Revisiting Parallel Context Windows: A Frustratingly Simple Alternative and Chain-of-Thought Deterioration

We identify two crucial limitations in the evaluation of recent parallel-integrated method Parallel Context Windows (PCW), which extends the maximum context lengths of language models, e.g., 2048 for LLaMA, by harnessing window-wise attention and positional embedding techniques. We first show that a simple yet strong baseline, weighted sum ensemble, is missing for the in-context few-shot classification. Moreover, on more challenging Chain-of-Thought (CoT) reasoning (e.g., HotpotQA), PCW would present unexpected deterioration regarding question miscomprehension and false inference. Based on our findings, we suggest that the existing PCW design may not guarantee sufficient improvement and practicality in handling lengthy documents in real-world applications. More community efforts on enabling language models' long context understanding ability should be paid

RNA methylation pattern and immune microenvironment characteristics mediated by m6A regulator in ischemic stroke

Background: Ischemic stroke (IS) is a highly heterogeneous disease. Recent studies have shown that epigenetic variables affect the immune response. However, only a few studies have examined the relationship between IS and m6A immunoregulation. Therefore, we aim to explore the methylation of RNA mediated by m6A regulatory factor and the immune microenvironment characteristics of IS.Methods: Differentially expressed m6A regulators were detected in IS microarray datasets GSE22255 and GSE58294. We used a series of machine learning algorithms to identify key IS-related m6A regulators and validated them on blood samples of IS patients, oxygen-glucose deprivation/reoxygenation (OGD/R) microglia and GSE198710 independent data sets. Different m6A modification modes were determined and the patients were classified. In addition, we systematically associate these modification patterns with the characteristics of immune microenvironment, including infiltrating immune cells, immune function genes and immune response genes. Then we developed a model of m6A score to quantify the m6A modification in IS samples.Results: Through the analysis of the differences between the control group and IS patients, METTL16, LRPPRC, and RBM15 showed strong diagnostic significance in three independent data sets. In addition, qRT-PCR and Western blotting also confirmed that the expression of METTL16 and LRPPRC was downregulated and the expression of RBM15 was upregulated after ischemia. Two m6A modification modes and two m6A gene modification modes were also identified. m6A gene cluster A (high m6A value group) was positively correlated with acquired immunity, while m6A gene cluster B (low m6A value group) was positively correlated with innate immunity. Similarly, five immune-related hub genes were significantly associated with m6Acore (CD28, IFNG, LTF, LCN2, and MMP9).Conclusion: The modification of m6A is closely related to the immune microenvironment. The evaluation of individual m6A modification pattern may be helpful for future immunomodulatory therapy of anti-ischemic response

FetusMapV2: Enhanced Fetal Pose Estimation in 3D Ultrasound

Fetal pose estimation in 3D ultrasound (US) involves identifying a set of associated fetal anatomical landmarks. Its primary objective is to provide comprehensive information about the fetus through landmark connections, thus benefiting various critical applications, such as biometric measurements, plane localization, and fetal movement monitoring. However, accurately estimating the 3D fetal pose in US volume has several challenges, including poor image quality, limited GPU memory for tackling high dimensional data, symmetrical or ambiguous anatomical structures, and considerable variations in fetal poses. In this study, we propose a novel 3D fetal pose estimation framework (called FetusMapV2) to overcome the above challenges. Our contribution is three-fold. First, we propose a heuristic scheme that explores the complementary network structure-unconstrained and activation-unreserved GPU memory management approaches, which can enlarge the input image resolution for better results under limited GPU memory. Second, we design a novel Pair Loss to mitigate confusion caused by symmetrical and similar anatomical structures. It separates the hidden classification task from the landmark localization task and thus progressively eases model learning. Last, we propose a shape priors-based self-supervised learning by selecting the relatively stable landmarks to refine the pose online. Extensive experiments and diverse applications on a large-scale fetal US dataset including 1000 volumes with 22 landmarks per volume demonstrate that our method outperforms other strong competitors.Comment: 16 pages, 11 figures, accepted by Medical Image Analysis(2023

Segment Anything Model for Medical Images?

The Segment Anything Model (SAM) is the first foundation model for general image segmentation. It designed a novel promotable segmentation task, ensuring zero-shot image segmentation using the pre-trained model via two main modes including automatic everything and manual prompt. SAM has achieved impressive results on various natural image segmentation tasks. However, medical image segmentation (MIS) is more challenging due to the complex modalities, fine anatomical structures, uncertain and complex object boundaries, and wide-range object scales. SAM has achieved impressive results on various natural image segmentation tasks. Meanwhile, zero-shot and efficient MIS can well reduce the annotation time and boost the development of medical image analysis. Hence, SAM seems to be a potential tool and its performance on large medical datasets should be further validated. We collected and sorted 52 open-source datasets, and build a large medical segmentation dataset with 16 modalities, 68 objects, and 553K slices. We conducted a comprehensive analysis of different SAM testing strategies on the so-called COSMOS 553K dataset. Extensive experiments validate that SAM performs better with manual hints like points and boxes for object perception in medical images, leading to better performance in prompt mode compared to everything mode. Additionally, SAM shows remarkable performance in some specific objects and modalities, but is imperfect or even totally fails in other situations. Finally, we analyze the influence of different factors (e.g., the Fourier-based boundary complexity and size of the segmented objects) on SAM's segmentation performance. Extensive experiments validate that SAM's zero-shot segmentation capability is not sufficient to ensure its direct application to the MIS.Comment: 23 pages, 14 figures, 12 table

AgentBench: Evaluating LLMs as Agents

Large Language Models (LLMs) are becoming increasingly smart and autonomous, targeting real-world pragmatic missions beyond traditional NLP tasks. As a result, there has been an urgent need to evaluate LLMs as agents on challenging tasks in interactive environments. We present AgentBench, a multi-dimensional evolving benchmark that currently consists of 8 distinct environments to assess LLM-as-Agent's reasoning and decision-making abilities in a multi-turn open-ended generation setting. Our extensive test over 27 API-based and open-sourced (OSS) LLMs shows that, while top commercial LLMs present a strong ability of acting as agents in complex environments, there is a significant disparity in performance between them and OSS competitors. We identify the typical reasons of failures in environments and LLMs, showing that poor long-term reasoning, decision-making, and instruction following abilities are the main obstacles for developing usable LLM agents. Training on code and high quality multi-turn alignment data could improve agent performance. Datasets, environments, and an integrated evaluation package for AgentBench are released at \url{https://github.com/THUDM/AgentBench}.Comment: 55 page

Claudin18.2 as a potential therapeutic target for primary ovarian mucinous carcinomas and metastatic ovarian mucinous carcinomas from upper gastrointestinal primary tumours

Abstract Background The vast majority of ovarian mucinous carcinomas are metastatic tumours derived from nonovarian primary cancers, typically gastrointestinal neoplasms. Therapy targeting claudin18.2 might be used in gastric, gastroesophageal junction and pancreatic cancers with high expression of claudin18.2. In this study, we aimed to profile the expression of claudin18.2 in primary ovarian mucinous carcinoma (POMC) and metastatic gastrointestinal mucinous carcinoma (MGMC). Methods Immunohistochemistry was used to detect claudin 18.2 expression in whole tissue sections of ovarian mucinous carcinomas, including 32 POMCs and 44 MGMCs, 23 of which were derived from upper gastrointestinal primary tumours and 21 of which were derived from lower gastrointestinal primary tumours. Immunohistochemical studies for claudin18.2, SATB2, PAX8, CK7 and CK20 were performed in all 76 cases. Results Among 76 primary and metastatic mucinous carcinomas, claudin18.2 was expressed in 56.6% (43/76) of cases. MGMCs from the upper gastrointestinal tract, including 22 derived from primary stomach tumours and one derived from a pancreas tumour, were positive for claudin 18.2 in 69.5% (16/23) of cases. MGMCs from the lower gastrointestinal tract, including 10 derived from primary appendiceal cancer and 11 derived from colorectal cancers, showed no claudin18.2 expression (0/21). The expression rate of claudin18.2 in primary ovarian mucinous neoplasms, including 22 primary ovarian mucinous carcinomas and 10 primary ovarian borderline mucinous tumours, was 84.4% (27/32). The common immunophenotypic characteristics of POMCs, upper gastrointestinal tract-derived MGMCs, and lower gastrointestinal tract-derived MGMCs were claudin18.2 + /PAX8 + /SATB2- (17/32), claudin18.2 + /PAX8-/SATB2- (16/23) and claudin18.2-/PAX8-/SATB2 + (19/21), respectively. Conclusion Claudin18.2 is highly expressed in POMCs and MGMCs derived from upper gastrointestinal tract primary tumours; therefore, claudin18.2-targeted therapy might serve as a potential therapeutic strategy for POMCs and MGMCs from the upper gastrointestinal tract

DataSheet1_RNA methylation pattern and immune microenvironment characteristics mediated by m6A regulator in ischemic stroke.docx

Background: Ischemic stroke (IS) is a highly heterogeneous disease. Recent studies have shown that epigenetic variables affect the immune response. However, only a few studies have examined the relationship between IS and m6A immunoregulation. Therefore, we aim to explore the methylation of RNA mediated by m6A regulatory factor and the immune microenvironment characteristics of IS.Methods: Differentially expressed m6A regulators were detected in IS microarray datasets GSE22255 and GSE58294. We used a series of machine learning algorithms to identify key IS-related m6A regulators and validated them on blood samples of IS patients, oxygen-glucose deprivation/reoxygenation (OGD/R) microglia and GSE198710 independent data sets. Different m6A modification modes were determined and the patients were classified. In addition, we systematically associate these modification patterns with the characteristics of immune microenvironment, including infiltrating immune cells, immune function genes and immune response genes. Then we developed a model of m6A score to quantify the m6A modification in IS samples.Results: Through the analysis of the differences between the control group and IS patients, METTL16, LRPPRC, and RBM15 showed strong diagnostic significance in three independent data sets. In addition, qRT-PCR and Western blotting also confirmed that the expression of METTL16 and LRPPRC was downregulated and the expression of RBM15 was upregulated after ischemia. Two m6A modification modes and two m6A gene modification modes were also identified. m6A gene cluster A (high m6A value group) was positively correlated with acquired immunity, while m6A gene cluster B (low m6A value group) was positively correlated with innate immunity. Similarly, five immune-related hub genes were significantly associated with m6Acore (CD28, IFNG, LTF, LCN2, and MMP9).Conclusion: The modification of m6A is closely related to the immune microenvironment. The evaluation of individual m6A modification pattern may be helpful for future immunomodulatory therapy of anti-ischemic response.</p

Segment anything model for medical images?

The Segment Anything Model (SAM) is the first foundation model for general image segmentation. It has achieved impressive results on various natural image segmentation tasks. However, medical image segmentation (MIS) is more challenging because of the complex modalities, fine anatomical structures, uncertain and complex object boundaries, and wide-range object scales. To fully validate SAM's performance on medical data, we collected and sorted 53 open-source datasets and built a large medical segmentation dataset with 18 modalities, 84 objects, 125 object-modality paired targets, 1050K 2D images, and 6033K masks. We comprehensively analyzed different models and strategies on the so-called COSMOS 1050K dataset. Our findings mainly include the following: (1) SAM showed remarkable performance in some specific objects but was unstable, imperfect, or even totally failed in other situations. (2) SAM with the large ViT-H showed better overall performance than that with the small ViT-B. (3) SAM performed better with manual hints, especially box, than the Everything mode. (4) SAM could help human annotation with high labeling quality and less time. (5) SAM was sensitive to the randomness in the center point and tight box prompts, and may suffer from a serious performance drop. (6) SAM performed better than interactive methods with one or a few points, but will be outpaced as the number of points increases. (7) SAM's performance correlated to different factors, including boundary complexity, intensity differences, etc. (8) Finetuning the SAM on specific medical tasks could improve its average DICE performance by 4.39% and 6.68% for ViT-B and ViT-H, respectively. Codes and models are available at: https://github.com/yuhoo0302/Segment-Anything-Model-for-Medical-Images. We hope that this comprehensive report can help researchers explore the potential of SAM applications in MIS, and guide how to appropriately use and develop SAM.</p