RDKG: A Reinforcement Learning Framework for Disease Diagnosis on Knowledge Graph

Automatic disease diagnosis from symptoms has attracted much attention in medical practices. It can assist doctors and medical practitioners in narrowing down disease candidates, reducing testing costs, improving diagnosis efficiency, and more importantly, saving human lives. Existing research has made significant progress in diagnosing disease but was limited by the gap between interpretability and accuracy. To fill this gap, in this paper, we propose a method called Reinforced Disease Diagnosis on Knowlege Graph (RDKG). Specifically, we first construct a knowledge graph containing all information from electronic medical records. To capture informative embeddings, we propose an enhanced knowledge graph embedding method that can embed information outside the knowledge graph into entity embedding. Then we transform the automatic disease diagnosis task into a Markov decision process on the knowledge graph. After that, we design a reinforcement learning method with a soft reward mechanism and a pruning strategy to solve the Markov decision process. We accomplish automated disease diagnosis by finding a path from symptoms to disease. The experimental results show that our model can effectively utilize heterogeneous information in the knowledge graph to complete the automatic disease diagnosis. Besides, our model demonstrates supreme performance in both accuracy and interpretability

(Z)-1,3-Bis(4-chloro­phen­yl)-2-(1H-1,2,4-triazol-1-yl)prop-2-en-1-one

In the title mol­ecule, C17H11Cl2N3O, the C=C bond connecting the triazole and 4-chloro­phenyl groups adopts a Z geometry. The dihedral angles formed by the triazole ring and the 4-chloro substituted benzene rings are 67.3 (1) and 59.1 (1)°. The dihedral angle between the two benzene rings is 73.5 (1)°

Rapidity losses in heavy-ion collisions from AGS to RHIC energies

We study the rapidity losses in central heavy-ion collisions from AGS to RHIC energies with the mean rapidity determined from the projectile net-baryon distribution after collisions. The projectile net-baryon distribution in the full rapidity range was obtained by removing the target contribution phenomenologically at forward rapidity region from the experimental net-baryon measurements and taking into account the projectile contribution at backward rapidity region. Based on the full projectile net-baryon distributions, calculation results show that the rapidity loss stops increasing from the SPS top energy to RHIC energies, indicating that baryon transport does not depend strongly on energy at high energies.Comment: 7 pages, 4 figure

Detection of DC electric forces with zeptonewton sensitivity by single-ion phonon laser

Detecting extremely small forces helps exploring new physics quantitatively. Here we demonstrate that the phonon laser made of a single trapped $^{40}$Ca$^{+}$ ion behaves as an exquisite sensor for small force measurement. We report our successful detection of small electric forces regarding the DC trapping potential with sensitivity of 2.41$\pm$0.49 zN/$\sqrt{{\rm Hz}}$, with the ion only under Doppler cooling, based on the injection-locking of the oscillation phase of the phonon laser in addition to the classical squeezing applied to suppress the measurement uncertainty. We anticipate that such a single-ion sensor would reach a much better force detection sensitivity in the future once the trapping system is further improved and the fluorescence collection efficiency is further enhanced.Comment: arXiv admin note: text overlap with arXiv:2110.0114

A Zinc Finger Motif in the P1 N Terminus, Highly Conserved in a Subset of Potyviruses, Is Associated with the Host Range and Fitness of Telosma Mosaic Virus

P1 is the first protein translated from the genomes of most viruses in the family Potyviridae, and it contains a C-terminal serine-protease domain that cis-cleaves the junction between P1 and HCPro in most cases. Intriguingly, P1 is the most divergent among all mature viral factors, and its roles during viral infection are still far from understood. In this study, we found that telosma mosaic virus (TelMV, genus Potyvirus) in passion fruit, unlike TelMV isolates present in other hosts, has two stretches at the P1 N terminus, named N1 and N2, with N1 harboring a Zn finger motif. Further analysis revealed that at least 14 different potyviruses, mostly belonging to the bean common mosaic virus subgroup, encode a domain equivalent to N1. Using the newly developed TelMV infectious cDNA clones from passion fruit, we demonstrated that N1, but not N2, is crucial for viral infection in both Nicotiana benthamiana and passion fruit. The regulatory effects of N1 domain on P1 cis cleavage, as well as the accumulation and RNA silencing suppression (RSS) activity of its cognate HCPro, were comprehensively investigated. We found that N1 deletion decreases HCPro abundance at the posttranslational level, likely by impairing P1 cis cleavage, thus reducing HCPro-mediated RSS activity. Remarkably, disruption of the Zn finger motif in N1 did not impair P1 cis cleavage and HCPro accumulation but severely debilitated TelMV fitness. Therefore, our results suggest that the Zn finger motif in P1s plays a critical role in viral infection that is independent of P1 protease activity and self-release, as well as HCPro accumulation and silencing suppression.This work is supported by grants from the Hainan Provincial National Science Foundation (grant nos. 2019RC010 and 322CXTD505), Sanya Yazhou Bay Sci-Tech City (SYND-2022-32 and SYND-2022-02), the National Natural Science Foundation of China (32060603), and the Central Public Interest Scientific Institution Basal Research Fund for Chinese Academy of Tropical Agricultural Sciences (19CXTD-33). We thank Fangfang Li (Chinese Academy of Agricultural Sciences) for providing pCHF3-35S-GFP and P19-expressing plasmidPeer reviewe

Effidit: Your AI Writing Assistant

In this technical report, we introduce Effidit (Efficient and Intelligent Editing), a digital writing assistant that facilitates users to write higher-quality text more efficiently by using artificial intelligence (AI) technologies. Previous writing assistants typically provide the function of error checking (to detect and correct spelling and grammatical errors) and limited text-rewriting functionality. With the emergence of large-scale neural language models, some systems support automatically completing a sentence or a paragraph. In Effidit, we significantly expand the capacities of a writing assistant by providing functions in five categories: text completion, error checking, text polishing, keywords to sentences (K2S), and cloud input methods (cloud IME). In the text completion category, Effidit supports generation-based sentence completion, retrieval-based sentence completion, and phrase completion. In contrast, many other writing assistants so far only provide one or two of the three functions. For text polishing, we have three functions: (context-aware) phrase polishing, sentence paraphrasing, and sentence expansion, whereas many other writing assistants often support one or two functions in this category. The main contents of this report include major modules of Effidit, methods for implementing these modules, and evaluation results of some key methods.Comment: Technical report for Effidit. arXiv admin note: text overlap with arXiv:2202.0641