Sequential Condition Evolved Interaction Knowledge Graph for Traditional Chinese Medicine Recommendation

Traditional Chinese Medicine (TCM) has a rich history of utilizing natural herbs to treat a diversity of illnesses. In practice, TCM diagnosis and treatment are highly personalized and organically holistic, requiring comprehensive consideration of the patient's state and symptoms over time. However, existing TCM recommendation approaches overlook the changes in patient status and only explore potential patterns between symptoms and prescriptions. In this paper, we propose a novel Sequential Condition Evolved Interaction Knowledge Graph (SCEIKG), a framework that treats the model as a sequential prescription-making problem by considering the dynamics of the patient's condition across multiple visits. In addition, we incorporate an interaction knowledge graph to enhance the accuracy of recommendations by considering the interactions between different herbs and the patient's condition. Experimental results on a real-world dataset demonstrate that our approach outperforms existing TCM recommendation methods, achieving state-of-the-art performance

111In-Labeled Cystine-Knot Peptides Based on the Agouti-Related Protein for Targeting Tumor Angiogenesis

Agouti-related protein (AgRP) is a 4-kDa cystine-knot peptide of human origin with four disulfide bonds and four solvent-exposed loops. The cell adhesion receptor integrin αvβ3 is an important tumor angiogenesis factor that determines the invasiveness and metastatic ability of many malignant tumors. AgRP mutants have been engineered to bind to integrin αvβ3 with high affinity and specificity using directed evolution. Here, AgRP mutants 7C and 6E were radiolabeled with 111In and evaluated for in vivo targeting of tumor integrin αvβ3 receptors. AgRP peptides were conjugated to the metal chelator 1, 4, 7, 10-tetra-azacyclododecane- N, N′, N″, N‴-tetraacetic acid (DOTA) and radiolabeled with 111In. The stability of the radiopeptides 111In-DOTA-AgRP-7C and 111In-DOTA-AgRP-6E was tested in phosphate-buffered saline (PBS) and mouse serum, respectively. Cell uptake assays of the radiolabeled peptides were performed in U87MG cell lines. Biodistribution studies were performed to evaluate the in vivo performance of the two resulting probes using mice bearing integrin-expressing U87MG xenograft tumors. Both AgRP peptides were easily labeled with 111In in high yield and radiochemical purity (>99%). The two probes exhibited high stability in phosphate-buffered saline and mouse serum. Compared with 111In-DOTA-AgRP-6E, 111In-DOTA-AgRP-7C showed increased U87MG tumor uptake and longer tumor retention (5.74 ± 1.60 and 1.29 ± 0.02%ID/g at 0.5 and 24 h, resp.), which was consistent with measurements of cell uptake. Moreover, the tumor uptake of 111In-DOTA-AgRP-7C was specifically inhibited by coinjection with an excess of the integrin-binding peptidomimetic c(RGDyK). Thus, 111In-DOTA-AgRP-7C is a promising probe for targeting integrin αvβ3 positive tumors in living subjects

Genome-wide Association Study Identifies New Loci Associated With Risk Of HBV Infection And Disease Progression

Background: Recent studies have identified susceptibility genes of HBV clearance, chronic hepatitis B, liver cirrhosis, hepatocellular carcinoma, and showed the host genetic factors play an important role in these HBV-related outcomes. Results: In order to discover new susceptibility genes for HBV-related outcomes, we conducted a genome-wide association study in 1031 Chinese participants, including 275 HBV clearance subjects, 92 asymptomatic persistence infection carriers (ASPI), 93 chronic hepatitis B patients (CHB), 188 HBV-related decompensated cirrhosis patients (DC), 214 HBV-related hepatocellular carcinoma patients (HCC) and 169 healthy controls (HC). In the case-control study, we observed novel locus significantly associated with CHB (SNP: rs1264473, Gene: GRHL2, P = 1.57×10-6) and HCC (SNP: rs2833856, Gene: EVA1C, P = 1.62×10-6; SNP: rs4661093, Gene: ETV3, P = 2.26×10-6). In the trend study across progressive stages post HBV infection, one novel locus (SNP: rs1537862, Gene: LACE1, P = 1.85×10-6), and three MHC loci (HLA-DRB1, HLA-DPB1, HLA-DPA2) showed significant increased progressive risk from ASPI to CHB. Interestingly, underlying the evolutionary study of HBV-related genes in public database, we found that the derived allele of two HBV clearance related locus, rs3077 and rs9277542, are under strong selection in European population. Conclusions: In this study, we identified several novel candidate genes associated with individual HBV infectious outcomes, progressive stages, and liver enzymes. Moreover, we identified two SNPs that show selective significance (HLA-DPA1, HLA-DPB1) in non-East Asian (European, American, South Asian) versus East Asian, indicating that host genetic factors contribute to the ethnic disparities of susceptibility of HBV infection. Taken together, these findings provided a new insight into the role of host genetic factors in HBV related outcomes and progression

Mapping crop phenology using NDVI time-series derived from HJ-1 A/B data

With the availability of high frequent satellite data, crop phenology could be accurately mapped using time-series remote sensing data. Vegetation index time-series data derived from AVHRR, MODIS, and SPOT-VEGETATION images usually have coarse spatial resolution. Mapping crop phenology parameters using higher spatial resolution images (e.g., Landsat TM-like) is unprecedented. Recently launched HJ-1 A/B CCD sensors boarded on China Environment Satellite provided a feasible and ideal data source for the construction of high spatio-temporal resolution vegetation index time-series. This paper presented a comprehensive method to construct NDVI time-series dataset derived from HJ-1 A/B CCD and demonstrated its application in cropland areas. The procedures of time-series data construction included image preprocessing, signal filtering, and interpolation for daily NDVI images then the NDVI time-series could present a smooth and complete phenological cycle. To demonstrate its application, TIMESAT program was employed to extract phenology parameters of crop lands located in Guanzhong Plain, China. The small-scale test showed that the crop season start/end derived from HJ-1 A/B NDVI time-series was comparable with local agro-metrological observation. The methodology for reconstructing time-series remote sensing data had been proved feasible, though forgoing researches will improve this a lot in mapping crop phenology. Last but not least, further studies should be focused on field-data collection, smoothing method and phenology definitions using time-series remote sensing data

Plan2Dance: Planning Based Choreographing from Music

The field of dancing robots has drawn much attention from numerous sources. Despite the success of previous systems on choreography for robots to dance with external stimuli, they are often either limited to a pre-defined set of movements or lack of considering “hard” relations among dancing motions. In the demonstration, we design a planning based choreographing system, which views choreography with music as planning problems and solve the problems with off-the-shelf planners. Our demonstration exhibits the effectiveness of our system via evaluating our system with various music

MODIS Evapotranspiration Downscaling Using a Deep Neural Network Trained Using Landsat 8 Reflectance and Temperature Data

The MODIS 8-day composite evapotranspiration (ET) product (MOD16A2) is widely used to study large-scale hydrological cycle and energy budgets. However, the MOD16A2 spatial resolution (500 m) is too coarse for local and regional water resource management in agricultural applications. In this study, we propose a Deep Neural Network (DNN)-based MOD16A2 downscaling approach to generate 30 m ET using Landsat 8 surface reflectance and temperature and AgERA5 meteorological variables. The model was trained at a 500 m resolution using the MOD16A2 ET as reference and applied to the Landsat 8 30 m resolution. The approach was tested on 15 Landsat 8 images over three agricultural study sites in the United States and compared with the classical random forest regression model that has been often used for ET downscaling. All evaluation sample sets applied to the DNN regression model had higher R2 and lower root-mean-square deviations (RMSD) and relative RMSD (rRMSD) (the average values: 0.67, 2.63 mm/8d and 14.25%, respectively) than the random forest model (0.64, 2.76 mm/8d and 14.92%, respectively). Spatial improvement was visually evident both in the DNN and the random forest downscaled 30 m ET maps compared with the 500 m MOD16A2, while the DNN-downscaled ET appeared more consistent with land surface cover variations. Comparison with the in situ ET measurements (AmeriFlux) showed that the DNN-downscaled ET had better accuracy, with R2 of 0.73, RMSD of 5.99 mm/8d and rRMSD of 48.65%, than the MOD16A2 ET (0.65, 7.18 and 50.42%, respectively)

Abnormal Activity Detection Using Pyroelectric Infrared Sensors

Healthy aging is one of the most important social issues. In this paper, we propose a method for abnormal activity detection without any manual labeling of the training samples. By leveraging the Field of View (FOV) modulation, the spatio-temporal characteristic of human activity is encoded into low-dimension data stream generated by the ceiling-mounted Pyroelectric Infrared (PIR) sensors. The similarity between normal training samples are measured based on Kullback-Leibler (KL) divergence of each pair of them. The natural clustering of normal activities is discovered through a self-tuning spectral clustering algorithm with unsupervised model selection on the eigenvectors of a modified similarity matrix. Hidden Markov Models (HMMs) are employed to model each cluster of normal activities and form feature vectors. One-Class Support Vector Machines (OSVMs) are used to profile the normal activities and detect abnormal activities. To validate the efficacy of our method, we conducted experiments in real indoor environments. The encouraging results show that our method is able to detect abnormal activities given only the normal training samples, which aims to avoid the laborious and inconsistent data labeling process