAs. Results In this paper, we utilize a variety of sRNA sequence-derived features to develop ensemble learning methods for the sRNA prediction. First, we compile a balanced dataset and four imbalanced datasets. Then, we investigate various sRNA sequence-derived features, such as spectrum profile, mismatch profile, reverse compliment k-mer and pseudo nucleotide composition. Finally, we consider two ensemble learning strategies to integrate all features for building ensemble learning models for the sRNA prediction. One is the weighted average ensemble method (WAEM), which uses the linear weighted sum of outputs from the individual feature-based predictors to predict sRNAs. The other is the neural network ensemble method (NNEM), which trains a deep neural network by combining diverse features. In the computational experiments, we evaluate our methods on these five datasets by using 5-fold cross validation. WAEM and NNEM can produce better results than existing state-of-the-art sRNA prediction methods. Conclusions WAEM and NNEM have great potential for the sRNA prediction, and are helpful for understanding the biological mechanism of bacteria.

Two hundred and sixty road dust samples collected from Xinjiang, China, were resuspended and analyzed for polycyclic aromatic hydrocarbons (PAHs) and their derivatives. The concentrations of ∑ 16 PAHs, ∑ 18 NPAHs and ∑ 5 OPAHs (ng mg −1 ) in the road dust PM 10 ranged from 22.03 to 179.04, 0.34–3.14, and 0.95–17.56, with median values of 69.09, 1.14, and 4.37, respectively. Phe, Ace, Flt, 2N-Pyr, 9-FO, and ATO were the main substances found. The order of the median concentrations of ∑ 16 PAHs in each city in the region was Altay > Aksu > Karamay > Bole > Kashgar > Korla > Tacheng > Hami > Turpan > Wusu > Hotan > Kuitun > Huyanghe > Atushi. The order of the ∑ 18 NPAHs concentrations in each city was Altay > Karamay > Hami > Korla > Bole > Kashgar > Wusu > Tacheng > Aksu > Huyanghe > Atushi > Kuitun > Hotan > Turpan. The order of the ∑ 5 OPAHs concentrations in each city was Altay > Karamay > Tacheng > Kuitun > Aksu > Kashgar > Korla > Hami > Wusu > Bole > Hotan > Huyanghe > Turpan > Atushi. According to the diagnostic ratio and PMF results, four source factors were identified: biomass combustion (36.3%), coal combustion (39.0%), gasoline and diesel combustion (13.3%), and petroleum (11.4%). The total incremental lifetime cancer risks (TILCR) due to human exposure to road dust PAHs in Xinjiang were 8.2 × 10 −6 for children, 1.2 × 10 −5 for females, and 1.2 × 10 −5 for males.

Background To investigate the independent and combined associations of hearing loss and depression with cognitive function among older adults and to analyze the differences by sex. Methods A cross-sectional analysis was conducted using 2018 data from the China Health and Retirement Longitudinal Study (CHARLS), including 2,894 participants aged ≥ 60 years. A composite variable based on self-reported hearing and depressive symptoms categorized participants into six groups. Weighted logistic regression assessed associations with cognitive function. Interaction terms and sex-stratified analyses were applied to examine sex differences. Results Poor hearing (OR = 2.07, 95% CI: 1.51–2.83) and depression (OR = 2.02, 95% CI: 1.58–2.58) were each significantly associated with cognitive impairment. Compared with the group with good hearing and no depression, participants in the fair hearing with depression group (OR = 2.11, 95% CI: 1.46–3.06) and the poor hearing with depression group (OR = 4.78, 95% CI: 3.19–7.16) showed significantly higher risks of cognitive impairment, with a stronger association observed in the latter. Interaction analysis revealed significant heterogeneity by sex ( P for interaction in Model 4 = 0.020). In sex-stratified analysis, the associations were stronger among males: fair hearing with depression (OR = 2.29, 95% CI: 1.35–3.89) and poor hearing with depression (OR = 5.44, 95% CI: 3.11–9.52). Among females, the corresponding ORs were 2.25 (95% CI: 1.34–3.78, P  = 0.002) and 4.99 (95% CI: 2.80–8.90, P  < 0.001), respectively. Conclusion The combination of hearing loss and depression is strongly associated with cognitive impairment in older adults, with significant heterogeneity across sex groups. The association is particularly stronger among males, suggesting the importance of early identification and targeted intervention in high-risk populations.

As one of the important hydrological elements of rivers, flow is of great significance to the development and utilization of water resources and the ecological environment. Based on the excellent nonlinear processing capability of CEEMDAN and the advantages of BILSTM in time-series data modeling, a coupled CEEMDAN-BILSTM model is constructed for flow prediction, and the i-month flows from 1951 to 2016 are used to predict the i-month flows from 2017 to 2021. The results show that the CEEMDAN-BILSTM coupled model predicts the trend more closely with the actual data variation, and the minimum relative error is 0.56 and maximum 9.48, which are maintained within 10%, and the deterministic coefficients are all greater than 0.9, so the prediction accuracy is high. The flow in month i of 5 years was picked up by monthly predictions for 66 consecutive years, which provides a new way of thinking about the prediction of river flow.

Accurate prediction of monthly precipitation is crucial for effective regional water resources management and utilization. However, precipitation series are influenced by multiple factors, exhibiting significant ambiguity, chance, and uncertainty. In this research, we propose a combined model that integrates adaptive noise-complete ensemble empirical mode decomposition (CEEMDAN), variational modal decomposition method (VMD), and bidirectional long- and short-term memory (BILSTM) to enhance precipitation prediction. We apply this model to forecast precipitation in Fuzhou City and compare its performance with existing models, including CEEMD–long and short-term memory (LSTM), CEEMD–BILSTM, and CEEMDAN–BILSTM. Our findings demonstrate that the combined CEEMDAN–VMD–BILSTM quadratic decomposition model yields more accurate predictions and captures the real variation in precipitation series with greater fidelity. The model achieves an average relative error of 1.69%, at a lower level, and an average absolute error of 1.32 m, with a Nash–Sutcliffe efficiency coefficient of 0.92. Overall, the proposed quadratic decomposition model exhibits excellent applicability, stability, and superior predictive capabilities in monthly precipitation forecasting.

The prediction of monthly precipitation is of great importance for regional water resources management and use. The monthly precipitation sequence is affected by various factors such as atmosphere, region and environment, and has obvious ambiguity, chance and uncertainty. CEEMD based on complementary ensemble empirical mode decomposition can effectively reduce the reconstruction error of time series, and bidirectional long short-term memory (BILSTM) model can effectively learn long-term dependencies in time series. A CEEMD-BILSTM (complementary integrated empirical mode decomposition-bidirectional long short-term memory) coupled model is constructed to predict the monthly precipitation in Zhengzhou, and the performances of the LSTM model, EEMD-LSTM model and EEMD-BILSTM model are compared. The CEEMD-BILSTM model has a maximum relative error of 7.28%, a minimum relative error of 0.00%, and an average relative error of 2.68%, with an RMS error of 2.6% and a coefficient of determination of 0.97 in predicting monthly precipitation in Zhengzhou, which is considered a good accuracy of the CEEMD-BILSTM model for predicting monthly precipitation in Zhengzhou. The model is better than the LSTM model, the EEMD-LSTM model, and the EEMD-BILSTM model and has better fitting ability. It also shows that it has strong nonlinear and complex process learning ability in the hydrological factor model of regional precipitation prediction.

Serine-52 (Ser52) is the major physiologic site of keratin 18 (K18) phosphorylation. Here, we report that serine-52 phosphorylated K18 (phospho-Ser52 K18) accumulated on centrosomes in a cell cycle-dependent manner. Moreover, we found that phospho-Ser52 K18 was located at the proximal end of the mother centriole. Transfection with the K18 Ser52 → Ala (K18 S52A) mutant prevented centriole localization of phospho-Ser52 K18 and resulted in separation of the mother–daughter centrioles. Inhibition of microtubule polymerization led to the disappearance of aggregated phospho-Ser52 K18 on the centrosome; removal of inhibitors resulted in reaccumulation of phospho-Ser52 K18 in microtubule-organizing centers. Transfection with a K18 S52A mutant inhibited microtubule nucleation. These results reveal a cell cycle-dependent change in centrosome localization of phospho-Ser52 k18 and strongly suggest that the phosphorylation status of Ser52 K18 of mother centrioles plays a critical role in maintaining a tight engagement between mother and daughter centrioles and also contributes to microtubule nucleation.

Increasing numbers of public and private locations now have surveillance cameras installed to make those areas more secure. Even though many organizations still hire someone to monitor the cameras, the person hired is more likely to miss some unexpected events in the video feeds because of human error. Several researchers have worked on surveillance data and have presented a number of approaches for automatically detecting aberrant events. To keep track of all the video data that accumulate, a supervisor is often required. To analyze the video data automatically, we recommend using neural networks to identify the crimes happening in the real world. Through our approach, it will be easier for police agencies to discover and assess criminal activity more quickly using our method, which will reduce the burden on their staff. In this paper, we aim to provide anomaly detection using surveillance videos as input specifically for the crimes of arson, burglary, stealing, and vandalism. It will provide an efficient and adaptable crime-detection system if integrated across the smart city infrastructure. In our project, we trained multiple accurate deep learning models for object detection and crime classification for arson, burglary and vandalism. For arson, the videos were trained using YOLOv5. Similarly for burglary and vandalism, we trained using YOLOv7 and YOLOv6, respectively. When the models were compared, YOLOv7 performed better with the highest mAP of 87. In this, we could not compare the model’s performance based on crime type because all the datasets for each crime type varied. So, for arson YOLOv5 performed well with 80% mAP and for vandalism, YOLOv6 performed well with 86% mAP. This paper designed an automatic identification of crime types based on camera or surveillance video in the absence of a monitoring person, and alerts registered users about crimes such as arson, burglary, and vandalism through an SMS service. To detect the object of the crime in the video, we trained five different machine learning models: Improved YOLOv5 for arson, Faster RCNN and YOLOv7 for burglary, and SSD MobileNet and YOLOv6 for vandalism. Other than improved models, we innovated by building ensemble models of all three crime types. The main aim of the project is to provide security to the society without human involvement and make affordable surveillance cameras to detect and classify crimes. In addition, we implemented the Web system design using the built package in Python, which is Gradio. This helps the registered user of the Twilio communication tool to receive alert messages when any suspicious activity happens around their communities.