A CNN based system for predicting the implied volatility and option prices.

The evaluations of option prices and implied volatility are critical for option risk management and trading. Common strategies in existing studies relied on the parametric models. However, these models are based on several idealistic assumptions. In addition, previous research of option pricing mainly depends on the historical transaction records without considering the performance of other concurrent options. To address these challenges, we proposed a convolutional neural network (CNN) based system for predicting the implied volatility and the option prices. Specifically, the customized non-parametric learning approach is first used to estimate the implied volatility. Second, several traditional parametric models are also implemented to estimate these prices as well. The convolutional neural network is utilized to obtain the predictions based on the estimation of the implied volatility. Our experiments based on Chinese SSE 50ETF options demonstrate that the proposed framework outperforms the traditional methods with at least 40.12% performance enhancement in terms of RMSE

AWTE-BERT:Attending to Wordpiece Tokenization Explicitly on BERT for Joint Intent Classification and SlotFilling

Intent classification and slot filling are two core tasks in natural language understanding (NLU). The interaction nature of the two tasks makes the joint models often outperform the single designs. One of the promising solutions, called BERT (Bidirectional Encoder Representations from Transformers), achieves the joint optimization of the two tasks. BERT adopts the wordpiece to tokenize each input token into multiple sub-tokens, which causes a mismatch between the tokens and the labels lengths. Previous methods utilize the hidden states corresponding to the first sub-token as input to the classifier, which limits performance improvement since some hidden semantic informations is discarded in the fine-tune process. To address this issue, we propose a novel joint model based on BERT, which explicitly models the multiple sub-tokens features after wordpiece tokenization, thereby generating the context features that contribute to slot filling. Specifically, we encode the hidden states corresponding to multiple sub-tokens into a context vector via the attention mechanism. Then, we feed each context vector into the slot filling encoder, which preserves the integrity of the sentence. Experimental results demonstrate that our proposed model achieves significant improvement on intent classification accuracy, slot filling F1, and sentence-level semantic frame accuracy on two public benchmark datasets. The F1 score of the slot filling in particular has been improved from 96.1 to 98.2 (2.1% absolute) on the ATIS dataset

RPFdb: a database for genome wide information of translated mRNA generated from ribosome profiling

Citation: Xie, S. Q., Nie, P., Wang, Y., Wang, H. W., Li, H. Y., Yang, Z. L., . . . Xie, Z. (2016). RPFdb: a database for genome wide information of translated mRNA generated from ribosome profiling. Nucleic Acids Research, 44(D1), D254-D258. doi:10.1093/nar/gkv972Translational control is crucial in the regulation of gene expression and deregulation of translation is associated with a wide range of cancers and human diseases. Ribosome profiling is a technique that provides genome wide information of mRNA in translation based on deep sequencing of ribosome protected mRNA fragments (RPF). RPFdb is a comprehensive resource for hosting, analyzing and visualizing RPF data, available at www.rpfdb.org or http://sysbio.sysu.edu.cn/rpfdb/index. html. The current version of database contains 777 samples from 82 studies in 8 species, processed and reanalyzed by a unified pipeline. There are two ways to query the database: by keywords of studies or by genes. The outputs are presented in three levels. (i) Study level: including meta information of studies and reprocessed data for gene expression of translated mRNAs; (ii) Sample level: including global perspective of translated mRNA and a list of the most translated mRNA of each sample from a study; (iii) Gene level: including normalized sequence counts of translated mRNA on different genomic location of a gene from multiple samples and studies. To explore rich information provided by RPF, RPFdb also provides a genome browser to query and visualize context-specific translated mRNA. Overall our database provides a simple way to search, analyze, compare, visualize and download RPF data sets

Efficacy and safety of robot-assisted deep brain stimulation for Parkinson’s disease: a meta-analysis

ObjectiveThis meta-analysis aims to assess the effectiveness and safety of robot-assisted deep brain stimulation (DBS) surgery for Parkinson’s disease(PD).MethodsFour databases (Medline, Embase, Web of Science and CENTRAL) were searched from establishment of database to 23 March 2024, for articles studying robot-assisted DBS in patients diagnosed with PD. Meta-analyses of vector error, complication rate, levodopa-equivalent daily dose (LEDD), Unified Parkinson’s Disease Rating Scale (UPDRS), UPDRS II, UPDRS III, and UPDRS IV were performed.ResultsA total of 15 studies were included in this meta-analysis, comprising 732 patients with PD who received robot-assisted DBS. The pooled results revealed that the vector error was measured at 1.09 mm (95% CI: 0.87 to 1.30) in patients with Parkinson’s disease who received robot-assisted DBS. The complication rate was 0.12 (95% CI, 0.03 to 0.24). The reduction in LEDD was 422.31 mg (95% CI: 68.69 to 775.94). The improvement in UPDRS, UPDRS III, and UPDRS IV was 27.36 (95% CI: 8.57 to 46.15), 14.09 (95% CI: 4.67 to 23.52), and 3.54 (95% CI: −2.35 to 9.43), respectively.ConclusionRobot-assisted DBS is a reliable and safe approach for treating PD. Robot-assisted DBS provides enhanced accuracy in contrast to conventional frame-based stereotactic techniques. Nevertheless, further investigation is necessary to validate the advantages of robot-assisted DBS in terms of enhancing motor function and decreasing the need for antiparkinsonian medications, in comparison to traditional frame-based stereotactic techniques.Clinical trial registration: PROSPERO(CRD42024529976)

Epigenetic Dysregulation in Mesenchymal Stem Cell Aging and Spontaneous Differentiation

BACKGROUND: Mesenchymal stem cells (MSCs) hold great promise for the treatment of difficult diseases. As MSCs represent a rare cell population, ex vivo expansion of MSCs is indispensable to obtain sufficient amounts of cells for therapies and tissue engineering. However, spontaneous differentiation and aging of MSCs occur during expansion and the molecular mechanisms involved have been poorly understood. METHODOLOGY/PRINCIPAL FINDINGS: Human MSCs in early and late passages were examined for their expression of genes involved in osteogenesis to determine their spontaneous differentiation towards osteoblasts in vitro, and of genes involved in self-renewal and proliferation for multipotent differentiation potential. In parallel, promoter DNA methylation and hostone H3 acetylation levels were determined. We found that MSCs underwent aging and spontaneous osteogenic differentiation upon regular culture expansion, with progressive downregulation of TERT and upregulation of osteogenic genes such as Runx2 and ALP. Meanwhile, the expression of genes associated with stem cell self-renewal such as Oct4 and Sox2 declined markedly. Notably, the altered expression of these genes were closely associated with epigenetic dysregulation of histone H3 acetylation in K9 and K14, but not with methylation of CpG islands in the promoter regions of most of these genes. bFGF promoted MSC proliferation and suppressed its spontaneous osteogenic differentiation, with corresponding changes in histone H3 acetylation in TERT, Oct4, Sox2, Runx2 and ALP genes. CONCLUSIONS/SIGNIFICANCE: Our results indicate that histone H3 acetylation, which can be modulated by extrinsic signals, plays a key role in regulating MSC aging and differentiation

Characterization of Oxidative Guanine Damage and Repair in Mammalian Telomeres

8-oxo-7,8-dihydroguanine (8-oxoG) and 2,6-diamino-4-hydroxy-5-formamidopyrimidine (FapyG) are among the most common oxidative DNA lesions and are substrates for 8-oxoguanine DNA glycosylase (OGG1)–initiated DNA base excision repair (BER). Mammalian telomeres consist of triple guanine repeats and are subject to oxidative guanine damage. Here, we investigated the impact of oxidative guanine damage and its repair by OGG1 on telomere integrity in mice. The mouse cells were analyzed for telomere integrity by telomere quantitative fluorescence in situ hybridization (telomere–FISH), by chromosome orientation–FISH (CO–FISH), and by indirect immunofluorescence in combination with telomere–FISH and for oxidative base lesions by Fpg-incision/Southern blot assay. In comparison to the wild type, telomere lengthening was observed in Ogg1 null (Ogg1−/−) mouse tissues and primary embryonic fibroblasts (MEFs) cultivated in hypoxia condition (3% oxygen), whereas telomere shortening was detected in Ogg1−/− mouse hematopoietic cells and primary MEFs cultivated in normoxia condition (20% oxygen) or in the presence of an oxidant. In addition, telomere length abnormalities were accompanied by altered telomere sister chromatid exchanges, increased telomere single- and double-strand breaks, and preferential telomere lagging- or G-strand losses in Ogg1−/− mouse cells. Oxidative guanine lesions were increased in telomeres in Ogg1−/− mice with aging and primary MEFs cultivated in 20% oxygen. Furthermore, oxidative guanine lesions persisted at high level in Ogg1−/− MEFs after acute exposure to hydrogen peroxide, while they rapidly returned to basal level in wild-type MEFs. These findings indicate that oxidative guanine damage can arise in telomeres where it affects length homeostasis, recombination, DNA replication, and DNA breakage repair. Our studies demonstrate that BER pathway is required in repairing oxidative guanine damage in telomeres and maintaining telomere integrity in mammals

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