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Cooking emissions account for a major fraction of urban volatile organic compounds and organic aerosol. Aldehyde species, in particular, are important exposure hazards in indoor residential and occupational environments, and precursors to particulate matter and ozone formation in outdoor air. Formation pathways of aldehydes from oils that lead to their emissions are not well understood. In this work, we investigate the underlying mechanisms involved in the formation of aldehydes from heated cooking oil emissions, through studying how antioxidants and oil composition modulate oxidation chemistry. Our results demonstrate that gaseous emissions are driven by radical-mediated autoxidation reactions in cooking oil, and the composition of cooking oils strongly influences the reaction mechanisms. Antioxidants have a dual effect on aldehyde emissions depending on the rates of radical propagation reactions. We propose a mechanistic framework that can be used to understand and predict cooking emissions under different cooking conditions. Our results highlight the need to understand the rates and mechanisms of autoxidation and other reactions in cooking oils in order to accurately predict the gas- and particle-phase emissions from food cooking in urban atmospheres.

Abstract: With the development of epidermal growth factor receptor (EGFR)-based tyrosine kinase inhibitors (TKIs) and their applications in the clinic, non-small-cell lung cancer (NSCLC) treatment has entered a new era, and a great number of patients have benefited. However, there still exist other subgroups of patients who may not benefit from EGFR TKIs, although EGFR mutation is the main driving mutation that leads to NSCLC. To identify potential NSCLC responders for TKI therapy and to detect EGFR status in vivo, noninvasive technology, such as TKI PET imaging, has been developed in recent years, and a great number of tyrosine kinase-targeted PET tracers have been reported. The visualization and quantification of EGFR expression in vivo by PET would provide the most important information for personalizing NSCLC therapy and prediction of response in clinical. This article overreviews the progress of small molecular tyrosine kinase-targeted PET tracers and their applications in preclinical experiments and clinical studies. The current limitations and future development of these tracers are also briefly discussed.

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.

Integral to the urban ecosystem, greening trees provide many ecological benefits, but the active biogenic volatile organic compounds (BVOCs) they release contribute to the production of ozone and secondary organic aerosols, which harm ambient air quality. It is, therefore, necessary to understand the BVOC emission characteristics of dominant greening tree species and their relative contribution to secondary pollutants in various urban contexts. Consequently, this study utilized a dynamic enclosure system to collect BVOC samples of seven dominant greening tree species in urban Chengdu, Southwest China. Gas chromatography/mass spectrometry was used to analyze the BVOC components and standardized BVOC emission rates of each tree species were then calculated to assess their relative potential to form secondary pollutants. We found obvious differences in the composition of BVOCs emitted by each species. Ficus virens displayed a high isoprene emission rate at 31.472 μgC/(gdw (g dry weight)•hr), while Cinnamomum camphora emitted high volumes of D-Limonene at 93.574 μgC/(gdw•hr). In terms of the BVOC emission rates by leaf area, C. camphora had the highest emission rate of total BVOCs at 13,782.59 μgC/(m

Four polysaccharides (GTPS, OTPS, BTPS and DTPS) were extracted from green tea, oolong tea, black tea and dark tea respectively. The physical and chemical properties, antioxidant and hypoglycemic activities were studied. Structural analysis showed that these tea polysaccharides were glycoprotein complexes, and there were significant differences in microstructure, protein, total sugar and uronic acid content. They were all composed of multiple monosaccharides and different molar ratios. In terms of antioxidant activity, completely fermented BTPS and DTPS had higher activity. Regarding to hypoglycemic effects, BTPS showed higher α-glucosidase inhibitory activity in vitro. And in the treatment of type 2 diabetes mice, Oral BTPS significantly controlled the levels of blood glucose, TG, TC, LDL-C, Cr, UREA, ALT and AST in diabetic mice, and improved insulin resistance. Histopathological observation further confirmed that BTPS can alleviate liver injury caused by hyperglycemia and hyperlipidemia. Data showed that BTPS significantly improved hyperglycemia and liver function in diabetic mice.

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.

Cooking emissions account for a major fraction of urban organic aerosol. It is therefore important to understand the atmospheric evolution in the physical and chemical properties of organic compounds emitted from cooking activities. In this work, we investigate the formation of secondary organic aerosol (SOA) from oxidation of gas-phase organic compounds from heated cooking oil. The chemical composition of cooking SOA is analyzed using thermal desorption–gas chromatography–mass spectrometry (TD–GC–MS). While the particle-phase composition of SOA is a highly complex mixture, we adopt a new method to achieve molecular speciation of the SOA. All the GC-elutable material is classified by the constituent functional groups, allowing us to provide a molecular description of its chemical evolution upon oxidative aging. Our results demonstrate an increase in average oxidation state (from −0.6 to −0.24) and decrease in average carbon number (from 5.2 to 4.9) with increasing photochemical aging of cooking oil, suggesting that fragmentation reactions are key processes in the oxidative aging of cooking emissions within 2 d equivalent of ambient oxidant exposure. Moreover, we estimate that aldehyde precursors from cooking emissions account for a majority of the SOA formation and oxidation products. Overall, our results provide insights into the atmospheric evolution of cooking SOA, a majority of which is derived from gas-phase oxidation of aldehydes.

Canonical Polyadic Decomposition (CPD) is one of the most popular tensor decomposition methods and plays an important role in big data analysis. For sparse tensor, the major computation procedure in CPD, which is known as matricized tensor times Khatri-Rao product (MTTKRP), exhibits discontinuous memory access and turns to be the performance bottleneck from achieving high performance on emerging processor architectures. In this paper, we propose swCPD, an efficient CPD implementation on the many-core Sunway processor. The swCPD accelerates the optimization algorithms dominating the performance of MTTKRP, including Alternating Least Squares (ALS), Gradient Descent (GD) and Randomized Block Sampling (RBS), as well as the latest fast Levenberg–Marquardt (fLM++) and Generalized Canonical Polyadic Decomposition with Stochastic Gradient Descent (GCP-SGD). The main idea adopted in swCPD is a hierarchical partitioning mechanism. From the computation perspective, the 64 Computation Processing Elements (CPEs) in a Sunway processor are divided into eight groups, with each group containing seven workers and one controller. From the data perspective, we partition the sparse tensor into different granularities, which are blocks, bands and tiles. Moreover, we develop a communication mechanism through register communication for cooperation between CPEs. We evaluate the implementation of swCPD with both synthesized and real-world datasets. The experiment results show that each optimized algorithm in swCPD achieves better performance than corresponding algorithms adopted in cutting-edge CPD implementations.

Multicasting and proactive caching at wireless edge, as two promising techniques to accommodate the explosive Video-on-Demand (VoD) traffic in mobile networks, have received significant attention in recent years. Various advantages of the two techniques have been revealed in existing works, which however are mainly based on strong assumptions on the behaviors of users for VoD service. In particular, multicasting usually assumes adequate synchronous requests to the same videos while proactive caching mainly assumes perfect prediction of content popularity. In this article, we resort to a real dataset collected from a campus network to examine the impact of these assumptions, and rethink the gain of multicasting and proactive caching at base stations for VoD service. We first review the principles of the two techniques in reducing traffic load, and then evaluate and compare their performance based on the collected dataset. The results demonstrate the limited opportunity and performance gain of multicasting for synchronous VoD requests, and identify the potential of proactive caching under different qualities of content popularity prediction. Based on the results, some open research directions regarding the two techniques are outlined for future study.

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.

Lycium barbarum polysaccharides (LBPs) are beneficial for vision; however, relevant research has mainly focused on entire crude polysaccharides, with the basis and exact structure of the polysaccharide rarely explored. In this study, LICP009-3F-2a, a novel polysaccharide from Lycium barbarum L., was separated and then purified using anion-exchange and size-exclusion chromatography. Structural characteristics were investigated using chemical and spectroscopic methods, which revealed that LICP009-3F-2a has an Mw of 13720 Da and is an acidic heteropolysaccharide composed of rhamnose (39.1%), arabinose (7.4%), galactose (22.5%), glucose (8.3%), galacturonic acid (13.7%), and glucuronic acid (4.0%). Linkage and NMR data revealed that LICP009-3F-2a has the following backbone: →2)-α-L-Rha-(1 → 2,4)-α-L-Rha-(1 → 4)-α-D-GalAp-(1 → 3,6)-β-D-Galp-(1 → 3,6)-β-D-Galp-(1 → 6)-β-D-Galp-(1→, with three main branches, including: α-L-Araf-(1 → 5)-α-L-Araf-(1 → 6)-β-D-Glcp-(1 → 2,4)-α-L-Rha-(1→, β-D-Glcp-(1 → 4)-β-D-Glcp-(1 → 3,6)-β-D-Galp-(1→, and β-D-Galp-(1 → 3)-β-D-Galp-(1 → 3,6)-β-D-Galp-(1 → . Differential scanning colorimetry and thermogravimetric analysis showed that LICP009-3F-2a is thermally stable, while X-ray diffractometry showed that LICP009-3F-2a has a semi-crystalline structure. In addition, LICP009-3F-2a protects ARPE-19 cells from H

The data stream processing framework processes the stream data based on event-time to ensure that the request can be responded to in real-time. In reality, streaming data usually arrives out-of-order due to factors such as network delay. The data stream processing framework commonly adopts the watermark mechanism to address the data disorderedness. Watermark is a special kind of data inserted into the data stream with a timestamp, which helps the framework to decide whether the data received is late and thus be discarded. Traditional watermark generation strategies are periodic; they cannot dynamically adjust the watermark distribution to balance the responsiveness and accuracy. This paper proposes an adaptive watermark generation mechanism based on the time series prediction model to address the above limitation. This mechanism dynamically adjusts the frequency and timing of watermark distribution using the disordered data ratio and other lateness properties of the data stream to improve the system responsiveness while ensuring acceptable result accuracy. We implement the proposed mechanism on top of Flink and evaluate it with real-world datasets. The experiment results show that our mechanism is superior to the existing watermark distribution strategies in terms of both system responsiveness and result accuracy.

In light of emerging antibiotic resistance, synthesis of active, environmental friendly antimicrobial alternatives becomes increasingly necessary. In this study, ZnO quantum dots (ZnO QDs) were developed by the sol-gel method and characterized. The antibacterial activities of ZnO QDs against

Colon cancer is one of the most common cancers with high mortality, and can easily spread and metastasize, remaining an urgent disease to be solved. Nanomaterial-associated starvation or photothermal therapy has been considered to be a promising strategy in tumor therapy. However, the therapeutic effect of a single regimen for cancer treatment still needs to be improved due to their respective limitations. Herein, a biomimetic multifunctional nanoreactor is developed by encapsulating glucose oxidase and gold nanorods (AuNRs) in an erythrocyte membrane camouflaged metal-organic framework (MOF) nanoparticle (ZGAM), which was exploited for synergistic treatment of colon cancer by combining glucose oxidase-based starvation with AuNR-based photothermal therapy (PTT). This biomimetic nanoreactor could not only exhaust endogenous glucose to suppress the growth of the tumor by the released glucose oxidase (GOx), but also enhance the effect of photothermal therapy via inhibiting the expression of heat shock protein (HSP). In vitro and in vivo investigations indicate that this biomimetic nanoreactor shows excellent therapeutic effects on tumors, resulting from the synergistic treatment of starvation therapy and PTT. Therefore, the proposed strategy may open a window to develop an intelligent therapeutic system for better therapeutic efficacy against cancer.

Tensor computations are gaining wide adoption in big data analysis and artificial intelligence. Among them, tensor completion is used to predict the missing or unobserved value in tensors. The decomposition-based tensor completion algorithms have attracted significant research attention since they exhibit better parallelization and scalability. However, existing optimization techniques for tensor completion cannot sustain the increasing demand for applying tensor completion on ever larger tensor data. To address the above limitations, we develop the first tensor completion library cuTC on multiple Graphics Processing Units (GPUs) with three widely used optimization algorithms such as alternating least squares (ALS), stochastic gradient descent (SGD) and coordinate descent (CCD+). We propose a novel TB-COO format that leverages warp shuffle and shared memory on GPU to enable efficient reduction. In addition, we adopt the auto-tuning method to determine the optimal parameters for better convergence and performance. We compare cuTC with state-of-the-art tensor completion libraries on real-world datasets, and the results show cuTC achieves significant speedup with similar or even better accuracy.

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 ...

The coflow scheduling in data-parallel clusters can improve application-level communication performance. The existing coflow scheduling method without prior knowledge usually uses Multi-Level Feedback Queue (MLFQ) with fixed threshold parameters, which is insensitive to coflow traffic characteristics. Manual adjustment of the threshold parameters for different application scenarios often has long optimization period and is coarse in optimization granularity. We propose M-DRL, a deep reinforcement learning based coflow traffic scheduler by dynamically setting thresholds of MLFQ to adapt to the coflow traffic characteristics, and reduces the average coflow completion time. Trace-driven simulations on the public dataset show that coflow communication stages using M-DRL complete 2.08\(\times \)(6.48\(\times \)) and 1.36\(\times \)(1.25\(\times \)) faster on average coflow completion time (95-th percentile) in comparison to per-flow fairness and Aalo, and is comparable to SEBF with prior knowledge.

Many current distributed file systems use erasure-coding based data redundancy techniques to improve the reliability of data storage. Such techniques can significantly improve the effective storage utilization. However, there are several drawbacks to the above techniques. Firstly, they introduce non-negligible computation overhead for decoding. Secondly, traditional data caching and placement strategies become less effective in such cases. To solve the above drawbacks, this paper proposes a new data cache allocation mechanism based on simulated annealing and a new data placement strategy based on convex optimization, which effectively reduces data block transmission delay and decoding delay. We have implemented the proposed data placement strategy in the real-world distributed file system Alluxio, and evaluated the performance of our strategy. Experiment results show that our strategy can significantly reduce the file read delay compared to traditional data placement strategies.

The β-site APP-cleaving enzyme 1 (BACE1) plays important roles in the proteolytic processing of amyloid precursor protein, and can be regarded as an important target for the diagnosis and treatment of AD. This study aimed to report the synthesis and evaluation of an

Cooking emissions account for a major fraction of urban organic aerosol. It is therefore important to understand the atmospheric evolution in the physical and chemical properties of organic compounds emitted from cooking activities. In this work, we investigate the formation of secondary organic aerosol (SOA) from oxidation of gas-phase organic compounds from heated cooking oil. The chemical composition of cooking SOA is analyzed using thermal desorption-gas chromatography-mass spectrometry. While the particle-phase composition of SOA is a highly complex mixture, we adopt a new method to achieve molecular speciation of the SOA. All the GC elutable material is classified by the constituent functional groups, allowing us to provide a molecular description of its chemical evolution upon oxidative aging. Our results demonstrate an increase in average oxidation state (from −0.6 to −0.24), and decrease in average carbon number (from 5.2 to 4.9) with increasing photochemical aging of cooking oil, suggesting that fragmentation reactions are key processes in the oxidative aging of cooking emissions within 2 days equivalent of ambient oxidant exposure. Moreover, we estimate that aldehyde precursors from cooking emissions account for a majority of the SOA formation and oxidation products. Overall, our results provide insights into the atmospheric evolution of cooking SOA, a majority of which is derived from gas-phase oxidation of aldehydes.

Recent studies have proven that the purinergic signaling pathway plays a key role in neurotransmission and neuromodulation, and is involved in various neurodegenerative diseases and psychiatric disorders. With the characterization of the subtypes of receptors in purinergic signaling, i.e. the P1 (adenosine), P2X (ion channel) and P2Y (G protein-coupled), more attention has been paid to the pathophysiology and therapeutic potential of purinergic signaling in the central nervous system disorders. Alzheimer’s disease (AD) is a progressive and deadly neurodegenerative disease that is characterized by memory loss, cognitive impairment and dementia. However, as drug development aimed to prevent or control AD has series of failures in recent years, more researchers have focused on the neuroprotection-related mechanisms such as purinergic signaling in AD patients to find a potential cure. This article reviews the recent discoveries of purinergic signaling in AD, and summarizes the potential agents as modulators for the receptors of purinergic signaling in AD-related research and treatments. Thus, our paper provides an insight into purinergic signaling in the development of anti- AD therapies.

Ultrasonic microbubbles in combination with microRNA (miRNAs/miRs) exhibited promising effects on cancer treatments. The aim was to investigate the role of miR-378 in hepatoma cells and the efficiency of it in combination with ultrasonic irradiation and SonoVue® microbubbles method for cell transfection. HuH-7, Hep3B and SK-Hep1 cells were transfected with an miR-378 mimic using only Lipofectamine® 3000 or combined with SonoVue microbubbles and ultrasonic irradiation at 0.5 W/cm2 for 30 sec. mRNAs and protein levels of Cyclin D1, Bcl-2, Bax, Akt, p53 and Survivin were detected by reverse transcription-quantitative PCR and western blotting, respectively. Cell survival rate, proliferation, cell cycle and apoptosis were determined by Cell Counting Kit-8, cell double cytochemical staining and flow cytometry, respectively. It was found that using a combination of ultrasonic irradiation and the SonoVue microbubbles method increased the effectiveness of miR-378 transfection into hepatocellular carcinoma (HCC) cells, and increased the inhibition of cell survival and proliferation. Moreover, miR-378 increased the rate of apoptosis and upregulated the expression of Bax and p53, and suppressed the cell cycle and downregulated the expression of Cyclin D1, Bcl-2, Akt, β-catenin and Survivin much more effectively in the HCC cell line by applying the combined method. Thus, miR-378 was shown to be a suppressive factor to reduce proliferation and increase apoptosis in HCC cells. Additionally, the combination of ultrasonic irradiation and SonoVue microbubbles method was more efficient in the transfection of miRNA.

De novo genome assembly reconstructs the chromosomes from massive relatively short fragmented reads and serves as fundamental for studying new species where there is no reference genome. Wtdbg2 is a de novo assembler for long reads that is up to hundreds of kilobases. It is based on fuzzy-Bruijn graph (FBG) and is ten times faster than the cutting-edge assemblers such as Canu. However, the performance of wtdbg2 still requires further improvement: 1) it requires up to terabytes of memory to compute the assembly, which is infeasible to run on commodity server; 2) it requires tens of hours for assembling on large datasets such as genomes of homo sapiens. To address the above drawbacks, we propose several optimization techniques for accelerating wtdbg2 on commodity server, including a memory auto-tuning scheme, sequence alignment optimization and intermediate result elimination in the output procedure. We compare the optimized wtdbg2 with the original implementation and two cutting-edge assemblers on real-world datasets. The experiment results demonstrate that optimized wtdbg2 achieves maximum and average speedup of 2.31\(\times \) and 1.54\(\times \) respectively. In addition, our proposed optimization reduces the memory usage of wtdbg2 by 39.5% without affecting the correctness.

Gradient Boosted Decision Trees (GBDT) is a practical machine learning method, which has been widely used in various application fields such as recommendation system. Optimizing the performance of GBDT on heterogeneous many-core processors exposes several challenges such as designing efficient parallelization scheme and mitigating the latency of irregular memory access. In this paper, we propose swGBDT, an efficient GBDT implementation on Sunway processor. In swGBDT, we divide the 64 CPEs in a core group into multiple roles such as loader, saver and worker in order to hide the latency of irregular global memory access. In addition, we partition the data into two granularities such as block and tile to better utilize the LDM on each CPE for data caching. Moreover, we utilize register communication for collaboration among CPEs. Our evaluation with representative datasets shows that swGBDT achieves 4.6\(\times \) and 2\(\times \) performance speedup on average compared to the serial implementation on MPE and parallel XGBoost on CPEs respectively.

Community Earth System Model (CESM) is one of the most popular climatology research models. However, the computation of CESM is quite expensive and usually lasts for weeks even on high-performance clusters. In this paper, we propose several optimization strategies to improve the parallelism of three hotspots in CESM on GPU. Specifically, we analyze the performance bottleneck of CESM and propose corresponding GPU accelerations. The experiment results show that after applying our GPU optimizations, the kernels of the physical model achieve significant performance speedup respectively.

Tasks are the basic unit of Spark application scheduling, and its execution is affected by various configurations of Spark cluster. Therefore, the prediction of task execution time is a challenging job. In this paper, we analyze the features of task execution procedure on different stages, and propose the method of prediction of each sub-stage execution time. Moreover, the correlative time overheads of GC and shuffle spill are analyzed in detail. As a result, we propose SPM, a task-level execution time prediction model. SPM can be used to predict the task execution time of each stage according to the input data size and configuration of parallelism. We further apply SPM to the Spark network emulation tool SNemu, which can determine the start time of each shuffle procedure for emulation effectively. Experimental results show that the prediction method can achieve high accuracy in a variety of Spark benchmarks on Hibench.

To speedup the accesses to massive amount of data, heterogeneous architecture has been widely adopted in the mainstream storage system. In such systems, load imbalance and scheduler overhead are the primary factors that slow down the I/O performance. In this paper, we propose an effective file scheduling strategy HSPP that includes statistic based file classification, partition with erasure coding and adaptive data placement to optimize load balance and read latency on the distributed heterogeneous storage system. The experiment results show that HSPP is superior than existing strategies in terms of load balance, read latency, and scheduling overhead.

Artificial light at night has been proposed as a global threat to biodiversity. Insectivorous bats are strictly nocturnal animals that are vulnerable to disruption from artificial light. Given that many light-sensitive bats tend to avoid night light during roost departure, it is often assumed that nighttime light pollution reduces their foraging opportunities, albeit empirical evidence in support of this hypothesis remains elusive. Here, we used least horseshoe bats, Rhinolophus pusillus, to assess whether white artificial light is detrimental for the opportunities of foraging. We manipulated the levels of ambient illumination and perceived predation risk inside the bat roost. We monitored bats' emergence activity using high-speed video and audio recording systems. DNA-based faecal dietary analysis and insect survey were applied to determine activity time of prey in foraging areas. Following experimentally manipulation of white light-emitting diode (LED) lighting 0–15 min after sunset, bat pass, flight duration, and echolocation pulse emission decreased. The mean emergence time of bats flying out was delayed by 14 min under lit treatment compared with the dark control. Only 10% of bats left for foraging during 40 min of light exposure. Aversive effects of LED light on bat emergence were robust regardless of the presence of a potential predator. Insect prey reached a peak of abundance between 30 and 60 min after sunset. These results demonstrate that white artificial light hinders evening emergence behavior in least horseshoe bats, leading to a mismatch between foraging onset and peak food availability. Our findings highlight that light pollution overrides foraging onset, suggesting the importance of improving artificial lighting scheme near the roosts of light-sensitive bats.

In light of emerging antibiotic resistance, synthesis of active, environmental friendly antimicrobial alternatives becomes increasingly necessary. In this study, ZnO quantum dots (ZnO QDs) were developed by the sol-gel method and characterized. The antibacterial activities of ZnO QDs againstEscherichia coli(E. coli),Staphylococcus aureus(S. aureus) andSalmonella Pullorum(S. Pullorum) were systematically investigated. Moreover, the protective effects of ZnO QDs on Salmonella-caused pullorosis in chicks were also explored. The results indicated that the size range of ZnO QDs was 3-6 nm. Antibacterial results showed that ZnO QDs treatment inhibited the growth ofE. coli,S. aureus, andS. Pullorumin the rate of 87.06 ± 0.98%, 94.75 ± 2.28%, and 85.55 ± 1.15%, respectively. Its excellent antibacterial property was manifested with the minimum inhibitory concentration of 0.7812, 0.0976, and 0.1953 mg ml-1, which may be attributed to the production of reactive oxygen species, the dissolution of Zn2+ions, and the loss of cell integrity. Furthermore, in thein vivotest, the ZnO QDs effectively reduced the mortality of chicks infected withS. Pullorumvia regulating the balance of the intestinal flora, protecting liver and intestine, and modulating the balance of antioxidation systems. This study reveals that ZnO QDs exerts remarkably antibacterial activityin vitroand anti-pullorosis effect in chicks.

Generative models (GMs) such as Generative Adversary Network (GAN) and Variational Auto-Encoder (VAE) have thrived these years and achieved high-quality results in generating new samples. Especially in Computer Vision, GMs have been used in image inpainting, denoising, and completion, which can be treated as the inference from observed pixels to corrupted pixels. However, images are hierarchically structured, which are quite different from many real-world inference scenarios with non-hierarchical features. These inference scenarios contain heterogeneous stochastic variables and irregular mutual dependences. Traditionally they are modeled by Bayesian Network (BN). However, the learning and inference of BN model are NP-hard thus the number of stochastic variables in BN is highly constrained. In this paper, we adapt typical GMs to enable heterogeneous learning and inference in polynomial time. We also propose an extended autoregressive (EAR) model and an EAR with adversary loss (EARA) model and give theoretical results on their effectiveness. Experiments on several BN datasets show that our proposed EAR model achieves the best performance in most cases compared to other GMs. Except for black box analysis, we've also done a serial of experiments on Markov border inference of GMs for white box analysis and give theoretical results.

Canonical Polyadic Decomposition (CPD) is one of the most popular methods in tensor decomposition and plays an important role in big data analysis. For sparse tensor, the major computation procedure in CPD, known as matricized tensor times Khatri-Rao product (MTTKRP), exhibits discontiguous memory access and becomes the performance bottleneck from achieving high performance on emerging processor architectures. In this paper, we propose swCPD, an efficient CPD implementation on the many-core Sunway architecture. The main idea adopted in swCPD is a hierarchical partitioning mechanism. From the computation perspective, the 64 CPEs are divided into eight groups, each group contains seven workers and one controller. From the data perspective, we partition the sparse tensor into different granularities including blocks, bands and tiles. Moreover, we develop a communication mechanism through register communication for cooperation between CPEs. We evaluate our implementation with both synthesized and real-world datasets, which achieves better performance than two cutting-edge CPD implementations.

The authors describe a fluorometric strategy for the determination of dopamine (DA). It is based on the use of aptamer-functionalized MoS2 quantum dots (QDs) and MoS2 nanosheets (NSs). The QDs and NSs were extensively characterized with regard to their physical and chemical properties using methods such as TEM, XRD, FT-IR, EDX and molecular spectroscopies. The aptamer against dopamine was labeled with QDs acting as the energy donor in an energy transfer system, while the NSs serve as the energy acceptor. Under the optimal conditions, the fluorescence (FL) intensity (best measured at excitation/emission peaks of 315/412 nm) increases with increasing DA concentration in the range from 0.1 nM to 1000 nM, with a lower detection limit of 45 pM. The method was successfully applied to the determination of DA in complex matrices. In our perception, the method has a wide scope in that it may be extended to other biomolecules for which respective aptamer are available. The QDs show excellent optical properties, good stability, low cytotoxicity, and may also be applied to fluorometric imaging of live cells.

Existing distributed tracing tools such as HTrace use static probabilistic samplers to collect the function call trees for performance analysis, which may fail to capture important but less executed function call trees and thus miss the opportunities for performance optimization. To address the above problem, we propose ASTracer, a new distributed tracing tool with two adaptive samplers. The advantage of adaptive samplers is that they can adjust the sampling rate dynamically, which is able to capture comprehensive function call trees and in the meanwhile maintain the size of trace file acceptable. In addition, we propose an auto-tuning mechanism to search for the optimal parameter settings of the adaptive samplers in ASTracer. The experiment results demonstrate the adaptive samplers are more effective in tracing the function call trees compared to probabilistic sampler. Moreover, we provide several case studies to demonstrate the usage of ASTracer in identifying potential performance bottlenecks.

International audience; The performance issue of HDFS has always been a great concern due to its widely adoption in both production and research environments. However, a fine-grained performance analysis tool is missing to effectively identify the bottlenecks as well as to provide useful guidance for performance optimization. In this paper, we propose a fine-grained performance bottleneck analysis tool, which extends HTrace with fine-grained instrumentation points that are missing in Hadoop official distribution. In addition, we propose an effective trace merging method that improves the understandability of our analysis. We analyze the performance of HDFS under different kinds of workloads and get undiscovered insights.

Alzheimer’s disease (AD) is a progressive and deadly neurodegenerative disease that is characterized by memory loss, cognitive impairment and dementia. Several hypotheses have been proposed for the pathogenesis based on the pathological changes in the brain of AD patients during the last few decades. Unfortunately, there is no effective agents/therapies to prevent or control AD at present. Currently, only a few drugs, which function as acetylcholinesterase (AChE) inhibitors or N-methyl-Daspartate (NMDA) receptor antagonists, are available to alleviate symptoms. Since many small molecule natural products have shown their functions as agonists or antagonists of receptors, as well as inhibitors of enzymes and proteins in the brain during the development of central nervous system (CNS) drugs, it is likely that natural products will play an important role in anti-AD drug development. We review recent papers on using small molecule natural products as drug candidates for the treatment of AD. These natural products possess antioxidant, anti-inflammatory, anticholinesterase, anti-amyloidogenic and neuroprotective activities. Moreover, bioactive natural products intended to be used for preventing AD, reducing the symptoms of AD and the new targets for treatment of AD are summarized.

The authors describe a fluorometric strategy for the determination of dopamine (DA). It is based on the use of aptamer-functionalized MoS

Histone acetyltransferase GCN5 is a critical component of the TGF-β/Smad signaling pathway in breast cancer cells; however, it remains unknown whether it is involved in the development and progression of breast cancer. The present study investigated the role of GCN5 in the induction of the EMT by TGF-β1 in breast cancer cells and its underlying molecular mechanism of action. GCN5 activity was elevated and GCN5 mRNA expression and protein expression were increased in MDA-MB231 cells following stimulation with TGF-β1. Furthermore, TGF-β1 stimulation decreased expression of the epithelial cell marker E-cadherin and increased expression of the mesenchymal cell markers, N-cadherin and vimentin, as well as the expression of other EMT markers, including snail and slug. However, these changes were reversed following GCN5 knockdown leading to the downregulation of GCN5 expression. GCN5 knockdown also inhibited the viability, migration and invasion of MDA-MB231 cells, decreased the expression of p-STAT3, p-AKT, MMP9 and E2F1, and increased the expression of p21 in MDA-MB231 cells compared with cells stimulated with TGF-β1 alone. Therefore, GCN5 may work downstream of TGF-β/Smad signaling pathway to regulate the EMT in breast cancer.

Spark is a popular big data framework aiming to speed up computation by caching data in memory. Spark splits the execution of an application into multiple operations which form a directed acyclic graph. Operations that only depend on the data partitions from their parents are grouped into a task. Traditional performance analysis of big data framework mostly studies straggler task and proposes mitigation technique which ignores the deeper reasoning about the efficiency of the internal operations. In addition, task level analysis cannot correlate ill-behaved task to the specific source code. In this work, we present a tool that dynamically instruments Spark to discover inefficient operations without changing the source code of both the framework and the application. In addition, we propose a new score function for each operation within a stage, which synthetically considers the impact of pipeline duration, peer operation and operation duration. Moreover, we also propose a characterization method that performs clustering analysis on the sampled JVM features in order to better understand the cause of the inefficient operations and provide useful guidance for performance optimization.

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.

Cloud service providers usually offer IPsec VPN services to tenants by deploying the software IPsec gateway on the virtual machine. However, the current software IPsec gateway solutions cannot make full use of the allocated multi-core virtual machine resources and unable to meet the performance requirement of tenants. In order to optimize the IPsec gateway performance, the flow processing load must be properly allocated to multi-cores considering the multiple dimensions of load to improve the throughput of IPsec gateway. In this paper, we propose an optimizing scheme which separates the encryption and decryption computation from the packet forwarding process in the IPsec gateway, and implements fine-grained network flows scheduling in parallel processors. Furthermore, we present an adaptive load balancing algorithm based on quantifying the load of each processing core in real-time. Experimental results show that the performance of the IPsec gateway has significant improvement.

Straggler task is commonly considered as the major bottleneck in parallel data processing. Previous work mainly focuses on the coarse-grained straggler detection and optimization such as speculative scheduling. However, fine-grained root-cause analysis of straggler tasks is rarely considered. In addition, existing work simply depends on empirical analysis, which lacks of useful guidance to performance optimization. In this paper, we propose a new methodology of fine-grained straggler root-cause analysis using machine learning. We collect raw metrics from Spark event log and hardware sampling tool, and refine them into high-level metrics for model learning. Then we present the root-cause analysis of stragglers through CART tree. A customized prune method is also applied to improve analysis accuracy. From the analysis, we derive several new findings beyond the well known causes of stragglers. Our work provides a new perspective on identifying and understanding the inefficiency in parallel data processing programs by applying machine learning techniques to fine-grained root-cause analysis of straggler tasks.