Postoperative UFT-/Tegafur-based Chemotherapy Versus Postoperative Radiotherapy for Early-stage Non-small Cell Lung Cancer：A Systematic Review and Network Meta-analysis

Background: Both of UFT-/Tegafur-based postoperative chemotherapy and postoperative radiotherapy have made large progress in treatment of early-stage non-small cell lung cancer. While it is unclear that, whether UFT-/Tegafur-based postoperative chemotherapy is superior to postoperative radiotherapy for early-stage non-small cell lung cancer with no direct evidence. Methods: Electronic databases (Pubmed, embase, cochrane library and clinicaltrials.gov) were searched to obtain relevant studies. This systematic review and meta-analysis is reported in accordance with the Preferred Items for Systematic Reviews and Meta-analysis (PRISMA) Statement and was registered at International Prospective Register of Systematic Reviews (number CRD42018095979). Sensitive analysis was conducted by excluding overweight studies. Funnel plot and egger’s test were performed to conduct publication bias. Results: Twenty-one randomized control trials were included. Our results suggested UFT-/Tegafur-based postoperative chemotherapy could improve overall survival over postoperative radiotherapy [HR=0.69 (0.59-0.80), p=0.000]. But subgroup analysis about stage showed there was no significant difference between them, no matter of stageⅠ,Ⅱ and Ⅲ. As to chemotherapy regime, both UFT-/Tegafur + platinum+vinca alkaloid [HR=0.68 (0.56-0.82), p=0.000] and UFT-/Tegafur only [HR=0.66 (0.54-0.79), p=0.000] were superior to radiotherapy. Subgroup analysis about radiotherapy delivery method and dose showed, significant improvement of chemotherapy over radiotherapy for Cobalt-60 only [HR=0.54 (0.39-0.75), p=0.000], Cobalt-60 and linac [HR=0.69 (0.59-0.81), p=0.000] and ≥45 Gy [HR=0.64 (0.54-0.75), p=0.000], but not for linac only [HR=0.78 (0.60-1.03), p=0.081] and ＜45 Gy [HR=0.86 (0.67-1.11), p=0.241]. Conclusion: UFT-/Tegafur-based postoperative chemotherapy was superior to postoperative radiotherapy for improving overall survival of early-stage non-small cell lung cancer, but it is not always so under certain circumstance, such as RT delivery method and radiation dose. Of course, it is imperative to further explore differences in specific stage, such as ⅠA and ⅠB

Integrated Information System for Sustainable Urban Regeneration

Information systems are widely used in urban planning process for communication between different side actors. However, most of them have been implemented without providing possibilities for decision makers to participate together with urban planners in the process. This research aims to outline a framework where an interactive model for decision making plays a key role in creating a collaborative environment. The proposal regards the information representation as the main instrument for encouraging a constructive dialog between different actors. The focus is on the relationship between three elements of information representation: level of detail, type of visualization and interaction. Combining these elements, information can be provided in a dynamic way enabling more effective exploration and understanding. The proposed strategy implements a digital model that operates on different scales and levels in order to support the key stages of the planning process for sustainable urban regeneration in Bulgaria. Positional approach is used to define the functionality and decision making operation for the selected process. As a result research ideas about the use of the digital model are presented

Inconsistent dialogue responses and how to recover from them

One critical issue for chat systems is to stay consistent about preferences, opinions, beliefs and facts of itself, which has been shown a difficult problem. In this work, we study methods to assess and bolster utterance consistency of chat systems. A dataset is first developed for studying the inconsistencies, where inconsistent dialogue responses, explanations of the inconsistencies, and recovery utterances are authored by annotators. This covers the life span of inconsistencies, namely introduction, understanding, and resolution. Building on this, we introduce a set of tasks centered on dialogue consistency, specifically focused on its detection and resolution. Our experimental findings indicate that our dataset significantly helps the progress in identifying and resolving conversational inconsistencies, and current popular large language models like ChatGPT which are good at resolving inconsistencies however still struggle with detection.Comment: Accepted in EACL 2024. Code and dataset available at https://github.com/mianzhang/CIDE

Discover, Explanation, Improvement: Automatic Slice Detection Framework for Natural Language Processing

Current natural language processing (NLP) models such as BERT and RoBERTa have achieved high overall performance, but they often make systematic errors due to bias or certain difficult features to learn. Thus research on slice detection models (SDM) which automatically identifies underperforming groups of datapoints has gradually caught more attention, which aims at both understanding model behaviors and providing insights for future model training and designing. However, there is little systematic research on SDM and quantitative evaluation of its assessment for NLP models. Our paper fills this gap by proposing "Discover, Explanation, Improvement" framework that discovers coherent and underperforming groups of datapoints and unites datapoints of each slice under human-understandable concepts; it also provides comprehensive evaluation tasks and the corresponding quantitative metrics, which enable convenient comparison for future works. Results show that our framework can accurately select error-prone datapoints with informative semantic features that summarize error patterns, based on which it directly boosts model performance by an average of 2.85 points based on trained models without tuning any parameters across multiple datasets.Comment: 15 pages, 5 figure

Collaborative decoding of critical tokens for boosting factuality of large language models

The most common training pipeline for large language models includes pretraining, finetuning and aligning phases, with their respective resulting models, such as the pretrained model and the finetuned model. Finetuned and aligned models show improved abilities of instruction following and safe generation, however their abilities to stay factual about the world are impacted by the finetuning process. Furthermore, the common practice of using sampling during generation also increases chances of hallucination. In this work, we introduce a collaborative decoding framework to harness the high factuality within pretrained models through the concept of critical tokens. We first design a critical token classifier to decide which model to use for the next token, and subsequently generates the next token using different decoding strategies. Experiments with different models and datasets show that our decoding framework is able to reduce model hallucination significantly, showcasing the importance of the collaborative decoding framework.Comment: work in progres

The Trickle-down Impact of Reward (In-)consistency on RLHF

Standard practice within Reinforcement Learning from Human Feedback (RLHF) involves optimizing against a Reward Model (RM), which itself is trained to reflect human preferences for desirable generations. A notable subject that is understudied is the (in-)consistency of RMs -- whether they can recognize the semantic changes to different prompts and appropriately adapt their reward assignments -- and their impact on the downstream RLHF model. In this paper, we visit a series of research questions relevant to RM inconsistency: (1) How can we measure the consistency of reward models? (2) How consistent are the existing RMs and how can we improve them? (3) In what ways does reward inconsistency influence the chatbots resulting from the RLHF model training? We propose Contrast Instructions -- a benchmarking strategy for the consistency of RM. Each example in Contrast Instructions features a pair of lexically similar instructions with different ground truth responses. A consistent RM is expected to rank the corresponding instruction and response higher than other combinations. We observe that current RMs trained with the standard ranking objective fail miserably on Contrast Instructions compared to average humans. To show that RM consistency can be improved efficiently without using extra training budget, we propose two techniques ConvexDA and RewardFusion, which enhance reward consistency through extrapolation during the RM training and inference stage, respectively. We show that RLHF models trained with a more consistent RM yield more useful responses, suggesting that reward inconsistency exhibits a trickle-down effect on the downstream RLHF process

Protective Effect of Heme Oxygenase-1 on High Glucose-Induced Pancreatic β-Cell Injury

BackgroundGlucose toxicity that is caused by chronic exposure to a high glucose concentration leads to islet dysfunction and induces apoptosis in pancreatic β-cells. Heme oxygenase-1 (HO-1) has been identified as an anti-apoptotic and cytoprotective gene. The purpose of this study is to investigate whether HO-1 up-regulation when using metalloprotophyrin (cobalt protoporphyrin, CoPP) could protect pancreatic β-cells from high glucose-induced apoptosis.MethodsReverse transcription-polymerase chain reaction was performed to analyze the CoPP-induced mRNA expression of HO-1. Cell viability of INS-1 cells cultured in the presence of CoPP was examined by acridine orange/propidium iodide staining. The generation of intracellular reactive oxygen species (ROS) was measured using flow cytometry. Glucose stimulated insulin secretion (GSIS) was determined following incubation with CoPP in different glucose concentrations.ResultsCoPP increased HO-1 mRNA expression in both a dose- and time-dependent manner. Overexpression of HO-1 inhibited caspase-3, and the number of dead cells in the presence of CoPP was significantly decreased when exposed to high glucose conditions (HG). CoPP also decreased the generation of intracellular ROS by 50% during 72 hours of culture with HG. However, decreased GSIS was not recovered even in the presence of CoPP.ConclusionOur data suggest that CoPP-induced HO-1 up-regulation results in protection from high glucose-induced apoptosis in INS-1 cells; however, glucose stimulated insulin secretion is not restored

Bilateral tension pneumothorax caused by an abrupt increase in airway pressure during cervical spine surgery in the prone position -A case report-

Elevated peak inspiratory airway pressure (PIP) can occur during general anesthesia and is usually easily rectified. In rare circumstances it can lead to potentially fatal conditions such as tension pneumothorax. We report on a 77-year-old male patient admitted for a cervical laminoplasty. The preoperative chest radiograph showed normal findings and there was no medical history of allergy or underlying airway inflammation. Anesthesia induction and maintenance progressed uneventfully. However, 5 minutes after prophylactic antibiotic administration, PIP suddenly increased and blood pressure dropped. The operation was abandoned and the patient was moved to a supine position to perform chest radiography. Cardiac arrest occurred, and cardiopulmonary resuscitation was performed. The radiograph showed bilateral tension pneumothorax. Needle aspiration was immediately performed, and chest tubes were inserted. Ventilation rapidly improved and the vital signs normalized. The patient was discharged without sequelae on postoperative day 36