DNA-T: Deformable Neighborhood Attention Transformer for Irregular Medical Time Series.

The real-world Electronic Health Records (EHRs) present irregularities due to changes in the patient's health status, resulting in various time intervals between observations and different physiological variables examined at each observation point. There have been recent applications of Transformer-based models in the field of irregular time series. However, the full attention mechanism in Transformer overly focuses on distant information, ignoring the short-term correlations of the condition. Thereby, the model is not able to capture localized changes or short-term fluctuations in patients' conditions. Therefore, we propose a novel end-to-end Deformable Neighborhood Attention Transformer (DNA-T) for irregular medical time series. The DNA-T captures local features by dynamically adjusting the receptive field of attention and aggregating relevant deformable neighborhoods in irregular time series. Specifically, we design a Deformable Neighborhood Attention (DNA) module that enables the network to attend to relevant neighborhoods by drifting the receiving field of neighborhood attention. The DNA enhances the model's sensitivity to local information and representation of local features, thereby capturing the correlation of localized changes in patients' conditions. We conduct extensive experiments to validate the effectiveness of DNA-T, outperforming existing state-of-the-art methods in predicting the mortality risk of patients. Moreover, we visualize an example to validate the effectiveness of the proposed DNA.

LRTK: a platform agnostic toolkit for linked-read analysis of both human genome and metagenome.

BACKGROUND: Linked-read sequencing technologies generate high-base quality short reads that contain extrapolative information on long-range DNA connectedness. These advantages of linked-read technologies are well known and have been demonstrated in many human genomic and metagenomic studies. However, existing linked-read analysis pipelines (e.g., Long Ranger) were primarily developed to process sequencing data from the human genome and are not suited for analyzing metagenomic sequencing data. Moreover, linked-read analysis pipelines are typically limited to 1 specific sequencing platform. FINDINGS: To address these limitations, we present the Linked-Read ToolKit (LRTK), a unified and versatile toolkit for platform agnostic processing of linked-read sequencing data from both human genome and metagenome. LRTK provides functions to perform linked-read simulation, barcode sequencing error correction, barcode-aware read alignment and metagenome assembly, reconstruction of long DNA fragments, taxonomic classification and quantification, and barcode-assisted genomic variant calling and phasing. LRTK has the ability to process multiple samples automatically and provides users with the option to generate reproducible reports during processing of raw sequencing data and at multiple checkpoints throughout downstream analysis. We applied LRTK on linked reads from simulation, mock community, and real datasets for both human genome and metagenome. We showcased LRTK's ability to generate comparative performance results from preceding benchmark studies and to report these results in publication-ready HTML document plots. CONCLUSIONS: LRTK provides comprehensive and flexible modules along with an easy-to-use Python-based workflow for processing linked-read sequencing datasets, thereby filling the current gap in the field caused by platform-centric genome-specific linked-read data analysis tools.

Exploring high-quality microbial genomes by assembling short-reads with long-range connectivity.

Although long-read sequencing enables the generation of complete genomes for unculturable microbes, its high cost limits the widespread adoption of long-read sequencing in large-scale metagenomic studies. An alternative method is to assemble short-reads with long-range connectivity, which can be a cost-effective way to generate high-quality microbial genomes. Here, we develop Pangaea, a bioinformatic approach designed to enhance metagenome assembly using short-reads with long-range connectivity. Pangaea leverages connectivity derived from physical barcodes of linked-reads or virtual barcodes by aligning short-reads to long-reads. Pangaea utilizes a deep learning-based read binning algorithm to assemble co-barcoded reads exhibiting similar sequence contexts and abundances, thereby improving the assembly of high- and medium-abundance microbial genomes. Pangaea also leverages a multi-thresholding algorithm strategy to refine assembly for low-abundance microbes. We benchmark Pangaea on linked-reads and a combination of short- and long-reads from simulation data, mock communities and human gut metagenomes. Pangaea achieves significantly higher contig continuity as well as more near-complete metagenome-assembled genomes (NCMAGs) than the existing assemblers. Pangaea also generates three complete and circular NCMAGs on the human gut microbiomes.

YIN Kejing's experience in treatment of cyclomastopathy by the accompanied needling at Tianchi (PC 1). / æ®·å ‹æ•¬å‚åˆºå¤©æ± ç©´æ²»ç–—ä¹³è ºå¢žç”Ÿç—‡ä¸´åºŠç»éªŒ.

The paper introduces professor YIN Kejing's clinical experience in treatment of cyclomastopathy. In pathogenesis, cyclomastopathy is relevant with dysfunction of the thoroughfare vessel and the conception vessel, the stagnation of blood and phlegm and the stagnation of liver qi. The principle of treatment prefers to unblocking the circulation of qi and blood and adjusting the balance between yin and yang. In diagnosis, the unique meridian palpation is adopted. Regarding the treatment of cyclomastopathy, the accompanied needling at Tianchi (PC 1) is recommended in combination with the external application of herbal medicines, presenting the specific characteristics of traditional Chinese medicine.

Analysis of Function Role and Long Noncoding RNA Expression in Chronic Heart Failure Rats Treated with Hui Yang Jiu Ji Decoction.

Hui Yang Jiu Ji (HYJJ) decoction has been applied as a prescription of traditional Chinese medicine for the treatment of chronic heart failure (CHF). However, its comprehensive molecular mechanism remains unclear now. Our study aimed to explore the possible function and lncRNA-miRNA regulation networks of HYJJ on CHF induced by doxorubicin (DOX) in rats. Our study showed that HYJJ could recover cardiac function and alleviate myocardial injury of DOX-induced CHF. Besides, HYJJ had an effect on restraining myocardial apoptosis in CHF rats. Moreover, RNA-sequencing and bioinformatics analysis indicated that among a total of 548 significantly up- and down-regulated differentially expressed (DE) long noncoding RNA (lncRNA), 511 up- and down-regulated DE miRNAs were identified. Cushing's syndrome and Adrenergic signaling in cardiomyocytes were common pathways between DE-lncRNAs-enriched pathways and DE-miRNAs-enriched pathways. Finally, we observed a new pathway-MSTRG.598.1/Lilrb2 pathway with the HYJJ treatment; however, it needs further studies. In conclusion, this study provided evidence that HYJJ may be a suitable medicine for treating CHF. Moreover, several pivotal miRNAs may serve important roles in these processes by regulating some key miRNAs or pathways in CHF.

[Professor YIN Ke-jing's experience of Jingluo Bietong acupuncture technique for shoulder arthralgia].

Professor YIN Ke-jing's experience of Jingluo Bietong acupuncture technique for shoulder arthralgia is summarized. According to the location of pain, shoulder arthralgia is divided into hand-taiyin type, hand-yangming type, hand-shaoyang type, hand-taiyang type and mixed type. The acupoint selection is based on Jingluo Bietong acupuncture technique, and Shugu (BL 65) or Feiyang (BL 58) are selected for hand-taiyin type, Taichong (LR 3) for hand-yangming type, Taixi (KI 3) and Dazhong (KI 4) for hand-shaoyang type, and Taibai (SP 3) or Diji (SP 8) for hand-taiyang type. In addition, combined with acupuncture at the blocking points of meridians and Xiangu (ST 43), better efficacy could be obtained.

Effects of mining on the molybdenum absorption and translocation of plants in the Luanchuan molybdenum mine.

BACKGROUND: There is a critical need to examine whether mining of molybdenum (Mo) ore will affect Mo absorption and translocation by plants at a community level. METHODS: Indigenous plants and their rhizospheric soil (0-20 cm) growing in two different areas including the mining and the unexploited areas were collected from the Luanchuan Mo mine-one of the largest Mo mines in Asia. The concentrations of Mo and other heavy metals of plants or soil were measured by ICP-AES. Mo absorption and translocation in plants growing in two areas were investigated and compared. Heavy metal pollution in soil was also evaluated by the potential ecological hazard index method. RESULTS: Mo concentration in mining soils was higher with the changes from 108.13 to 268.13 mg kg-1 compared to unexploited area. Mo concentrations in shoots and roots of plants growing in the mining area were also significant higher than those growing in the unexploited area with 2.59 and 2.99 times, respectively. The Mo translocation factor of plants growing in the unexploited area was 1.61, which reached 1.69 times that of plants growing in the mining area. Mo was the main heavy metal pollutant in the soil of both the mining and the unexploited areas. CONCLUSION: Mining of Mo had changed not only the Mo concentration in soil but also Mo absorption and translocation in plants. Plants growing in the mining area absorbed more Mo from the soil but translocated relatively less to shoots than plants of the unexploited area. However, the mechanisms of Mo absorption and translocation of plants in mining area should be further studied in the future.

[Professor YIN Ke-jing's experience of treating pain syndrome from meridians and collaterals].

Professor YIN Ke-jing is good at identifying the location of the disease by using meridian examination. With rich knowledge of the classics, combined with his own clinical experience, professor YIN puts forward "spasmodic muscle-tendon leads to pain", and the effect of Guanci (acupuncture at joints) is superior. Moreover, according to Inner Canon of Yellow Emperor, he has summarized new method of jingluo bietong, which uses fewer acupoints but achieves satisfactory efficacy. This method has broadened the principles of acupoint selection and prescriptions for clinical treatment. This method for treating pain can harmonize qi-blood and smooth meridians, and improve the clinical effect.

LogPar: Logistic PARAFAC2 Factorization for Temporal Binary Data with Missing Values.

Binary data with one-class missing values are ubiquitous in real-world applications. They can be represented by irregular tensors with varying sizes in one dimension, where value one means presence of a feature while zero means unknown (i.e., either presence or absence of a feature). Learning accurate low-rank approximations from such binary irregular tensors is a challenging task. However, none of the existing models developed for factorizing irregular tensors take the missing values into account, and they assume Gaussian distributions, resulting in a distribution mismatch when applied to binary data. In this paper, we propose Logistic PARAFAC2 (LogPar) by modeling the binary irregular tensor with Bernoulli distribution parameterized by an underlying real-valued tensor. Then we approximate the underlying tensor with a positive-unlabeled learning loss function to account for the missing values. We also incorporate uniqueness and temporal smoothness regularization to enhance the interpretability. Extensive experiments using large-scale real-world datasets show that LogPar outperforms all baselines in both irregular tensor completion and downstream predictive tasks. For the irregular tensor completion, LogPar achieves up to 26% relative improvement compared to the best baseline. Besides, LogPar obtains relative improvement of 13.2% for heart failure prediction and 14% for mortality prediction on average compared to the state-of-the-art PARAFAC2 models.

Context-Aware Time Series Imputation for Multi-Analyte Clinical Data.

Clinical time series imputation is recognized as an essential task in clinical data analytics. Most models rely either on strong assumptions regarding the underlying data-generation process or on preservation of only local properties without effective consideration of global dependencies. To advance the state of the art in clinical time series imputation, we participated in the 2019 ICHI Data Analytics Challenge on Missing Data Imputation (DACMI). In this paper, we present our proposed model: Context-Aware Time Series Imputation (CATSI), a novel framework based on a bidirectional LSTM in which patients' health states are explicitly captured by learning a "global context vector" from the entire clinical time series. The imputations are then produced with reference to the global context vector. We also incorporate a cross-feature imputation component to explore the complex feature correlations. Empirical evaluations demonstrate that CATSI obtains a normalized root mean square deviation (nRMSD) of 0.1998, which is 10.6% better than that of state-of-the-art models. Further experiments on consecutive missing datasets also illustrate the effectiveness of incorporating the global context in the generation of accurate imputations.

The role of different therapeutic courses in treating 47 cases of rheumatoid arthritis with acupuncture.

OBJECTIVE: To observe the effect of acupuncture therapeutic courses on rheumatoid arthritis. METHOD: Forty-seven patients were treated with acupuncture for 6 courses and at the end of the third and sixth course of treatment, the therapeutic effects of acupuncture on morning rigidity, swelling and pain of joints as well as rheumatoid factor (RF), erythrocyte sedimentation rate (ESR) and C-reactive protein (CRP) were observed. RESULT: At the end of the third course, the markedly effective rate was 34.0% for morning rigidity, 9.5% for swelling and 23.4% for pain, the RF negative-turning rate was 2.2%, and there was no significant difference (P > 0.05) in the reduction of ESR and CRP as compared to that before treatment. However, at the end of the sixth course, the markedly effective rate was 80.9% for morning rigidity, 64.3% for swelling and 87.2% for pain, the RF negative-turning rate was 26.7%, and there was a very significant difference (P < 0.01) in the reduction of ESR and CRP as compared to that before treatment. CONCLUSION: More acupuncture therapeutic courses can bring about better therapeutic results on rheumatoid arthritis.