Showing papers by "Sun Yat-sen University published in 2019"

Applications of Deep Reinforcement Learning in Communications and Networking: A Survey

Abstract: This paper presents a comprehensive literature review on applications of deep reinforcement learning (DRL) in communications and networking. Modern networks, e.g., Internet of Things (IoT) and unmanned aerial vehicle (UAV) networks, become more decentralized and autonomous. In such networks, network entities need to make decisions locally to maximize the network performance under uncertainty of network environment. Reinforcement learning has been efficiently used to enable the network entities to obtain the optimal policy including, e.g., decisions or actions, given their states when the state and action spaces are small. However, in complex and large-scale networks, the state and action spaces are usually large, and the reinforcement learning may not be able to find the optimal policy in reasonable time. Therefore, DRL, a combination of reinforcement learning with deep learning, has been developed to overcome the shortcomings. In this survey, we first give a tutorial of DRL from fundamental concepts to advanced models. Then, we review DRL approaches proposed to address emerging issues in communications and networking. The issues include dynamic network access, data rate control, wireless caching, data offloading, network security, and connectivity preservation which are all important to next generation networks, such as 5G and beyond. Furthermore, we present applications of DRL for traffic routing, resource sharing, and data collection. Finally, we highlight important challenges, open issues, and future research directions of applying DRL.

Genome-wide gene-based analyses of weight loss interventions identify a potential role for NKX6.3 in metabolism.

Abstract: Hundreds of genetic variants have been associated with Body Mass Index (BMI) through genome-wide association studies (GWAS) using observational cohorts. However, the genetic contribution to efficient weight loss in response to dietary intervention remains unknown. We perform a GWAS in two large low-caloric diet intervention cohorts of obese participants. Two loci close to NKX6.3/MIR486 and RBSG4 are identified in the Canadian discovery cohort (n = 1166) and replicated in the DiOGenes cohort (n = 789). Modulation of HGTX (NKX6.3 ortholog) levels in Drosophila melanogaster leads to significantly altered triglyceride levels. Additional tissue-specific experiments demonstrate an action through the oenocytes, fly hepatocyte-like cells that regulate lipid metabolism. Our results identify genetic variants associated with the efficacy of weight loss in obese subjects and identify a role for NKX6.3 in lipid metabolism, and thereby possibly weight control. Individuals show large variability in their capacity to lose weight and maintain this weight. Here, the authors perform GWAS in two weight loss intervention cohorts and identify two genetic loci associated with weight loss that are taken forward for Bayesian fine-mapping and functional assessment in flies.

Current cancer situation in China: Good or bad news from the 2018 Global Cancer Statistics?

Abstract: Cancer is the leading cause of death in China and depicting the cancer pattern of China would provide basic knowhows on how to tackle it more effectively. In this study we have reviewed several reports of cancer burden, including the Global cancer statistics 2018 and Cancer statistics in China, 2015, along with the GLOBCAN 2018 online database, to investigate the differences of cancer patterns between China, the United States (USA) and the United Kingdom (UK). An estimated 4.3 million new cancer cases and 2.9 million new cancer deaths occurred in China in 2018. Compared to the USA and UK, China has lower cancer incidence but a 30% and 40% higher cancer mortality than the UK and USA, among which 36.4% of the cancer-related deaths were from the digestive tract cancers (stomach, liver, and esophagus cancer) and have relatively poorer prognoses. In comparison, the digestive cancer deaths only took up ≤ 5% of the total cancer deaths in either USA or UK. Other reasons for the higher mortality in China may be the low rate of early-stage cancers at diagnosis and non-uniformed clinical cancer treatment strategies performed by different regions. China is undergoing the cancer transition stage where the cancer spectrum is changing from developing country to developed country, with a rapidly increase cancer burden of colorectal, prostate, female breast cancers in addition to a high occurrence of infection-related and digestive cancers. The incidence of westernized lifestyle-related cancers in China (i.e. colorectal cancer, prostate, bladder cancer) has risen but the incidence of the digestive cancers has decreased from 2000 to 2011. An estimated 40% of the risk factors can be attributed to environmental and lifestyle factors either in China or other developed countries. Tobacco smoking is the single most important carcinogenic risk factor in China, contributing to ~ 24.5% of cancers in males. Chronic infection is another important preventable cancer contributor which is responsible for ~ 17% of cancers. Comprehensive prevention and control strategies in China should include effective tobacco-control policy, recommendations for healthier lifestyles, along with enlarging the coverage of effective screening, educating, and vaccination programs to better sensitize greater awareness control to the general public.

The Simons Observatory : Science goals and forecasts

Abstract: The Simons Observatory (SO) is a new cosmic microwave background experiment being built on Cerro Toco in Chile, due to begin observations in the early 2020s. We describe the scientific goals of the experiment, motivate the design, and forecast its performance. SO will measure the temperature and polarization anisotropy of the cosmic microwave background in six frequency bands centered at: 27, 39, 93, 145, 225 and 280 GHz. The initial configuration of SO will have three small-aperture 0.5-m telescopes and one large-aperture 6-m telescope, with a total of 60,000 cryogenic bolometers. Our key science goals are to characterize the primordial perturbations, measure the number of relativistic species and the mass of neutrinos, test for deviations from a cosmological constant, improve our understanding of galaxy evolution, and constrain the duration of reionization. The small aperture telescopes will target the largest angular scales observable from Chile, mapping ≈ 10% of the sky to a white noise level of 2 μK-arcmin in combined 93 and 145 GHz bands, to measure the primordial tensor-to-scalar ratio, r, at a target level of σ(r)=0.003. The large aperture telescope will map ≈ 40% of the sky at arcminute angular resolution to an expected white noise level of 6 μK-arcmin in combined 93 and 145 GHz bands, overlapping with the majority of the Large Synoptic Survey Telescope sky region and partially with the Dark Energy Spectroscopic Instrument. With up to an order of magnitude lower polarization noise than maps from the Planck satellite, the high-resolution sky maps will constrain cosmological parameters derived from the damping tail, gravitational lensing of the microwave background, the primordial bispectrum, and the thermal and kinematic Sunyaev-Zel'dovich effects, and will aid in delensing the large-angle polarization signal to measure the tensor-to-scalar ratio. The survey will also provide a legacy catalog of 16,000 galaxy clusters and more than 20,000 extragalactic sources.

Edge Intelligence: Paving the Last Mile of Artificial Intelligence With Edge Computing

Abstract: With the breakthroughs in deep learning, the recent years have witnessed a booming of artificial intelligence (AI) applications and services, spanning from personal assistant to recommendation systems to video/audio surveillance. More recently, with the proliferation of mobile computing and Internet of Things (IoT), billions of mobile and IoT devices are connected to the Internet, generating zillions bytes of data at the network edge. Driving by this trend, there is an urgent need to push the AI frontiers to the network edge so as to fully unleash the potential of the edge big data. To meet this demand, edge computing, an emerging paradigm that pushes computing tasks and services from the network core to the network edge, has been widely recognized as a promising solution. The resulted new interdiscipline, edge AI or edge intelligence (EI), is beginning to receive a tremendous amount of interest. However, research on EI is still in its infancy stage, and a dedicated venue for exchanging the recent advances of EI is highly desired by both the computer system and AI communities. To this end, we conduct a comprehensive survey of the recent research efforts on EI. Specifically, we first review the background and motivation for AI running at the network edge. We then provide an overview of the overarching architectures, frameworks, and emerging key technologies for deep learning model toward training/inference at the network edge. Finally, we discuss future research opportunities on EI. We believe that this survey will elicit escalating attentions, stimulate fruitful discussions, and inspire further research ideas on EI.

Turning foes to friends: targeting cancer-associated fibroblasts

Abstract: Current paradigms of cancer-centric therapeutics are usually not sufficient to eradicate the malignancy, as the cancer stroma may prompt tumour relapse and therapeutic resistance. Among all the stromal cells that populate the tumour microenvironment, cancer-associated fibroblasts (CAFs) are the most abundant and are critically involved in cancer progression. CAFs regulate the biology of tumour cells and other stromal cells via cell–cell contact, releasing numerous regulatory factors and synthesizing and remodelling the extracellular matrix, and thus these cells affect cancer initiation and development. The recent characterization of CAFs based on specific cell surface markers not only deepens our insight into their phenotypic heterogeneity and functional diversity but also brings CAF-targeting therapies for cancer treatment onto the agenda. In this Review, we discuss the current knowledge of biological hallmarks, cellular origins, phenotypical plasticity and functional heterogeneity of CAFs and underscore their contribution to cancer progression. Moreover, we highlight relevant translational advances and potential therapeutic strategies that target CAFs for cancer treatment. Cancer-associated fibroblasts (CAFs) are often the most abundant cell type in the tumour microenvironment. Here, Song and colleagues discuss how to target or harness these cells for cancer therapy. They highlight the progress made to date and the remaining challenges in bringing CAF-targeted therapies to the clinic.

Semiconductor polymeric graphitic carbon nitride photocatalysts: the “holy grail” for the photocatalytic hydrogen evolution reaction under visible light

Abstract: Semiconductor polymeric graphitic carbon nitride (g-C3N4) photocatalysts have attracted dramatically growing attention in the field of the visible-light-induced hydrogen evolution reaction (HER) because of their facile synthesis, easy functionalization, attractive electronic band structure, high physicochemical stability and photocatalytic activity. This review article presents a panorama of the latest advancements in the rational design and development of g-C3N4 and g-C3N4-based composite photocatalysts for HER application. Concretely, the review starts with the development history, synthetic strategy, electronic structure and physicochemical characteristics of g-C3N4 materials, followed by the rational design and engineering of various nanostructured g-C3N4 (e.g. thinner, highly crystalline, doped, and porous g-C3N4) photocatalysts for HER application. Then a series of highly efficient g-C3N4 (e.g., metal/g-C3N4, semiconductor/g-C3N4, metal organic framework/g-C3N4, carbon/g-C3N4, conducting polymer/g-C3N4, sensitizer/g-C3N4) composite photocatalysts are exemplified. Lastly, this review provides a comprehensive summary and outlook on the major challenges, opportunities, and inspiring perspectives for future research in this hot area on the basis of pioneering works. It is believed that the emerging g-C3N4-based photocatalysts will act as the “holy grail” for highly efficient photocatalytic HER under visible-light irradiation.

Dendrite‐Free Zinc Deposition Induced by Multifunctional CNT Frameworks for Stable Flexible Zn‐Ion Batteries

Abstract: The current boom of safe and renewable energy storage systems is driving the recent renaissance of Zn-ion batteries. However, the notorious tip-induced dendrite growth on the Zn anode restricts their further application. Herein, the first demonstration of constructing a flexible 3D carbon nanotube (CNT) framework as a Zn plating/stripping scaffold is constituted to achieve a dendrite-free robust Zn anode. Compared with the pristine deposited Zn electrode, the as-fabricated Zn/CNT anode affords lower Zn nucleation overpotential and more homogeneously distributed electric field, thus being more favorable for highly reversible Zn plating/stripping with satisfactory Coulombic efficiency rather than the formation of Zn dendrites or other byproducts. As a consequence, a highly flexible symmetric cell based on the Zn/CNT anode presents appreciably low voltage hysteresis (27 mV) and superior cycling stability (200 h) with dendrite-free morphology at 2 mA cm-2 , accompanied by a high depth of discharge (DOD) of 28%. Such distinct performance overmatches most of recently reported Zn-based anodes. Additionally, this efficient rechargeability of the Zn/CNT anode also enables a substantially stable Zn//MnO2 battery with 88.7% capacity retention after 1000 cycles and remarkable mechanical flexibility.

Blockchain for Internet of Things: A Survey

Abstract: Internet of Things (IoT) is reshaping the incumbent industry to smart industry featured with data-driven decision-making. However, intrinsic features of IoT result in a number of challenges, such as decentralization, poor interoperability, privacy, and security vulnerabilities. Blockchain technology brings the opportunities in addressing the challenges of IoT. In this paper, we investigate the integration of blockchain technology with IoT. We name such synthesis of blockchain and IoT as blockchain of things (BCoT). This paper presents an in-depth survey of BCoT and discusses the insights of this new paradigm. In particular, we first briefly introduce IoT and discuss the challenges of IoT. Then, we give an overview of blockchain technology. We next concentrate on introducing the convergence of blockchain and IoT and presenting the proposal of BCoT architecture. We further discuss the issues about using blockchain for fifth generation beyond in IoT as well as industrial applications of BCoT. Finally, we outline the open research directions in this promising area.

Increased atmospheric vapor pressure deficit reduces global vegetation growth

Abstract: Atmospheric vapor pressure deficit (VPD) is a critical variable in determining plant photosynthesis. Synthesis of four global climate datasets reveals a sharp increase of VPD after the late 1990s. In response, the vegetation greening trend indicated by a satellite-derived vegetation index (GIMMS3g), which was evident before the late 1990s, was subsequently stalled or reversed. Terrestrial gross primary production derived from two satellite-based models (revised EC-LUE and MODIS) exhibits persistent and widespread decreases after the late 1990s due to increased VPD, which offset the positive CO2 fertilization effect. Six Earth system models have consistently projected continuous increases of VPD throughout the current century. Our results highlight that the impacts of VPD on vegetation growth should be adequately considered to assess ecosystem responses to future climate conditions.

High-performance hybrid silicon and lithium niobate Mach–Zehnder modulators for 100 Gbit s −1 and beyond

Abstract: Optical modulators are at the heart of optical communication links. Ideally, they should feature low loss, low drive voltage, large bandwidth, high linearity, compact footprint and low manufacturing cost. Unfortunately, these criteria have been achieved only on separate occasions. Based on a silicon and lithium niobate hybrid integration platform, we demonstrate Mach–Zehnder modulators that simultaneously fulfil these criteria. The presented device exhibits an insertion loss of 2.5 dB, voltage–length product of 2.2 V cm in single-drive push–pull operation, high linearity, electro-optic bandwidth of at least 70 GHz and modulation rates up to 112 Gbit s−1. The high-performance modulator is realized by seamless integration of a high-contrast waveguide based on lithium niobate—a popular modulator material—with compact, low-loss silicon circuitry. The hybrid platform demonstrated here allows for the combination of ‘best-in-breed’ active and passive components, opening up new avenues for future high-speed, energy-efficient and cost-effective optical communication networks. Low-loss, high-speed and efficient optical modulators on a silicon platform are demonstrated.

Versatile Polydopamine Platforms: Synthesis and Promising Applications for Surface Modification and Advanced Nanomedicine

Abstract: As a mussel-inspired material, polydopamine (PDA), possesses many properties, such as a simple preparation process, good biocompatibility, strong adhesive property, easy functionalization, outstanding photothermal conversion efficiency, and strong quenching effect. PDA has attracted increasingly considerable attention because it provides a simple and versatile approach to functionalize material surfaces for obtaining a variety of multifunctional nanomaterials. In this review, recent significant research developments of PDA including its synthesis and polymerization mechanism, physicochemical properties, different nano/microstructures, and diverse applications are summarized and discussed. For the sections of its applications in surface modification and biomedicine, we mainly highlight the achievements in the past few years (2016-2019). The remaining challenges and future perspectives of PDA-based nanoplatforms are discussed rationally at the end. This timely and overall review should be desirable for a wide range of scientists and facilitate further development of surface coating methods and the production of PDA-based materials.

Underexposed Photo Enhancement Using Deep Illumination Estimation

Abstract: This paper presents a new neural network for enhancing underexposed photos. Instead of directly learning an image-to-image mapping as previous work, we introduce intermediate illumination in our network to associate the input with expected enhancement result, which augments the network's capability to learn complex photographic adjustment from expert-retouched input/output image pairs. Based on this model, we formulate a loss function that adopts constraints and priors on the illumination, prepare a new dataset of 3,000 underexposed image pairs, and train the network to effectively learn a rich variety of adjustment for diverse lighting conditions. By these means, our network is able to recover clear details, distinct contrast, and natural color in the enhancement results. We perform extensive experiments on the benchmark MIT-Adobe FiveK dataset and our new dataset, and show that our network is effective to deal with previously challenging images.

Convergence of Edge Computing and Deep Learning: A Comprehensive Survey

Abstract: Ubiquitous sensors and smart devices from factories and communities are generating massive amounts of data, and ever-increasing computing power is driving the core of computation and services from the cloud to the edge of the network. As an important enabler broadly changing people's lives, from face recognition to ambitious smart factories and cities, developments of artificial intelligence (especially deep learning, DL) based applications and services are thriving. However, due to efficiency and latency issues, the current cloud computing service architecture hinders the vision of "providing artificial intelligence for every person and every organization at everywhere". Thus, unleashing DL services using resources at the network edge near the data sources has emerged as a desirable solution. Therefore, edge intelligence, aiming to facilitate the deployment of DL services by edge computing, has received significant attention. In addition, DL, as the representative technique of artificial intelligence, can be integrated into edge computing frameworks to build intelligent edge for dynamic, adaptive edge maintenance and management. With regard to mutually beneficial edge intelligence and intelligent edge, this paper introduces and discusses: 1) the application scenarios of both; 2) the practical implementation methods and enabling technologies, namely DL training and inference in the customized edge computing framework; 3) challenges and future trends of more pervasive and fine-grained intelligence. We believe that by consolidating information scattered across the communication, networking, and DL areas, this survey can help readers to understand the connections between enabling technologies while promoting further discussions on the fusion of edge intelligence and intelligent edge, i.e., Edge DL.

Dendritic Neuron Model With Effective Learning Algorithms for Classification, Approximation, and Prediction

Abstract: An artificial neural network (ANN) that mimics the information processing mechanisms and procedures of neurons in human brains has achieved a great success in many fields, e.g., classification, prediction, and control. However, traditional ANNs suffer from many problems, such as the hard understanding problem, the slow and difficult training problems, and the difficulty to scale them up. These problems motivate us to develop a new dendritic neuron model (DNM) by considering the nonlinearity of synapses, not only for a better understanding of a biological neuronal system, but also for providing a more useful method for solving practical problems. To achieve its better performance for solving problems, six learning algorithms including biogeography-based optimization, particle swarm optimization, genetic algorithm, ant colony optimization, evolutionary strategy, and population-based incremental learning are for the first time used to train it. The best combination of its user-defined parameters has been systemically investigated by using the Taguchi’s experimental design method. The experiments on 14 different problems involving classification, approximation, and prediction are conducted by using a multilayer perceptron and the proposed DNM. The results suggest that the proposed learning algorithms are effective and promising for training DNM and thus make DNM more powerful in solving classification, approximation, and prediction problems.

Bioactive compounds and bioactivities of ginger (zingiber officinale roscoe)

Abstract: Ginger (Zingiber officinale Roscoe) is a common and widely used spice. It is rich in various chemical constituents, including phenolic compounds, terpenes, polysaccharides, lipids, organic acids, and raw fibers. The health benefits of ginger are mainly attributed to its phenolic compounds, such as gingerols and shogaols. Accumulated investigations have demonstrated that ginger possesses multiple biological activities, including antioxidant, anti-inflammatory, antimicrobial, anticancer, neuroprotective, cardiovascular protective, respiratory protective, antiobesity, antidiabetic, antinausea, and antiemetic activities. In this review, we summarize current knowledge about the bioactive compounds and bioactivities of ginger, and the mechanisms of action are also discussed. We hope that this updated review paper will attract more attention to ginger and its further applications, including its potential to be developed into functional foods or nutraceuticals for the prevention and management of chronic diseases.

Twenty-three unsolved problems in hydrology (UPH)–a community perspective

Abstract: This paper is the outcome of a community initiative to identify major unsolved scientific problems in hydrology motivated by a need for stronger harmonisation of research efforts. The procedure involved a public consultation through online media, followed by two workshops through which a large number of potential science questions were collated, prioritised, and synthesised. In spite of the diversity of the participants (230 scientists in total), the process revealed much about community priorities and the state of our science: a preference for continuity in research questions rather than radical departures or redirections from past and current work. Questions remain focused on the process-based understanding of hydrological variability and causality at all space and time scales. Increased attention to environmental change drives a new emphasis on understanding how change propagates across interfaces within the hydrological system and across disciplinary boundaries. In particular, the expansion of the human footprint raises a new set of questions related to human interactions with nature and water cycle feedbacks in the context of complex water management problems. We hope that this reflection and synthesis of the 23 unsolved problems in hydrology will help guide research efforts for some years to come.

METTL3 facilitates tumor progression via an m6A-IGF2BP2-dependent mechanism in colorectal carcinoma

Abstract: Colorectal carcinoma (CRC) is one of the most common malignant tumors, and its main cause of death is tumor metastasis. RNA N6-methyladenosine (m6A) is an emerging regulatory mechanism for gene expression and methyltransferase-like 3 (METTL3) participates in tumor progression in several cancer types. However, its role in CRC remains unexplored. Western blot, quantitative real-time PCR (RT-qPCR) and immunohistochemical (IHC) were used to detect METTL3 expression in cell lines and patient tissues. Methylated RNA immunoprecipitation sequencing (MeRIP-seq) and transcriptomic RNA sequencing (RNA-seq) were used to screen the target genes of METTL3. The biological functions of METTL3 were investigated in vitro and in vivo. RNA pull-down and RNA immunoprecipitation assays were conducted to explore the specific binding of target genes. RNA stability assay was used to detect the half-lives of the downstream genes of METTL3. Using TCGA database, higher METTL3 expression was found in CRC metastatic tissues and was associated with a poor prognosis. MeRIP-seq revealed that SRY (sex determining region Y)-box 2 (SOX2) was the downstream gene of METTL3. METTL3 knockdown in CRC cells drastically inhibited cell self-renewal, stem cell frequency and migration in vitro and suppressed CRC tumorigenesis and metastasis in both cell-based models and PDX models. Mechanistically, methylated SOX2 transcripts, specifically the coding sequence (CDS) regions, were subsequently recognized by the specific m6A “reader”, insulin-like growth factor 2 mRNA binding protein 2 (IGF2BP2), to prevent SOX2 mRNA degradation. Further, SOX2 expression positively correlated with METTL3 and IGF2BP2 in CRC tissues. The combined IHC panel, including “writer”, “reader”, and “target”, exhibited a better prognostic value for CRC patients than any of these components individually. Overall, our study revealed that METTL3, acting as an oncogene, maintained SOX2 expression through an m6A-IGF2BP2-dependent mechanism in CRC cells, and indicated a potential biomarker panel for prognostic prediction in CRC.

Diet–microbiota interactions and personalized nutrition

Abstract: Conceptual scientific and medical advances have led to a recent realization that there may be no single, one-size-fits-all diet and that differential human responses to dietary inputs may rather be driven by unique and quantifiable host and microbiome features. Integration of these person-specific host and microbiome readouts into actionable modules may complement traditional food measurement approaches in devising diets that are of benefit to the individual. Although many host-derived factors are hardwired and difficult to modulate, the microbiome may be more readily reshaped by environmental factors such as dietary exposures and is increasingly recognized to potentially impact human physiology by participating in digestion, the absorption of nutrients, shaping of the mucosal immune response and the synthesis or modulation of a plethora of potentially bioactive compounds. Thus, diet-induced microbiota alterations may be harnessed in order to induce changes in host physiology, including disease development and progression. However, major limitations in ‘big-data’ processing and analysis still limit our interpretive and translational capabilities concerning these person-specific host, microbiome and diet interactions. In this Review, we describe the latest advances in understanding diet–microbiota interactions, the individuality of gut microbiota composition and how this knowledge could be harnessed for personalized nutrition strategies to improve human health. In this Review, Kolodziejczyk, Zheng and Elinav describe the latest advances in understanding diet–microbiota interactions, the individuality of gut microbiota composition and how this knowledge could be harnessed for personalized nutrition strategies to improve human health.

The Chinese Society of Clinical Oncology (CSCO): clinical guidelines for the diagnosis and treatment of gastric cancer.

Abstract: China is one of the countries with the highest incidence of gastric cancer. There are differences in epidemiological characteristics, clinicopathological features, tumor biological characteristics, treatment patterns, and drug selection between gastric cancer patients from the Eastern and Western countries. Non-Chinese guidelines cannot specifically reflect the diagnosis and treatment characteristics for the Chinese gastric cancer patients. The Chinese Society of Clinical Oncology (CSCO) arranged for a panel of senior experts specializing in all sub-specialties of gastric cancer to compile, discuss, and revise the guidelines on the diagnosis and treatment of gastric cancer based on the findings of evidence-based medicine in China and abroad. By referring to the opinions of industry experts, taking into account of regional differences, giving full consideration to the accessibility of diagnosis and treatment resources, these experts have conducted experts’ consensus judgement on relevant evidence and made various grades of recommendations for the clinical diagnosis and treatment of gastric cancer to reflect the value of cancer treatment and meeting health economic indexes. This guideline uses tables and is complemented by explanatory and descriptive notes covering the diagnosis, comprehensive treatment, and follow-up visits for gastric cancer.

Global diversity and biogeography of bacterial communities in wastewater treatment plants

Abstract: Microorganisms in wastewater treatment plants (WWTPs) are essential for water purification to protect public and environmental health. However, the diversity of microorganisms and the factors that control it are poorly understood. Using a systematic global-sampling effort, we analysed the 16S ribosomal RNA gene sequences from ~1,200 activated sludge samples taken from 269 WWTPs in 23 countries on 6 continents. Our analyses revealed that the global activated sludge bacterial communities contain ~1 billion bacterial phylotypes with a Poisson lognormal diversity distribution. Despite this high diversity, activated sludge has a small, global core bacterial community (n = 28 operational taxonomic units) that is strongly linked to activated sludge performance. Meta-analyses with global datasets associate the activated sludge microbiomes most closely to freshwater populations. In contrast to macroorganism diversity, activated sludge bacterial communities show no latitudinal gradient. Furthermore, their spatial turnover is scale-dependent and appears to be largely driven by stochastic processes (dispersal and drift), although deterministic factors (temperature and organic input) are also important. Our findings enhance our mechanistic understanding of the global diversity and biogeography of activated sludge bacterial communities within a theoretical ecology framework and have important implications for microbial ecology and wastewater treatment processes.

Edge Intelligence: Paving the Last Mile of Artificial Intelligence with Edge Computing

Abstract: With the breakthroughs in deep learning, the recent years have witnessed a booming of artificial intelligence (AI) applications and services, spanning from personal assistant to recommendation systems to video/audio surveillance. More recently, with the proliferation of mobile computing and Internet-of-Things (IoT), billions of mobile and IoT devices are connected to the Internet, generating zillions Bytes of data at the network edge. Driving by this trend, there is an urgent need to push the AI frontiers to the network edge so as to fully unleash the potential of the edge big data. To meet this demand, edge computing, an emerging paradigm that pushes computing tasks and services from the network core to the network edge, has been widely recognized as a promising solution. The resulted new inter-discipline, edge AI or edge intelligence, is beginning to receive a tremendous amount of interest. However, research on edge intelligence is still in its infancy stage, and a dedicated venue for exchanging the recent advances of edge intelligence is highly desired by both the computer system and artificial intelligence communities. To this end, we conduct a comprehensive survey of the recent research efforts on edge intelligence. Specifically, we first review the background and motivation for artificial intelligence running at the network edge. We then provide an overview of the overarching architectures, frameworks and emerging key technologies for deep learning model towards training/inference at the network edge. Finally, we discuss future research opportunities on edge intelligence. We believe that this survey will elicit escalating attentions, stimulate fruitful discussions and inspire further research ideas on edge intelligence.

Effect of Laparoscopic vs Open Distal Gastrectomy on 3-Year Disease-Free Survival in Patients With Locally Advanced Gastric Cancer: The CLASS-01 Randomized Clinical Trial

Abstract: Importance Laparoscopic distal gastrectomy is accepted as a more effective approach to conventional open distal gastrectomy for early-stage gastric cancer. However, efficacy for locally advanced gastric cancer remains uncertain. Objective To compare 3-year disease-free survival for patients with locally advanced gastric cancer after laparoscopic distal gastrectomy or open distal gastrectomy. Design, Setting, and Patients The study was a noninferiority, open-label, randomized clinical trial at 14 centers in China. A total of 1056 eligible patients with clinical stage T2, T3, or T4a gastric cancer without bulky nodes or distant metastases were enrolled from September 2012 to December 2014. Final follow-up was on December 31, 2017. Interventions Participants were randomized in a 1:1 ratio after stratification by site, age, cancer stage, and histology to undergo either laparoscopic distal gastrectomy (n = 528) or open distal gastrectomy (n = 528) with D2 lymphadenectomy. Main Outcomes and Measures The primary end point was 3-year disease-free survival with a noninferiority margin of −10% to compare laparoscopic distal gastrectomy with open distal gastrectomy. Secondary end points of 3-year overall survival and recurrence patterns were tested for superiority. Results Among 1056 patients, 1039 (98.4%; mean age, 56.2 years; 313 [30.1%] women) had surgery (laparoscopic distal gastrectomy [n=519] vs open distal gastrectomy [n=520]), and 999 (94.6%) completed the study. Three-year disease-free survival rate was 76.5% in the laparoscopic distal gastrectomy group and 77.8% in the open distal gastrectomy group, absolute difference of −1.3% and a 1-sided 97.5% CI of −6.5% to ∞, not crossing the prespecified noninferiority margin. Three-year overall survival rate (laparoscopic distal gastrectomy vs open distal gastrectomy: 83.1% vs 85.2%; adjusted hazard ratio, 1.19; 95% CI, 0.87 to 1.64;P = .28) and cumulative incidence of recurrence over the 3-year period (laparoscopic distal gastrectomy vs open distal gastrectomy: 18.8% vs 16.5%; subhazard ratio, 1.15; 95% CI, 0.86 to 1.54;P = .35) did not significantly differ between laparoscopic distal gastrectomy and open distal gastrectomy groups. Conclusions and Relevance Among patients with a preoperative clinical stage indicating locally advanced gastric cancer, laparoscopic distal gastrectomy, compared with open distal gastrectomy, did not result in inferior disease-free survival at 3 years. Trial Registration ClinicalTrials.gov Identifier:NCT01609309