Ginkgetin aglycone exerts anti-osteoporotic effect via regulation of NOX4/Akt/PI3K pathway

Purpose: To investigate the protective effect of Ginkgetin aglycone (GA) on ovariectomy-induced osteoporosis in rats, as well as the mechanism of action involved. Methods: Adult female Wistar rats (n = 40) were separated into four group: normal control, ovariectomy (OVR), 100 mg GA/kg dose, and 200 mg GA/kg dose. The rats were ovariectomized using standard procedures, except for those in normal control group. Rats in the two treatment groups received 100 or 200 mg GA/kg orally for a period of 12 weeks. Biochemical assays were performed on the urine and blood. Markers of bone formation and mediators of inflammation were assessed. Bone microarchitectural changes were examined using micro-CT scanner, while Western blotting was used to determine the expressions of NOX4, NF-κB p65, PI3K, Akt and JNK proteins in rat femurs. Results: Phosphorus and calcium levels in the serum varied among different groups. Levels of calcium, phosphorus and creatinine decreased (p < 0.01) significantly to a greater extent in the urine of GA group than in that of OVR group (p < 0.05). Interleukin-1β (IL-1β), tumor necrosis factor α (TNF-α) and osteocalcin (OC) levels and the activity of alkaline phosphatase (ALP) decreased more in GA group than in OVR group. In GA-treated group, bone mineral density (BMD) was enhanced in a dose dependent manner than OVR group (p < 0.05). Treatment with GA ameliorated altered bone microarchitecture in OVR rats. Treatment of osteoporotic rats with GA led to significant and dosedependent decrease in the expressions of JNK, NOX4, NF-κB p65 and PI3K, and (p < 0.05) increase in the expression of Akt in femur tissue. Conclusion: In conclusion, result of study proves the anti-osteoporotic activity of GA is exerted via regulation of NOX4/PI3K/Akt pathway

An Empirical Investigation of Domain Adaptation Ability for Chinese Spelling Check Models

Chinese Spelling Check (CSC) is a meaningful task in the area of Natural Language Processing (NLP) which aims at detecting spelling errors in Chinese texts and then correcting these errors. However, CSC models are based on pretrained language models, which are trained on a general corpus. Consequently, their performance may drop when confronted with downstream tasks involving domain-specific terms. In this paper, we conduct a thorough evaluation about the domain adaption ability of various typical CSC models by building three new datasets encompassing rich domain-specific terms from the financial, medical, and legal domains. Then we conduct empirical investigations in the corresponding domain-specific test datasets to ascertain the cross-domain adaptation ability of several typical CSC models. We also test the performance of the popular large language model ChatGPT. As shown in our experiments, the performances of the CSC models drop significantly in the new domains.Comment: ICASSP202

Improving Fine-grained Entity Typing with Entity Linking

Fine-grained entity typing is a challenging problem since it usually involves a relatively large tag set and may require to understand the context of the entity mention. In this paper, we use entity linking to help with the fine-grained entity type classification process. We propose a deep neural model that makes predictions based on both the context and the information obtained from entity linking results. Experimental results on two commonly used datasets demonstrates the effectiveness of our approach. On both datasets, it achieves more than 5\% absolute strict accuracy improvement over the state of the art.Comment: EMNLP 201

ConsistentEE: A Consistent and Hardness-Guided Early Exiting Method for Accelerating Language Models Inference

Early Exiting is one of the most popular methods to achieve efficient inference. Current early exiting methods adopt the (weighted) sum of the cross entropy loss of all internal classifiers during training, imposing all these classifiers to predict all instances correctly. However, during inference, as long as one internal classifier predicts an instance correctly, it can accelerate without losing accuracy. Thus, there is a notable gap between training and inference. We propose ConsistentEE, an early exiting method that is consistent in training and inference. ConsistentEE formulates the early exiting process as a reinforcement learning problem. A policy network is added to decide whether an instance should exit or continue. The training objective of ConsistentEE only require each instance to be predicted correctly by one internal classifier. Additionally, we introduce the concept Memorize Layer to measure the hardness of an instance. We incorporate memorized layer into reward function design, which allows "easy" instances to focus more on acceleration while "hard" instances to focus more on accuracy. Experimental results show that our method outperforms other baselines on various natural language understanding and generation tasks.Comment: Accepted in AAAI2

Genetic variants in ELOVL2 and HSD17B12 predict melanoma‐specific survival

Fatty acids play a key role in cellular bioenergetics, membrane biosynthesis and intracellular signaling processes and thus may be involved in cancer development and progression. In the present study, we comprehensively assessed associations of 14,522 common single‐nucleotide polymorphisms (SNPs) in 149 genes of the fatty‐acid synthesis pathway with cutaneous melanoma disease‐specific survival (CMSS). The dataset of 858 cutaneous melanoma (CM) patients from a published genome‐wide association study (GWAS) by The University of Texas M.D. Anderson Cancer Center was used as the discovery dataset, and the identified significant SNPs were validated by a dataset of 409 CM patients from another GWAS from the Nurses’ Health and Health Professionals Follow‐up Studies. We found 40 noteworthy SNPs to be associated with CMSS in both discovery and validation datasets after multiple comparison correction by the false positive report probability method, because more than 85% of the SNPs were imputed. By performing functional prediction, linkage disequilibrium analysis, and stepwise Cox regression selection, we identified two independent SNPs of ELOVL2 rs3734398 T>C and HSD17B12 rs11037684 A>G that predicted CMSS, with an allelic hazards ratio of 0.66 (95% confidence interval = 0.51–0.84 and p = 8.34 × 10−4) and 2.29 (1.55–3.39 and p = 3.61 × 10−5), respectively. Finally, the ELOVL2 rs3734398 variant CC genotype was found to be associated with a significantly increased mRNA expression level. These SNPs may be potential markers for CM prognosis, if validated by additional larger and mechanistic studies

Slightly Shift New Classes to Remember Old Classes for Video Class-Incremental Learning

Recent video class-incremental learning usually excessively pursues the accuracy of the newly seen classes and relies on memory sets to mitigate catastrophic forgetting of the old classes. However, limited storage only allows storing a few representative videos. So we propose SNRO, which slightly shifts the features of new classes to remember old classes. Specifically, SNRO contains Examples Sparse(ES) and Early Break(EB). ES decimates at a lower sample rate to build memory sets and uses interpolation to align those sparse frames in the future. By this, SNRO stores more examples under the same memory consumption and forces the model to focus on low-semantic features which are harder to be forgotten. EB terminates the training at a small epoch, preventing the model from overstretching into the high-semantic space of the current task. Experiments on UCF101, HMDB51, and UESTC-MMEA-CL datasets show that SNRO performs better than other approaches while consuming the same memory consumption

Causes of Infection after Earthquake, China, 2008

To determine which organisms most commonly cause infection after natural disasters, we cultured specimens from injured earthquake survivors in Wenchuan, China, 2008. Of 123 cultures, 46 (59%) grew only 1 type of pathogenic bacteria. Smear was more effective than culture for early diagnosis of gas gangrene. Early diagnosis and treatment of wounds are crucial