A Population-Based Descriptive Atlas of Invasive Pneumococcal Strains Recovered Within the U.S. During 2015–2016

Invasive pneumococcal disease (IPD) has greatly decreased since implementation in the U.S. of the 7 valent conjugate vaccine (PCV7) in 2000 and 13 valent conjugate vaccine (PCV13) in 2010. We used whole genome sequencing (WGS) to predict phenotypic traits (serotypes, antimicrobial phenotypes, and pilus determinants) and determine multilocus genotypes from 5334 isolates (~90% of cases) recovered during 2015–2016 through Active Bacterial Core surveillance. We identified 44 serotypes; 26 accounted for 98% of the isolates. PCV13 serotypes (inclusive of serotype 6C) accounted for 1503 (28.2%) isolates, with serotype 3 most common (657/5334, 12.3%), while serotypes 1 and 5 were undetected. Of 305 isolates from children <5 yrs, 60 (19.7%) were of PCV13 serotypes 19A, 19F, 3, 6B, and 23F (58/60 were 19A, 19F, or 3). We quantitated MLST-based lineages first detected during the post-PCV era (since 2002) that potentially arose through serotype-switching. The 7 predominant emergent post-PCV strain complexes included 23B/CC338, 15BC/CC3280, 19A/CC244, 4/CC439, 15A/CC156, 35B/CC156, and 15BC/CC156. These strains accounted for 332 isolates (6.2% of total) and were more frequently observed in children <5 yrs (17.7%; 54/305). Fifty-seven categories of recently emerged (in the post PCV7 period) putative serotype-switch variants were identified, accounting for 402 isolates. Many of these putative switch variants represented newly emerged resistant strains. Penicillin-nonsusceptibility (MICs > 0.12 μg/ml) was found among 22.4% (1193/5334) isolates, with higher penicillin MICs (2–8 μg/ml) found in 8.0% (425/5334) of isolates that were primarily (372/425, 87.5%) serotypes 35B and 19A. Most (792/1193, 66.4%) penicillin-nonsusceptible isolates were macrolide-resistant, 410 (34.4%) of which were erm gene positive and clindamycin-resistant. The proportion of macrolide-resistant isolates increased with increasing penicillin MICs; even isolates with reduced penicillin susceptibility (MIC = 0.06 μg/ml) were much more likely to be macrolide-resistant than basally penicillin-susceptible isolates (MIC < 0.03 μg/ml). The contribution of recombination to strain diversification was assessed through quantitating 35B/CC558-specific bioinformatic pipeline features among non-CC558 CCs and determining the sizes of gene replacements. Although IPD has decreased greatly and stabilized in the post-PCV13 era, the species continually generates recombinants that adapt to selective pressures exerted by vaccines and antimicrobials. These data serve as a baseline for monitoring future changes within each invasive serotype

MST-RNN: A Multi-Dimension Spatiotemporal Recurrent Neural Networks for Recommending the Next Point of Interest

With the increasing popularity of location-aware Internet-of-Vehicle services, the next-Point-of-Interest (POI) recommendation has gained significant research interest, predicting where drivers will go next from their sequential movements. Many researchers have focused on this problem and proposed solutions. Machine learning-based methods (matrix factorization, Markov chain, and factorizing personalized Markov chain) focus on a POI sequential transition. However, they do not recommend the user’s position for the next few hours. Neural network-based methods can model user mobility behavior by learning the representations of the sequence data in the high-dimensional space. However, they just consider the influence from the spatiotemporal dimension and ignore many important influences, such as duration time at a POI (Point of Interest) and the semantic tags of the POIs. In this paper, we propose a novel method called multi-dimension spatial–temporal recurrent neural networks (MST-RNN), which extends the ST-RNN and exploits the duration time dimension and semantic tag dimension of POIs in each layer of neural networks. Experiments on real-world vehicle movement data show that the proposed MST-RNN is effective and clearly outperforms the state-of-the-art methods

Spatial-temporal Pattern Evolution of Ecological Land Use in Hebei Province

Ecological land use is an important component of ecosystem. This study presents spatial and temporal pattern evolution characteristics of ecological land for the period from 2009 to 2017, based on GIS technology and mathematical statistics. The results show that ecological land structure is stable, amount tends to decrease, average annual decrease of 21, 000 hm2. Ecological land types were mainly transformed into farmland and urban land, Internal transformed mainly between woodland and grassland. Spatial aggregation were existed, Aggregation degree: forest land > grassland > water area and wetland > desert. But high concentration areas (“HH” related area) is reduced from 11 counties to 9 counties, and low concentration areas (“LL” related areas) increased from 37 counties to 40 counties, from 2009 to 2017. According to the results, proposals for ecological land use were put forward

MST-RNN: A Multi-Dimension Spatiotemporal Recurrent Neural Networks for Recommending the Next Point of Interest

With the increasing popularity of location-aware Internet-of-Vehicle services, the next-Point-of-Interest (POI) recommendation has gained significant research interest, predicting where drivers will go next from their sequential movements. Many researchers have focused on this problem and proposed solutions. Machine learning-based methods (matrix factorization, Markov chain, and factorizing personalized Markov chain) focus on a POI sequential transition. However, they do not recommend the user’s position for the next few hours. Neural network-based methods can model user mobility behavior by learning the representations of the sequence data in the high-dimensional space. However, they just consider the influence from the spatiotemporal dimension and ignore many important influences, such as duration time at a POI (Point of Interest) and the semantic tags of the POIs. In this paper, we propose a novel method called multi-dimension spatial–temporal recurrent neural networks (MST-RNN), which extends the ST-RNN and exploits the duration time dimension and semantic tag dimension of POIs in each layer of neural networks. Experiments on real-world vehicle movement data show that the proposed MST-RNN is effective and clearly outperforms the state-of-the-art methods

Data

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Engineering application of ecological lake purification tailwater from sewage plant in vein industrial park

As the raising of environmental protection requirements, the outlet water (tailwater) quality standards of many sewage treatment plants need to be further improved. This paper mainly introduces the advanced treatment of tailwater from sewage treatment plant of vein industrial park by artificial ecological lake. The designed processing amount of this project was 1300 m3/d, the inlet water COD was 30 mg/L, the ammonia nitrogen content was 1.5 mg/L, and the outlet water main index reached the surface water environment quality standard (GB3838-2002) Ⅲ class water quality standard, which had improved the outlet water (tailwater) quality of sewage treatment plants and improved the regional water environment quality. By constructing an artificial landscape lake, the resource utilization of tailwater can be realized

Data from: An experimental study of the influence of lithology on compaction behaviour of broken waste rock in coal mine backfill

The research aims to explore the influences of lithology on the compaction behaviours of broken waste rocks. For this purpose, a WAW1000D servo test machine and a self-made bidirectional loading test system for granular materials were used to conduct axial and lateral compaction tests on four typical types of broken waste rocks: sandstone, mudstone, limestone, and shale. On this basis, we analysed the relationships between lateral and axial stress with the strain in, and porosity of, the four types of broken waste rocks. In addition, the relationship of axial stress with lateral stress and lateral pressure coefficient, and the changes in the particle size distribution of broken waste rocks before, and after, compaction were discussed. The test results demonstrated that the samples of higher strength were found to have low lateral and axial strains as well as a lower porosity in axial and lateral loading tests; while samples of lower strength showed low lateral stress and lateral pressure coefficient under axial load. After being compacted, the samples of the four types of broken waste rocks were found to have a higher proportion of small particles, indicating some particle crushing. Moreover, the samples of lower strength were broken to a greater extent

SATB1 is Correlated with Progression and Metastasis of Breast Cancers: A Meta-Analysis

Background/Aims: Several researches have evaluated the significance of SATB1 (Special AT-rich sequence binding protein 1) expression in breast cancers (BCs), but the results have been disputed, especially in the aspects of clinicopathological features and prognosis. Therefore, our study aimed to use a meta-analysis to summarize the clinical and prognostic relevance of SATB1 gene expression in BCs. Methods: A literature search of PubMed, EMBASE, Cochrane library, Chinese Wanfang and CNKI was performed to identify eligible studies. Ten studies total, comprising 5,185 patients (1,699 SATB1-positive and 3,486 SATB1-negative), were enrolled in our study, which was performed using Revman5.3 Software and Stata11.0 Software. Results: This meta-analysis showed that the expression of SATB1 was significantly higher in breast cancer than in normal tissues (OR = 12.28; 95%CI = 6.01-25.09), and was statistically related to several clinicopathological parameters, including lymph node metastasis (OR = 1.55, 95%CI = 1.01-2.39) and Tumor Node Metastasis(TNM) stage (OR = 0.35, 95%CI = 0.22-0.56). However, the level of SATB1 was not statistically associated with the age (OR = 1.13, 95%CI = 0.87-1.46), tumour size (OR = 0.72, 95%CI = 0.44-1.19), estrogen receptor (OR = 0.78, 95%CI = 0.55-1.09), progesterone receptor (OR = 0.64, 95%CI = 0.32-1.29), HER2 status (OR=1.98, 95%CI = 0.74-5.30), and histological type (OR = 0.49, 95%CI = 0.22-1.11). Conclusion: High expression of SATB1 was significantly correlated with tumourigenesis and metastasis of BCs, indicating poor prognosis for patients. SATB1 could serve as a potential marker for detection and prognosis evaluation of breast cancers

Water Level Fluctuation under the Impact of Lake Regulation and Ecological Implication in Huayang Lakes, China

Water level fluctuation (WLF) in shallow lakes in the middle and lower reaches of the Yangtze River has been a concern of many researchers. This work aims to investigate the effects of climate change and regulation of floodgates and the Three Gorges Dam (TGD) on WLF and lake volume in Huayang Lakes during the past 52 years. The results revealed that precipitation is the dominant factor that leads to seasonal variation of lake levels, whereas regulation of floodgates and TGD are the key drivers of hydrology regime change in the past 20 years. Natural lake regime has higher water level when there is more precipitation and less lake volume. Floodgates and TGD regulations have changed this pattern since 2003, causing less difference in water level in spite of more precipitation and lake recession. Under the combined impacts of floodgates and TGD regulations, Huayang Lakes have experienced a prolonged outflow time since 2003 and the contribution rate caused by the floodgates and TGD regulations has increased by 19.90%. Additionally, the water level of Huayang Lakes decreased by approximately 0.3~0.5 m from September to November, but it showed no alteration from January to March in the past two decades. This indicated that floodgate regulations used for agricultural irrigation and fishery culture dominate the hydrology regime in winter and early spring. This study is beneficial for aquatic ecosystem protection in floodgate-controlled lakes under the circumstance of climate change and vigorous anthropology activities