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Non-variceal upper gastrointestinal bleeding remains an important emergency condition, leading to significant morbidity and mortality. As endoscopic therapy is the ’gold standard' of management, treatment of these patients can be considered in three stages: pre-endoscopic treatment, endoscopic haemostasis and post-endoscopic management. Since publication of the Asia-Pacific consensus on non-variceal upper gastrointestinal bleeding (NVUGIB) 7 years ago, there have been significant advancements in the clinical management of patients in all three stages. These include pre-endoscopy risk stratification scores, blood and platelet transfusion, use of proton pump inhibitors; during endoscopy new haemostasis techniques (haemostatic powder spray and over-the-scope clips); and post-endoscopy management by second-look endoscopy and medication strategies. Emerging techniques, including capsule endoscopy and Doppler endoscopic probe in assessing adequacy of endoscopic therapy, and the pre-emptive use of angiographic embolisation, are attracting new attention. An emerging problem is the increasing use of dual antiplatelet agents and direct oral anticoagulants in patients with cardiac and cerebrovascular diseases. Guidelines on the discontinuation and then resumption of these agents in patients presenting with NVUGIB are very much needed. The Asia-Pacific Working Group examined recent evidence and recommends practical management guidelines in this updated consensus statement.

\"Driven by the newly placed importance and growing search for ways to make computing greener and more efficient, this reference is the first research-level book devoted to green computing and large-scale energy efficiency. With contributions from leading experts in the field, the book presents current research and developments in hardware, systems software, run-time systems, programming languages, data center management, and applications. It also covers the emerging green movement in computing, including the Green Grid and the Green 500 list, as well as important programs in grassroots organizations and government agencies\"-- Provided by publisher.

The gut microbiota has been implicated in cancer and shown to modulate anticancer drug efficacy. Altered gut microbiota is associated with resistance to chemo drugs or immune checkpoint inhibitors (ICIs), whereas supplementation of distinct bacterial species restores responses to the anticancer drugs. Accumulating evidence has revealed the potential of modulating the gut microbiota to enhance the efficacy of anticancer drugs. Regardless of the valuable findings by preclinical models and clinical data of patients with cancer, a more thorough understanding of the interactions of the microbiota with cancer therapy helps researchers identify novel strategy for cancer prevention, stratify patients for more effective treatment and reduce treatment complication. In this review, we discuss the scientific evidence on the role of gut microbiota in cancer treatment, and highlight the latest knowledge and technologies leveraged to target specific bacteria that contribute to tumourigenesis. First, we provide an overview of the role of the gut microbiota in cancer, establishing the links between bacteria, inflammation and cancer treatment. Second, we highlight the mechanisms used by distinct bacterial species to modulate cancer growth, immune responses, as well as the efficacy of chemotherapeutic drugs and ICIs. Third, we demonstrate various approaches to modulate the gut microbiota and their potential in translational research. Finally, we discuss the limitations of current microbiome research in the context of cancer treatment, ongoing efforts to overcome these challenges and future perspectives.

Whereas standard transmission electron microscopy studies are unable to preserve the native state of chemically reactive and beam-sensitive battery materials after operation, such materials remain pristine at cryogenic conditions. It is then possible to atomically resolve individual lithium metal atoms and their interface with the solid electrolyte interphase (SEI). We observe that dendrites in carbonate-based electrolytes grow along the (preferred), , or directions as faceted, single-crystalline nanowires. These growth directions can change at kinks with no observable crystallographic defect. Furthermore, we reveal distinct SEI nanostructures formed in different electrolytes.

Fast-charging and high-energy-density batteries pose significant safety concerns due to high rates of heat generation. Understanding how localized high temperatures affect the battery is critical but remains challenging, mainly due to the difficulty of probing battery internal temperature with high spatial resolution. Here we introduce a method to induce and sense localized high temperature inside a lithium battery using micro-Raman spectroscopy. We discover that temperature hotspots can induce significant lithium metal growth as compared to the surrounding lower temperature area due to the locally enhanced surface exchange current density. More importantly, localized high temperature can be one of the factors to cause battery internal shorting, which further elevates the temperature and increases the risk of thermal runaway. This work provides important insights on the effects of heterogeneous temperatures within batteries and aids the development of safer batteries, thermal management schemes, and diagnostic tools. Operation of lithium batteries at high, non-uniform temperatures can lead to safety issues, but the effects of localized high temperatures are difficult to probe. Here the authors use micro-Raman spectroscopy to show that local-temperature hotspots can induce lithium metal growth and trigger circuit shorting.

During the COVID-19 pandemic, the continuous spread of misinformation on the internet posed an ongoing threat to public trust and understanding of epidemic prevention policies. Although the pandemic is now under control, information regarding the risks of long-term COVID-19 effects and reinfection still needs to be integrated into COVID-19 policies. This study aims to develop a robust and generalizable deep learning framework for detecting misinformation related to the prolonged impacts of COVID-19 by integrating pretrained language models (PLMs) with an innovative fuzzy rank-based ensemble approach. A comprehensive dataset comprising 566 genuine and 2361 fake samples was curated from reliable open sources and processed using advanced techniques. The dataset was randomly split using the scikit-learn package to facilitate both training and evaluation. Deep learning models were trained for 20 epochs on a Tesla T4 for hierarchical attention networks (HANs) and an RTX A5000 (for the other models). To enhance performance, we implemented an ensemble learning strategy that incorporated a reparameterized Gompertz function, which assigned fuzzy ranks based on each model's prediction confidence for each test case. This method effectively fused outputs from state-of-the-art PLMs such as robustly optimized bidirectional encoder representations from transformers pretraining approach (RoBERTa), decoding-enhanced bidirectional encoder representations from transformers with disentangled attention (DeBERTa), and XLNet. After training on the dataset, various classification methods were evaluated on the test set, including the fuzzy rank-based method and state-of-the-art large language models. Experimental results reveal that language models, particularly XLNet, outperform traditional approaches that combine term frequency-inverse document frequency features with support vector machine or utilize deep models like HAN. The evaluation metrics-including accuracy, precision, recall, F -score, and area under the curve (AUC)-indicated a clear performance advantage for models that had a larger number of parameters. However, this study also highlights that model architecture, training procedures, and optimization techniques are critical determinants of classification effectiveness. XLNet's permutation language modeling approach enhances bidirectional context understanding, allowing it to surpass even larger models in the bidirectional encoder representations from transformers (BERT) series despite having relatively fewer parameters. Notably, the fuzzy rank-based ensemble method, which combines multiple language models, achieved impressive results on the test set, with an accuracy of 93.52%, a precision of 94.65%, an F -score of 96.03%, and an AUC of 97.15%. The fusion of ensemble learning with PLMs and the Gompertz function, employing fuzzy rank-based methodology, introduces a novel prediction approach with prospects for enhancing accuracy and reliability. Additionally, the experimental results imply that training solely on textual content can yield high prediction accuracy, thereby providing valuable insights into the optimization of fake news detection systems. These findings not only aid in detecting misinformation but also have broader implications for the application of advanced deep learning techniques in public health policy and communication.