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Backgrounds: Pulmonary tuberculosis (PTB) is a major health and economic burden. Accurate PTB detection is an important step to eliminating TB globally. I nterferon gamma-induced protein 10 (IP-10) has been reported as a potential diagnostic marker for PTB since 2007. In this study, a meta-analysis approach was used to assess diagnostic value of IP-10 for PTB. Methods: Web of Science, PubMed, the Cochrane Library, and Embase databases were searched for studies published in English up to February 2019. The pooled sensitivity, specificity, positive likelihood ratio (PLR), negative likelihood ratio (NLR), diagnostic odds ratio (DOR), the area under the curve (AUC) and hierarchical summary receiver operating characteristic (HSROC) curve were estimated by the HSROC model and random effect model. Results: Eighteen studies including 2836 total participants met our inclusion criteria. The pooled sensitivity, specificity, PLR, and NLR of IP-10 for PTB detection were 86%, 88%, 7.00, and 0.16, respectively. The pooled DOR was 43.01, indicating a very powerful discriminatory ability of IP-10. The AUC was 0.93 (95% CI: 0.91–0.95), showed the accuracy of IP-10 was good. Meta-regression showed that there was no heterogeneity with respect to TB burden, study design type, age, IP-10 assay method, IP-10 condition and HIV-infection status. Conclusions: Our results showed that IP-10 is a promising marker for differentiating PTB from non-TB.

Motivated by the challenging of deep learning on the low data regime and the urgent demand for intelligent design on highly energetic materials, we explore a correlated deep learning framework, which consists of three recurrent neural networks (RNNs) correlated by the transfer learning strategy, to efficiently generate new energetic molecules with a high detonation velocity in the case of very limited data available. To avoid the dependence on the external big data set, data augmentation by fragment shuffling of 303 energetic compounds is utilized to produce 500,000 molecules to pretrain RNN, through which the model can learn sufficient structure knowledge. Then the pretrained RNN is fine-tuned by focusing on the 303 energetic compounds to generate 7153 molecules similar to the energetic compounds. In order to more reliably screen the molecules with a high detonation velocity, the SMILE enumeration augmentation coupled with the pretrained knowledge is utilized to build an RNN-based prediction model, through which R 2 is boosted from 0.4446 to 0.9572. The comparable performance with the transfer learning strategy based on an existing big database (ChEMBL) to produce the energetic molecules and drug-like ones further supports the effectiveness and generality of our strategy in the low data regime. High-precision quantum mechanics calculations further confirm that 35 new molecules present a higher detonation velocity and lower synthetic accessibility than the classic explosive RDX, along with good thermal stability. In particular, three new molecules are comparable to caged CL-20 in the detonation velocity. All the source codes and the data set are freely available at https://github.com/wangchenghuidream/ RNNMGM.

Due to its low density, high strength, and stiffness the intermetallic phase Al 3 Ti is a good candidate as reinforcement for Al alloys. In this work, in situ Al 3 Ti particle reinforced Al composites are fabricated from Ti particles and Al melt via melt stirring with a high shearing mixer. Microstructure and mechanical properties are investigated. The results indicate that, owing to the high shearing effect and intensive macroscopic flow of the melt, reinforced particles are distributed homogeneously on the microscopic and macroscopic scale. Furthermore, Al 3 Ti particles are proved to be effective nuclei for heterogeneous nucleation of α-Al, thus the grain size of the Al matrix is significantly decreased. As a result of the fine grains and the uniform distribution of Al 3 Ti particles, E-modulus, yield, and tensile strength of the composites are enhanced.

ObjectivesTo investigate twin reversed arterial perfusion (TRAP) sequence for the prediction of TRAP-related adverse pregnancy outcomes at the gestational age of 11-14 weeks. MethodsPregnant women in the rst trimester diagnosed with TRAP were recruited at West China Second University Hospital from January 2015 to June 2018. Systematic screening for the pump twin's crownrump length (CRL) and acardiac twin's upper pole-rump length (URL) was conducted using ultrasonic detection. The (CRL-URL)/CRL and URL/CRL ratios were used to assess the pregnancy outcomes for the pump twin. ResultsTwenty-one pregnant women aged 21-39 years with a gestation of 11-14 weeks were recruited. TRAP was diagnosed on average (± standard deviation [SD]) at pregnancy week 13.1 ± 0.18. The pump twins' mean (± SD) CRL was 6.65 ± 1.1 cm. The incidence of intrauterine death for the pump twins was 19.0% (n=4), the miscarriage rate was 14.3% (n=3), and the live birth rate was 66.7% (n=14). The (CRL-URL)/CRL ratios between the non-survival (intrauterine death and miscarriage) and survival groups signi cantly differed (0.33 ± 0.08 vs. 0.58 ± 0.08, p < 0.05). Similarly, the URL/CRL ratios between the non-survival and survival groups signi cantly differed (0.67 ± 0.08 vs. 0.42 ± 0.08, p < 0.05). ConclusionsThe (CRL-URL)/CRL and URL/CRL ratios were valuable indicators for determining pregnancy outcomes of pump twins with TRAP at an early gestational age.