The associations of CNR1 SNPs and haplotypes with vulnerability and treatment response phenotypes in Han Chinese with major depressive disorder:A case-control association study

BACKGROUND: Understanding how genetic polymorphisms are associated with the pathophysiology of major depressive disorder (MDD) may aid in diagnosis and the development of personalized treatment strategies. CNR1 is the gene coding Cannabinoid type 1 receptor which is highly involved in emotional processing and in regulating neurotransmitter releases. We aimed to investigate the associations of CNR1 single‐nucleotide polymorphisms (SNPs) with MDD susceptibility and treatment response. METHODS: The study reported data on 181 Han Chinese with MDD and 80 healthy controls. The associations of CNR1 genetic polymorphisms with MDD susceptibility and treatment response were examined, wherein the MDD patients were subgrouped further by responding to antidepressant treatment, compared with healthy controls separately. RESULTS: The CNR1 SNPs rs806367 and rs6454674 and haplotype C‐T‐T‐C of rs806366, rs806367, rs806368, and rs806370 were associated with increased susceptibility for MDD and antidepressant treatment resistance, but the association was not detected in other SNPs or the haplotype block of rs806368 and rs806370. CONCLUSION: The CNR1 is a promising candidate for the genetic association study of MDD. Larger and well‐characterized samples are required to confirm the genetic association of CNR1 with MDD because of the limitations such as relatively small sample size and lack of information for correcting confounding factors

Comparison of the perioperative outcomes between robotic-assisted thoracic surgery and video-assisted thoracic surgery in non-small cell lung cancer patients with different body mass index ranges

Background: Non-small cell lung cancer (NSCLC) is the most common malignancy and one of the most common causes of cancer-related death worldwide. Robotic-assisted thoracic surgery (RATS) has gradually become a prevalent surgical method for patients with NSCLC. Previous studies have found that body mass index (BMI) is associated with postoperative outcomes. This study aimed to investigate the effectiveness of RATS compared to video-assisted thoracic surgery (VATS) in the treatment of NSCLC with different BMI, in terms of perioperative outcomes.Methods: The baseline and perioperative data, including BMI, of 849 NSCLC patients who underwent minimally invasive anatomic lung resections from August 2020 to April 2021 were retrospectively collected and analyzed. Propensity score matching analysis was applied to minimize potential bias between the two groups (VATS and RATS), and the perioperative outcomes were compared. Subgroup analysis was subsequently performed.Results: Compared to VATS, RATS had more lymph nodes dissected 19 [inter-quartile range (11QR), 6-12] vs. 7 (IQR, 6-10), P<0.001), a lower estimated bleeding volume [40 (IQR, 30-50) vs. 50 (IQR, 40-60) mL, P<0.001], and other better postoperative outcomes, but a higher cost of hospitalization [(sic)83,626 (IQR, 77,211-92,686) vs. (sic)75,804 (IQR, 66,184-83,693), P<0.001]. Multivariable logistic regression analysis indicated that RATS (P=0.027) and increasing BMI (P=0.030) were associated with a statistically significant reduction in the risk of postoperative complications. Subgroup analysis indicated that the advantages of RATS may be more obvious in patients with a BMI of 24-28 kg/m(2), in which the RATS group had more lymph nodes dissected [9 (IQR, 6-12) vs. 7 (IQR, 5-10), P<0.001] and a decreased risk of total postoperative complications [odds ratio (OR), 0.443; 95% confidence interval (CI), 0.212-0.924; P=0.030] compared to the VATS group.Conclusions: Both, RATS and VATS can be safely applied for patients with NSCLC. Perioperative outcome parameters indicate advantages for RATS, however at a higher cost of hospitalization. The advantages of RATS might be more obvious in patients with a BMI of 24-28 kg/m(2)

ChatKBQA: A Generate-then-Retrieve Framework for Knowledge Base Question Answering with Fine-tuned Large Language Models

Knowledge Base Question Answering (KBQA) aims to derive answers to natural language questions over large-scale knowledge bases (KBs), which are generally divided into two research components: knowledge retrieval and semantic parsing. However, three core challenges remain, including inefficient knowledge retrieval, retrieval errors adversely affecting semantic parsing, and the complexity of previous KBQA methods. In the era of large language models (LLMs), we introduce ChatKBQA, a novel generate-then-retrieve KBQA framework built on fine-tuning open-source LLMs such as Llama-2, ChatGLM2 and Baichuan2. ChatKBQA proposes generating the logical form with fine-tuned LLMs first, then retrieving and replacing entities and relations through an unsupervised retrieval method, which improves both generation and retrieval more straightforwardly. Experimental results reveal that ChatKBQA achieves new state-of-the-art performance on standard KBQA datasets, WebQSP, and ComplexWebQuestions (CWQ). This work also provides a new paradigm for combining LLMs with knowledge graphs (KGs) for interpretable and knowledge-required question answering. Our code is publicly available.Comment: Preprin

A Survey on Diffusion Models for Time Series and Spatio-Temporal Data

The study of time series is crucial for understanding trends and anomalies over time, enabling predictive insights across various sectors. Spatio-temporal data, on the other hand, is vital for analyzing phenomena in both space and time, providing a dynamic perspective on complex system interactions. Recently, diffusion models have seen widespread application in time series and spatio-temporal data mining. Not only do they enhance the generative and inferential capabilities for sequential and temporal data, but they also extend to other downstream tasks. In this survey, we comprehensively and thoroughly review the use of diffusion models in time series and spatio-temporal data, categorizing them by model category, task type, data modality, and practical application domain. In detail, we categorize diffusion models into unconditioned and conditioned types and discuss time series and spatio-temporal data separately. Unconditioned models, which operate unsupervised, are subdivided into probability-based and score-based models, serving predictive and generative tasks such as forecasting, anomaly detection, classification, and imputation. Conditioned models, on the other hand, utilize extra information to enhance performance and are similarly divided for both predictive and generative tasks. Our survey extensively covers their application in various fields, including healthcare, recommendation, climate, energy, audio, and transportation, providing a foundational understanding of how these models analyze and generate data. Through this structured overview, we aim to provide researchers and practitioners with a comprehensive understanding of diffusion models for time series and spatio-temporal data analysis, aiming to direct future innovations and applications by addressing traditional challenges and exploring innovative solutions within the diffusion model framework.Comment: Ongoing work & Under review; 27 pages, 8 figures, 2 tables; Github Repo: https://github.com/yyysjz1997/Awesome-TimeSeries-SpatioTemporal-Diffusion-Mode

Distinguishing EGFR mutant subtypes in stage IA non-small cell lung cancer using the presence status of ground glass opacity and final histologic classification: a systematic review and meta-analysis

BackgroundThe progression of early stage non-small cell lung cancer (NSCLC) is closely related to epidermal growth factor receptor (EGFR) mutation status. The purpose of this study was to systematically investigate the relationship between EGFR mutation status and demographic, imaging, and ultimately pathologic features in patients with NSCLC.MethodsA complete literature search was conducted using the PubMed, Web of Science, EMBASE, and Cochrane Library databases to discover articles published by May 15, 2023 that were eligible. The relationship between EGFR mutation status and specific demographic, imaging, and ultimately pathologic features in patients with NSCLC was evaluated using pooled odds ratios (ORs) and their 95% confidence intervals (CIs). The standardized mean difference (SMD) with 95% CIs was the appropriate statistic to summarize standard deviations (SDs) means for continuous variables.ResultsA total of 9 studies with 1789 patients were included in this analysis. The final findings suggested that patients with a greater age, female gender, and non-smoking status would have a relatively higher incidence of EGFR mutations. Additionally, the risk of EGFR mutations increased with larger tumor diameter, tumor imaging presentation of mixed ground glass opacity (mGGO), and tumor pathological findings of minimally invasive adenocarcinoma (MIA) or invasive adenocarcinoma (IAC). Significantly, malignancies presenting as MIA are more likely to contain L858R point mutations (OR = 1.80; 95% CI: 1.04–3.13; p = 0.04) rather than exon 19 deletions (OR = 1.81; 95% CI: 0.95–3.44; p = 0.07).ConclusionThis meta-analysis showed that imaging parameters and histological classifications of pulmonary nodules may be able to predict stage IA NSCLC genetic changes

SciMMIR:Benchmarking Scientific Multi-modal Information Retrieval

Multi-modal information retrieval (MMIR) is a rapidly evolving field, where significant progress, particularly in image-text pairing, has been made through advanced representation learning and cross-modality alignment research. However, current benchmarks for evaluating MMIR performance in image-text pairing within the scientific domain show a notable gap, where chart and table images described in scholarly language usually do not play a significant role. To bridge this gap, we develop a specialised scientific MMIR (SciMMIR) benchmark by leveraging open-access paper collections to extract data relevant to the scientific domain. This benchmark comprises 530K meticulously curated image-text pairs, extracted from figures and tables with detailed captions in scientific documents. We further annotate the image-text pairs with two-level subset-subcategory hierarchy annotations to facilitate a more comprehensive evaluation of the baselines. We conducted zero-shot and fine-tuning evaluations on prominent multi-modal image-captioning and visual language models, such as CLIP and BLIP. Our analysis offers critical insights for MMIR in the scientific domain, including the impact of pre-training and fine-tuning settings and the influence of the visual and textual encoders. All our data and checkpoints are publicly available at https://github.com/Wusiwei0410/SciMMIR

SciMMIR: Benchmarking Scientific Multi-modal Information Retrieval

Multi-modal information retrieval (MMIR) is a rapidly evolving field, where significant progress, particularly in image-text pairing, has been made through advanced representation learning and cross-modality alignment research. However, current benchmarks for evaluating MMIR performance in image-text pairing within the scientific domain show a notable gap, where chart and table images described in scholarly language usually do not play a significant role. To bridge this gap, we develop a specialised scientific MMIR (SciMMIR) benchmark by leveraging open-access paper collections to extract data relevant to the scientific domain. This benchmark comprises 530K meticulously curated image-text pairs, extracted from figures and tables with detailed captions in scientific documents. We further annotate the image-text pairs with two-level subset-subcategory hierarchy annotations to facilitate a more comprehensive evaluation of the baselines. We conducted zero-shot and fine-tuning evaluations on prominent multi-modal image-captioning and visual language models, such as CLIP and BLIP. Our analysis offers critical insights for MMIR in the scientific domain, including the impact of pre-training and fine-tuning settings and the influence of the visual and textual encoders. All our data and checkpoints are publicly available at https://github.com/Wusiwei0410/SciMMIR.Comment: camera-ready version for ACL 2024 Finding

Permo-Triassic detrital records of South China and implications for the Indosinian events in East Asia

This work was supported by the National Natural Science Foundation of China (Grant No. 41602105, 41672106 and 41530966) and China Postdoctoral Science Foundation (Grant No. 2016M590655), the Fundamental Research Funds for the Central Universities, Ocean University of China. Peter Cawood acknowledges support from the Australian Research Council grant FL160100168.Provenance analyses of Lower to Middle Triassic strata from the Greater Youjiang Basin along with the Permian strata of Hainan Island, provide a record of the collisional assembly of the South China Craton and Indochina Block and their incorporation into Asia. Detrital zircons from Lower and Middle Triassic samples show similar overall age spectra ranging from Archean to Triassic with major age groups at 300–250 Ma, 480–420 Ma, and 1200–900 Ma, as well as at 400–300 Ma in one Triassic sample. Permian siltstones from Hainan Island, to the southeast of the Greater Youjiang Basin, record different age spectra with major age groups at 400–300 Ma and 530–420 Ma and subordinate components at 1200–900 Ma and 1900–1700 Ma. These age data in combination with available paleocurrent data and regional geological relations suggest that Precambrian detrital zircons were derived from the Precambrian basement or recycled from the overlying early Paleozoic sedimentary rocks that contain Precambrian detritus. Early Paleozoic detrital zircons were derived from igneous rocks in the South China Craton. Devonian-Triassic detrital zircons in the Triassic strata were likely sourced from coeval magmatic activity related to closure of Paleo-Tethys branch ocean that lay to the southwest, whereas 400–300 Ma detrital zircons in the Permian siltstones of Hainan Island were likely derived from a Paleozoic magmatic arc source that extended along the eastern-southeastern margin of China from Hainan Island to Japan in response to subduction of the Paleo-Pacific oceanic crust. Detrital zircon, trace element, and sandstone modal data for Permo-Triassic strata from the Greater Youjiang Basin indicate that the basin evolved from a trailing-edge passive margin setting to a peripheral foreland basin during closure of the Paleo-Tethys Ocean and collision between Indochina and South China. The initiation time of the foreland basin decreases from southeast to southwest across the basin, probably reflecting oblique collision. In contrast, the Permian strata on Hainan Island record a provenance history distinct from the Greater Youjiang Basin, which is related to late Paleozoic to Mesozoic subduction of the Paleo-Pacific Plate beneath South China.PostprintPeer reviewe