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Fibroblast activation protein (FAP) is regarded as a promising target for the diagnosis and treatment of tumors as it was overexpressed in cancer-associated fibroblasts. FAP inhibitors bearing a quinoline scaffold have been proven to show high affinity against FAP in vitro and in vivo, and the scaffold has been radio-labeled for the imaging and treatment of FAP-positive tumors. However, currently available FAP imaging agents both contain chelator groups to enable radio-metal labeling, making those tracers more hydrophilic and not suitable for the imaging of lesions in the brain. Herein, we report the synthesis, radio-labeling, and evaluation of a 18F-labeled quinoline analogue ([18F]3) as a potential FAP-targeted PET tracer, which holds the potential to be blood–brain barrier-permeable. [18F]3 was obtained by one-step radio-synthesis via a copper-mediated SNAR reaction from a corresponding boronic ester precursor. [18F]3 showed moderate lipophilicity with a log D7.4 value of 1.11. In cell experiments, [18F]3 showed selective accumulation in A549-FAP and U87 cell lines and can be effectively blocked by the pre-treatment of a cold reference standard. Biodistribution studies indicated that [18F]3 was mainly excreted by hepatic clearance and urinary excretion, and it may be due to its moderate lipophilicity. In vivo PET imaging studies indicated [18F]3 showed selective accumulation in FAP-positive tumors, and specific binding was confirmed by blocking studies. However, low brain uptake was observed in biodistribution and PET imaging studies. Although our preliminary data indicated that [18F]3 holds the potential to be developed as a blood–brain barrier penetrable FAP-targeted PET tracer, its low brain uptake limits its application in the detection of brain lesions. Herein, we report the synthesis and evaluation of [18F]3 as a novel small-molecule FAPI-targeted PET tracer, and our results suggest further structural optimizations would be needed to develop a BBB-permeable PET tracer with this scaffold.

Lung cancer has been the leading cause of cancer-related mortality in China in recent decades. Positron emission tomography-computer tomography (PET/CT) has been established in the diagnosis of lung cancer. 18F-FDG is the most widely used PET tracer in foci diagnosis, tumor staging, treatment planning, and prognosis assessment by monitoring abnormally exuberant glucose metabolism in tumors. However, with the increasing knowledge on tumor heterogeneity and biological characteristics in lung cancer, a variety of novel radiotracers beyond 18F-FDG for PET imaging have been developed. For example, PET tracers that target cellular proliferation, amino acid metabolism and transportation, tumor hypoxia, angiogenesis, pulmonary NETs and other targets, such as tyrosine kinases and cancer-associated fibroblasts, have been reported, evaluated in animal models or under clinical investigations in recent years and play increasing roles in lung cancer diagnosis. Thus, we perform a comprehensive literature review of the radiopharmaceuticals and recent progress in PET tracers for the study of lung cancer biological characteristics beyond glucose metabolism.

With the deployment of innovative memories such as non-volatile memory and 3D-stacked memory in distributed systems, how to improve the application performance by utilizing the unique characteristics of these hybrid memories remains an active research direction. For instance, the Intel Knight Landing (KNL) processor incorporates a High Bandwidth Memory (HBM) using 3D-stacked technology with traditional DRAM onto the same chip. HBM achieves much higher bandwidth than traditional DRAM when the application exhibits high parallelism and sequential access. In this paper, we propose a new metric SP-factor to guide the data scheduling in distributed system using hybrid memories such as HBM and DRAM. The SP-factor incorporates the data access patterns including data block size and data access parallelism, which leads to better data scheduling decision for higher performance. We apply SP-factor to several data eviction policies on the hybrid memory system, which achieves better performance. Moreover, an adaptive data scheduling method (ADSM) is proposed for such hybrid memory system with HBM and DRAM. ADSM can dynamically adjust scheduling decisions based on runtime performance metrics so that it can adapt to workloads with different data access patterns. Our experimental results show that ADSM can significantly improve the performance of the representative workloads. For SQL query application with mixed access pattern, the cache hit ratio increases by 10.4% and the execution time reduces by 14.6% using ADSM compared to ARC policy.

A complete mitogenome of a cave dwelling pseudoscorpion Bisetocreagris titanium is reported here. The mitogenome is a circular DNA molecule with a length of 14,756 base pairs (bp), and it contains 13 protein coding genes (PCGs), 22 transfer RNAs (tRNAs), 2 ribosomal RNAs (rRNAs), and 1 putative control region. Phylogenetic analysis of 30 Arachnida species was performed based on the amino acid datasets of 13 PCGs, and the result indicated Pseudoscorpiones is the sister lineage of Acariformes. This result is congruent with the former phylogenetic results of mitogenomes, but incongruent with the results of morphological characters and/or ribosomal DNA data that indicated Pseudoscorpiones are positioned in a clade with the Solifugae.

Stragglers are commonly accepted to have a great impact on the performance of big data system. However, the reason to cause straggler is complicated. Previous works mostly focus on straggler detection, scheduling optimization, and coarse-grained root-cause analysis. These methods fail to provide useful insights to help users optimize their programs. In this paper, we propose BigRoots, a general method incorporating both framework and system features for root-cause analysis of stragglers in the big data system. BigRoots analyzes the stragglers using features from big data framework such as shuffle read/write bytes and JVM garbage collection time, as well as system resource utilization, such as CPU, I/O, and network, which is able to detect both internal and external causes of stragglers. We verify BigRoots by injecting high resource utilization across different system components and perform case studies to analyze different workloads in Hibench. The experimental results demonstrate that BigRoots is effective to identify the root causes of stragglers and provide useful guidance for performance optimization. Based on the root causes identified by BigRoots, the workloads achieve significant performance improvement (by 37.74% in the best case) after optimization.

Table of contents 01 The eco-toxicological effects of cadmium stress on five ornamental plants Zhouli Liu, Wei Chen, Xingyuan He, Shuai Yu, Weihang Ding 02 Study on the formation and release mechanisms of hydrogen sulfide in Longtan coal mine Jianjun Liu, Yuliang Wu, Kaixiang Fan 03 Coral bleaching in the North Reef of China’s Xisha Islands in 2014 Yuanchao Li, Xinqing Zheng, Shiquan Chen, Haiqun Xie 04 Soil chemical characteristics in a Cunninghamia lanceolata stand suffering from ice-snow damage Jie Li, Zhuomin Wang, Li Xue 05 Diversity of understory vegetation was under a Cunninghamia lanceolata stand suffering from ice-snow damage Zhiyang Lie, Weilong Huang, Tongtong Zhou, Li Xue 06 The effect of water, nitrogen and harvesting time on yield and biomass allocation of Helianthus tuberosus L. Kai Gao, Tiexia Zhu 07 The vertical variation of microbial communities in the sediment of sea cucumber pond Weiteng Mao, Pei Yu, Xinfang Lv, Yunchun Li 08 Indicating significances of alpine plant functional groups to environmental change Peixi Su, Rui Shi 09 Research on the daily CH4 fluxes of soil in summer mangrove community at Yingluogang of Guangxi, China Yong Jiang, Xingbin Chen, Shichu liang 10 Beach protection structures in middle and lower reaches of Yangtze River Junfeng Xu 11 Social responsibility management of large dam project with owner as core: an empirical case in China Jiwei Zhu, Zhao Zhai, Linan Zhou, Yun Le, Li Cao 12 The ecological function value assessment analysis of urban waterfront Jiwei Zhu, Ping Lu, Zhiqian Huang, Bei Zhou 13 Mechanism of diurnal osmotic potential changes and adjustment of three Caragana species in different habitats of the Inner Mongolia Plateau Zhirong Zheng, Shihai Lv, Chaoyang Feng 14 Biodiversity of phytoplankton and environmental influences analysis of Longfeng Wetland, China Nan Song, Yan Liu, Xinxin Lu, Yawen Fan 15 The effect of human activities on culturable soil microbes from Yaoquan Mountain in Wudalianchi, China Jianfei Guan, Jihua Wang, Xueping Zhang

BACKGROUND: Pulmonary tuberculosis (TB) patients often suffer from anorexia and poor nutrition, causing weight loss. The peptide hormones leptin and its counterpart ghrelin, acting in the regulation of food intake and fat utilization, play an important role in nutritional balance. This study aimed to investigate the association of blood concentrations of leptin, ghrelin and inflammatory cytokines with body mass index (BMI) in TB patients with and without type 2 diabetes mellitus (T2DM). METHODS: BMI, biochemical parameters and plasma levels of leptin, ghrelin and inflammatory cytokines were measured before the start of treatment in 27 incident TB patients with T2DM, 21 TB patients and 23 healthy subjects enrolled in this study. RESULTS: The levels of leptin were significantly higher in TB patients (35.2 ± 19.1 ng/ml) than TB+T2DM (12.6 ± 6.1 ng/ml) and control (16.1 ± 11.1 ng/ml) groups. The level of ghrelin was significantly lower in TB (119.9 ± 46.1 pg/ml) and non-significantly lower in TB+T2DM (127.7 ± 38.6 pg/ml) groups than control (191.6 ± 86.5 pg/ml) group. The levels of TNF-α were higher, while IFN-γ and IL-6 levels were lower in patients than in the control group. Leptin showed a negative correlation with BMI in TB (r=-0.622, p0.05) groups, but negative correlation with BMI in the control (r=-0.693, p