CaSR Induces Osteoclast Differentiation and Promotes Bone Metastasis in Lung Adenocarcinoma

Objective: Explore the mechanism of CaSR's involvement in bone metastasis in lung adenocarcinoma. Methods: Immunohistochemistry (IHC) was used to detect the expression of calcium-sensing receptor (CaSR) in 120 cases of lung adenocarcinoma with bone metastasis. Stably transfected cell lines with CaSR overexpression and knockdown based on A549 cells were constructed. The expression of CaSR was verified by western blot and qPCR. The proliferation and migration abilities of A549 cells were tested using cholecystokinin-8 (CCK-8) and Transwell assays, respectively. Western blotting was used to detect the expression of matrix metalloproteinases MMP2, MMP9, CaSR, and NF-κB. The supernatant from each cell culture group was collected as a conditional co-culture solution to study the induction of osteoclast precursor cells and osteoblasts. Western blot and qPCR were used to validate the expression of bone matrix degradation-related enzymes cathepsin K and hormone calcitonin receptor (CTR) and osteoblast-induced osteoclast maturation and differentiation enzyme receptor activator of nuclear factor-κB ligand (RANKL), macrophage colony-stimulating factor (M-CSF), osteoprotegerin (OPG), and PTHrP. Immunofluorescent staining was used to detect F-actin ring formation and osteocalcin expression. Western blot results for NF-κB expression identified a regulatory relationship between NF-κB and CaSR. Results: CaSR expression in lung cancer tissues was significantly higher than that in adjacent and normal lung tissues. The expression of CaSR in lung cancer tissues with bone metastasis was higher than that in non-metastatic lung cancer tissues. The proliferation and migration ability of A549 cells increased significantly with overexpressed CaSR. The co-culture solution directly induced osteoclast precursor cells and the expression of bone matrix degradation-related enzymes significantly increased. Osteoblasts were significantly inhibited and osteoblast-induced osteoclast maturation and differentiation enzymes were significantly downregulated. It was found that the expression of NF-κB and PTHrP increased when CaSR was overexpressed. Osteoclast differentiation factor expression was also significantly increased, which directly induces osteoclast differentiation and maturation. These results were reversed when CaSR was knocked down. Conclusions: CaSR can positively regulate NF-κB and PTHrP expression in A549 cells with a high metastatic potential, thereby promoting osteoclast differentiation and maturation, and facilitating the occurrence and development of bone metastasis in lung adenocarcinoma

Meta-KD: A Meta Knowledge Distillation Framework for Language Model Compression across Domains

Pre-trained language models have been applied to various NLP tasks with considerable performance gains. However, the large model sizes, together with the long inference time, limit the deployment of such models in real-time applications. One line of model compression approaches considers knowledge distillation to distill large teacher models into small student models. Most of these studies focus on single-domain only, which ignores the transferable knowledge from other domains. We notice that training a teacher with transferable knowledge digested across domains can achieve better generalization capability to help knowledge distillation. Hence we propose a Meta-Knowledge Distillation (Meta-KD) framework to build a meta-teacher model that captures transferable knowledge across domains and passes such knowledge to students. Specifically, we explicitly force the meta-teacher to capture transferable knowledge at both instance-level and feature-level from multiple domains, and then propose a meta-distillation algorithm to learn single-domain student models with guidance from the meta-teacher. Experiments on public multi-domain NLP tasks show the effectiveness and superiority of the proposed Meta-KD framework. Further, we also demonstrate the capability of Meta-KD in the settings where the training data is scarce

Chinese CLIP: Contrastive Vision-Language Pretraining in Chinese

The tremendous success of CLIP (Radford et al., 2021) has promoted the research and application of contrastive learning for vision-language pretraining. In this work, we construct a large-scale dataset of image-text pairs in Chinese, where most data are retrieved from publicly available datasets, and we pretrain Chinese CLIP models on the new dataset. We develop 5 Chinese CLIP models of multiple sizes, spanning from 77 to 958 million parameters. Furthermore, we propose a two-stage pretraining method, where the model is first trained with the image encoder frozen and then trained with all parameters being optimized, to achieve enhanced model performance. Our comprehensive experiments demonstrate that Chinese CLIP can achieve the state-of-the-art performance on MUGE, Flickr30K-CN, and COCO-CN in the setups of zero-shot learning and finetuning, and it is able to achieve competitive performance in zero-shot image classification based on the evaluation on the ELEVATER benchmark (Li et al., 2022). We have released our codes, models, and demos in https://github.com/OFA-Sys/Chinese-CLI

Seeing the arrow of time

URL to conference programWe explore whether we can observe Time’s Arrow in a temporal sequence–is it possible to tell whether a video is running forwards or backwards? We investigate this somewhat philosophical question using computer vision and machine learning techniques. We explore three methods by which we might detect Time’s Arrow in video sequences, based on distinct ways in which motion in video sequences might be asymmetric in time. We demonstrate good video forwards /backwards classification results on a selection of YouTube video clips, and on natively-captured sequences (with no temporally-dependent video compression), and examine what motions the models have learned that help discriminate forwards from backwards time.European Research Council (ERC grant VisRec no. 228180)National Basic Research Program of China (973 Program) (2013CB329503)National Natural Science Foundation (China) (NSFC Grant no. 91120301)United States. Office of Naval Research (ONR MURI grant N00014-09-1-1051)National Science Foundation (U.S.) (NSF CGV-1111415

Application of machine learning algorithms to construct and validate a prediction model for coronary heart disease risk in patients with periodontitis: a population-based study

BackgroundThe association between periodontitis and cardiovascular disease is increasingly recognized. In this research, a prediction model utilizing machine learning (ML) was created and verified to evaluate the likelihood of coronary heart disease in individuals affected by periodontitis.MethodsWe conducted a comprehensive analysis of data obtained from the National Health and Nutrition Examination Survey (NHANES) database, encompassing the period between 2009 and 2014.This dataset comprised detailed information on a total of 3,245 individuals who had received a confirmed diagnosis of periodontitis. Subsequently, the dataset was randomly partitioned into a training set and a validation set at a ratio of 6:4. As part of this study, we conducted weighted logistic regression analyses, both univariate and multivariate, to identify risk factors that are independent predictors for coronary heart disease in individuals who have periodontitis. Five different machine learning algorithms, namely Logistic Regression (LR), Gradient Boosting Machine (GBM), Support Vector Machine (SVM), K-Nearest Neighbor (KNN), and Classification and Regression Tree (CART), were utilized to develop the model on the training set. The evaluation of the prediction models’ performance was conducted on both the training set and validation set, utilizing metrics including AUC (Area under the receiver operating characteristic curve), Brier score, calibration plot, and decision curve analysis (DCA). Additionally, a graphical representation called a nomogram was created using logistic regression to visually depict the predictive model.ResultsThe factors that were found to independently contribute to the risk, as determined by both univariate and multivariate logistic regression analyses, encompassed age, race, presence of myocardial infarction, chest pain status, utilization of lipid-lowering medications, levels of serum uric acid and serum creatinine. Among the five evaluated machine learning models, the KNN model exhibited exceptional accuracy, achieving an AUC value of 0.977. The calibration plot and brier score illustrated the model's ability to accurately estimate probabilities. Furthermore, the model's clinical applicability was confirmed by DCA.ConclusionOur research showcases the effectiveness of machine learning algorithms in forecasting the likelihood of coronary heart disease in individuals with periodontitis, thereby aiding healthcare professionals in tailoring treatment plans and making well-informed clinical decisions

Qwen Technical Report

Large language models (LLMs) have revolutionized the field of artificial intelligence, enabling natural language processing tasks that were previously thought to be exclusive to humans. In this work, we introduce Qwen, the first installment of our large language model series. Qwen is a comprehensive language model series that encompasses distinct models with varying parameter counts. It includes Qwen, the base pretrained language models, and Qwen-Chat, the chat models finetuned with human alignment techniques. The base language models consistently demonstrate superior performance across a multitude of downstream tasks, and the chat models, particularly those trained using Reinforcement Learning from Human Feedback (RLHF), are highly competitive. The chat models possess advanced tool-use and planning capabilities for creating agent applications, showcasing impressive performance even when compared to bigger models on complex tasks like utilizing a code interpreter. Furthermore, we have developed coding-specialized models, Code-Qwen and Code-Qwen-Chat, as well as mathematics-focused models, Math-Qwen-Chat, which are built upon base language models. These models demonstrate significantly improved performance in comparison with open-source models, and slightly fall behind the proprietary models.Comment: 59 pages, 5 figure

The Human Activity Radar Challenge: benchmarking based on the ‘Radar signatures of human activities’ dataset from Glasgow University

Radar is an extremely valuable sensing technology for detecting moving targets and measuring their range, velocity, and angular positions. When people are monitored at home, radar is more likely to be accepted by end-users, as they already use WiFi, is perceived as privacy-preserving compared to cameras, and does not require user compliance as wearable sensors do. Furthermore, it is not affected by lighting condi-tions nor requires artificial lights that could cause discomfort in the home environment. So, radar-based human activities classification in the context of assisted living can empower an aging society to live at home independently longer. However, challenges remain as to the formulation of the most effective algorithms for radar-based human activities classification and their validation. To promote the exploration and cross-evaluation of different algorithms, our dataset released in 2019 was used to benchmark various classification approaches. The challenge was open from February 2020 to December 2020. A total of 23 organizations worldwide, forming 12 teams from academia and industry, participated in the inaugural Radar Challenge, and submitted 188 valid entries to the challenge. This paper presents an overview and evaluation of the approaches used for all primary contributions in this inaugural challenge. The proposed algorithms are summarized, and the main parameters affecting their performances are analyzed