Showing papers by "Southwest University published in 2019"

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.

Reduced default mode network functional connectivity in patients with recurrent major depressive disorder.

Abstract: Major depressive disorder (MDD) is common and disabling, but its neuropathophysiology remains unclear. Most studies of functional brain networks in MDD have had limited statistical power and data analysis approaches have varied widely. The REST-meta-MDD Project of resting-state fMRI (R-fMRI) addresses these issues. Twenty-five research groups in China established the REST-meta-MDD Consortium by contributing R-fMRI data from 1,300 patients with MDD and 1,128 normal controls (NCs). Data were preprocessed locally with a standardized protocol before aggregated group analyses. We focused on functional connectivity (FC) within the default mode network (DMN), frequently reported to be increased in MDD. Instead, we found decreased DMN FC when we compared 848 patients with MDD to 794 NCs from 17 sites after data exclusion. We found FC reduction only in recurrent MDD, not in first-episode drug-naive MDD. Decreased DMN FC was associated with medication usage but not with MDD duration. DMN FC was also positively related to symptom severity but only in recurrent MDD. Exploratory analyses also revealed alterations in FC of visual, sensory-motor, and dorsal attention networks in MDD. We confirmed the key role of DMN in MDD but found reduced rather than increased FC within the DMN. Future studies should test whether decreased DMN FC mediates response to treatment. All R-fMRI indices of data contributed by the REST-meta-MDD consortium are being shared publicly via the R-fMRI Maps Project.

Interactive effects of drought and heat stresses on morpho-physiological attributes, yield, nutrient uptake and oxidative status in maize hybrids.

Abstract: Maize is a sensitive crop to drought and heat stresses, particularly at the reproductive stages of development. The present study investigated the individual and interactive effects of drought (50% field capacity) and heat (38 °C/30 °C) stresses on morpho-physiological growth, yield, nutrient uptake and oxidative metabolism in two maize hybrids i.e., ‘Xida 889’ and ‘Xida 319’. The stress treatments were applied at tasseling stage for 15 days. Drought, heat and drought + heat stress caused oxidative stress by the over-production of ROS (O2−, H2O2, OH−) and enhanced malondialdehyde contents, which led to reduced photosynthetic components, nutrients uptake and yield attributes. The concurrent occurrence of drought and heat was more severe for maize growth than the single stress. However, both stresses induced the metabolites accumulation and enzymatic and non-enzymatic antioxidants to prevent the oxidative damage. The performance of Xida 899 was more prominent than the Xida 319. The greater tolerance of Xida 889 to heat and drought stresses was attributed to strong antioxidant defense system, higher osmolyte accumulation, and maintenance of photosynthetic pigments and nutrient balance compared with Xida 319.

Energy-Efficient Dynamic Computation Offloading and Cooperative Task Scheduling in Mobile Cloud Computing

Abstract: Mobile cloud computing (MCC) as an emerging and prospective computing paradigm, can significantly enhance computation capability and save energy for smart mobile devices (SMDs) by offloading computation-intensive tasks from resource-constrained SMDs onto resource-rich cloud. However, how to achieve energy-efficient computation offloading under hard constraint for application completion time remains a challenge. To address such a challenge, in this paper, we provide an energy-efficient dynamic offloading and resource scheduling (eDors) policy to reduce energy consumption and shorten application completion time. We first formulate the eDors problem into an energy-efficiency cost (EEC) minimization problem while satisfying task-dependency requirement and completion time deadline constraint. We then propose a distributed eDors algorithm consisting of three subalgorithms of computation offloading selection, clock frequency control, and transmission power allocation. Next, we show that computation offloading selection depends on not only the computing workload of a task, but also the maximum completion time of its immediate predecessors and the clock frequency and transmission power of the mobile device. Finally, we provide experimental results in a real testbed and demonstrate that the eDors algorithm can effectively reduce EEC by optimally adjusting CPU clock frequency of SMDs in local computing, and adapting the transmission power for wireless channel conditions in cloud computing.

Discovery of TaFeSb-based half-Heuslers with high thermoelectric performance

Abstract: Discovery of thermoelectric materials has long been realized by the Edisonian trial and error approach. However, recent progress in theoretical calculations, including the ability to predict structures of unknown phases along with their thermodynamic stability and functional properties, has enabled the so-called inverse design approach. Compared to the traditional materials discovery, the inverse design approach has the potential to substantially reduce the experimental efforts needed to identify promising compounds with target functionalities. By adopting this approach, here we have discovered several unreported half-Heusler compounds. Among them, the p-type TaFeSb-based half-Heusler demonstrates a record high ZT of ~1.52 at 973 K. Additionally, an ultrahigh average ZT of ~0.93 between 300 and 973 K is achieved. Such an extraordinary thermoelectric performance is further verified by the heat-to-electricity conversion efficiency measurement and a high efficiency of ~11.4% is obtained. Our work demonstrates that the TaFeSb-based half-Heuslers are highly promising for thermoelectric power generation. The discovery of thermodynamically stable thermoelectric materials for power generation has relied on empirical methods that were not effective. Here, the authors apply the inverse design approach to identify and experimentally realize TaFeSb-based half Heuslers with high thermoelectric performance.

Circular RNA: a novel biomarker and therapeutic target for human cancers.

Abstract: Circular (circ)RNAs, a newly recognized class of noncoding RNA, have been implicated in the occurrence and development of several diseases, including neurological and cardiovascular diseases. Studies of human tumors, including those of liver cancer, gastric cancer, lung cancer and colorectal cancer, have shown differential expression profiles of circRNAs, suggesting regulatory roles in cancer pathogenesis and metastasis. In this review, we discuss the most recent research into tumor-related circRNAs, providing a comprehensive summary of the expression or/and function of these circRNAs and proposing rational perspectives on the potential clinical application of circRNAs as helpful biomarkers or therapeutic targets in human tumors.

Whole-genome resequencing reveals Brassica napus origin and genetic loci involved in its improvement.

Abstract: Brassica napus (2n = 4x = 38, AACC) is an important allopolyploid crop derived from interspecific crosses between Brassica rapa (2n = 2x = 20, AA) and Brassica oleracea (2n = 2x = 18, CC). However, no truly wild B. napus populations are known; its origin and improvement processes remain unclear. Here, we resequence 588 B. napus accessions. We uncover that the A subgenome may evolve from the ancestor of European turnip and the C subgenome may evolve from the common ancestor of kohlrabi, cauliflower, broccoli, and Chinese kale. Additionally, winter oilseed may be the original form of B. napus. Subgenome-specific selection of defense-response genes has contributed to environmental adaptation after formation of the species, whereas asymmetrical subgenomic selection has led to ecotype change. By integrating genome-wide association studies, selection signals, and transcriptome analyses, we identify genes associated with improved stress tolerance, oil content, seed quality, and ecotype improvement. They are candidates for further functional characterization and genetic improvement of B. napus. Brassica napus is a globally important oil crop, but the origin of the allotetraploid genome and its improvement process are largely unknown. Here, the authors take a population genetic approach to resolve its origin and evolutionary history, and identify candidate genes related to important agricultural traits.

Hate speech detection: A solved problem? The challenging case of long tail on Twitter

Abstract: In recent years, the increasing propagation of hate speech on social media and the urgent need for effective counter-measures have drawn significant investment from governments, companies, and researchers. A large number of methods have been developed for automated hate speech detection online. This aims to classify textual content into non-hate or hate speech, in which case the method may also identify the targeting characteristics (i.e., types of hate, such as race, and religion) in the hate speech. However, we notice significant difference between the performance of the two (i.e., non-hate v.s. hate). In this work, we argue for a focus on the latter problem for practical reasons. We show that it is a much more challenging task, as our analysis of the language in the typical datasets shows that hate speech lacks unique, discriminative features and therefore is found in the 'long tail' in a dataset that is difficult to discover. We then propose Deep Neural Network structures serving as feature extractors that are particularly effective for capturing the semantics of hate speech. Our methods are evaluated on the largest collection of hate speech datasets based on Twitter, and are shown to be able to outperform the best performing method by up to 5 percentage points in macro-average F1, or 8 percentage points in the more challenging case of identifying hateful content.

High sensitive and low-concentration sulfur dioxide (SO2) gas sensor application of heterostructure NiO-ZnO nanodisks

Abstract: This paper reports the synthesis and characterization of NiO-ZnO nanodisks synthesized through a facile hydrothermal method. The morphological characterizations confirmed the formation of well-defined nanodisks in high density with an average thickness of 60 ± 5 nm. The x-ray diffraction analysis confirmed the well incorporation of the NiO into the matrix of ZnO crystal and the average crystallite size for the NiO-ZnO nanodisks was found to be 39.71 nm. The compositional analysis revealed the formation of pure NiO-ZnO nanostructures. The synthesized NiO-ZnO nanodisks were used as electrode materials to fabricate Sulfur dioxide (SO2) gas sensors and the gas sensor response and recovery times were systematically analyzed with respect to the operating temperatures and the SO2 gas concentration. The observed sensor gas response, response time and recovery time of the fabricated NiO-ZnO nanodisks based gas sensor were 16.25, 52 s and 41 s, respectively, towards 20 ppm SO2 gas at an optimized temperature of 240 °C. Thus, it is believed that the NiO-ZnO nanodisks could be a promising candidate for the fabrication of efficient gas sensors towards toxic and hazardous gases.

An Overview of Scoring Functions Used for Protein–Ligand Interactions in Molecular Docking

Abstract: Currently, molecular docking is becoming a key tool in drug discovery and molecular modeling applications. The reliability of molecular docking depends on the accuracy of the adopted scoring function, which can guide and determine the ligand poses when thousands of possible poses of ligand are generated. The scoring function can be used to determine the binding mode and site of a ligand, predict binding affinity and identify the potential drug leads for a given protein target. Despite intensive research over the years, accurate and rapid prediction of protein-ligand interactions is still a challenge in molecular docking. For this reason, this study reviews four basic types of scoring functions, physics-based, empirical, knowledge-based, and machine learning-based scoring functions, based on an up-to-date classification scheme. We not only discuss the foundations of the four types scoring functions, suitable application areas and shortcomings, but also discuss challenges and potential future study directions.

Completion Time Minimization for Multi-UAV-Enabled Data Collection

Abstract: Energy consumption is one of the important design aspect for data collection in wireless sensor networks (WSNs). This paper studies data collection from a set of sensor nodes (SNs) in WSNs enabled by multiple unmanned aerial vehicles (UAVs). We aim to minimize the maximum mission completion time among all UAVs by jointly optimizing the UAV trajectory, as well as the wake-up scheduling and association for SNs, while ensuring that each SN can successfully upload the targeting amount of data with a given energy budget. The formulated problem is a non-convex problem which is difficult to be solved directly. To tackle this problem, we first propose a simple scheme that each UAV only collects data while hovering, termed as hovering mode (Hmode) . For this mode, in order to find the optimized hovering locations for each SN and the serving order among all locations, we propose an efficient algorithm by leveraging the min–max multiple Traveling Salesman Problem (min–max m-TSP) and convex optimization techniques. Furthermore, we propose the more general scheme that enables continuous data collection even while flying, termed as flying mode (Fmode) . By leveraging bisection method and time discretization technique, the original problem is transformed into a discretized equivalent with a finite number of optimization variables, based on which a Karush–Kuhn–Tucker (KKT) solution is obtained by applying the successive convex approximation (SCA) technique. The simulation results show that the proposed multi-UAV enabled data collection with joint trajectory and communication design achieves significant performance gains over the benchmark schemes.

Progress in molecular docking

Abstract: In recent years, since the molecular docking technique can greatly improve the efficiency and reduce the research cost, it has become a key tool in computer-assisted drug design to predict the binding affinity and analyze the interactive mode. This study introduces the key principles, procedures and the widely-used applications for molecular docking. Also, it compares the commonly used docking applications and recommends which research areas are suitable for them. Lastly, it briefly reviews the latest progress in molecular docking such as the integrated method and deep learning. Limited to the incomplete molecular structure and the shortcomings of the scoring function, current docking applications are not accurate enough to predict the binding affinity. However, we could improve the current molecular docking technique by integrating the big biological data into scoring function.

Overcoming the Reticuloendothelial System Barrier to Drug Delivery with a “Don’t-Eat-Us” Strategy

Abstract: Overcoming the reticuloendothelial system (RES) has long been a vital challenge to nanoparticles as drug carriers. Modification of nanoparticles with polyethylene glycol helps them avoid clearance by macrophages but also suppresses their internalization by target cells. To overcome this paradox, we developed an RES-specific blocking system utilizing a "don't-eat-us" strategy. First, a CD47-derived, enzyme-resistant peptide ligand was designed and placed on liposomes (d-self-peptide-labeled liposome, DSL). After mainline administration, DSL was quickly adsorbed onto hepatic phagocyte membranes (including those of Kupffer cells and liver sinusoidal endothelial cells), forming a long-lasting mask that enclosed the cell membranes and thus reducing interactions between phagocytes and subsequently injected nanoparticles. Compared with blank conventional liposomes (CL), DSL blocked the RES at a much lower dose, and the effect was sustained for a much longer time, highly prolonging the elimination half-life of the subsequently injected nanoparticles. This "don't-eat-us" strategy by DSL was further verified on the brain-targeted delivery against a cryptococcal meningitis model, providing dramatically enhanced brain accumulation of the targeted delivery system and superior therapeutic outcome of model drug Amphotericin B compared with CL. Our study demonstrates a strategy that blocks the RES by masking phagocyte surfaces to prolong nanoparticle circulation time without excess modification and illustrates its utility in enhancing nanoparticle delivery.

Physical Layer Security in Cognitive Radio Inspired NOMA Network

Abstract: This paper investigates physical layer security (PLS) in cognitive radio inspired non-orthogonal multiple access (CR-NOMA) networks with multiple primary and secondary users. To manage the interferences among the users and guarantee the quality of services of primary users, a new secure NOMA transmission strategy is designed, where the primary and secondary users are paired according to their channel gains, respectively, and power-domain NOMA is employed to transmit the signal. Then, the closed-form expressions for connection outage probability, secrecy outage probability, and effective secrecy throughput are derived for the primary users over Nakagami- $m$ fading channels when the secondary users are considered as eavesdroppers. Typically, the secrecy performance can be improved by pairing the primary users with best channel gains or reducing the number of secondary users. In addition, we also investigate the performances of secondary users by deriving the closed-form expressions for throughput of secondary network. Furthermore, simulations are conducted to verify our analysis results and provide insights into the impact of the parameters on system performance.

Multilevel Microstructured Flexible Pressure Sensors with Ultrahigh Sensitivity and Ultrawide Pressure Range for Versatile Electronic Skins.

Abstract: Flexible pressure sensors as electronic skins have attracted wide attention to their potential applications for healthcare and intelligent robotics. However, the tradeoff between their sensitivity and pressure range restricts their practical applications in various healthcare fields. Herein, a cost-effective flexible pressure sensor with an ultrahigh sensitivity over an ultrawide pressure-range is developed by combining a sandpaper-molded multilevel microstructured polydimethylsiloxane and a reduced oxide graphene film. The unique multilevel microstructure via a two-step sandpaper-molding method leads to an ultrahigh sensitivity (2.5-1051 kPa-1 ) and can detect subtle and large pressure over an ultrawide range (0.01-400 kPa), which covers the overall pressure regime in daily life. Sharp increases in the contact area and additional contact sites caused by the multilevel microstructures jointly contribute to such unprecedented performance, which is confirmed by in situ observation of the gap variations and the contact states of the sensor under different pressures. Examples of the flexible pressure sensors are shown in potential applications involving the detection of various human physiological signals, such as breathing rate, vocal-cord vibration, heart rate, wrist pulse, and foot plantar pressure. Another object manipulation application is also demonstrated, where the material shows its great potential as electronic skin intelligent robotics and prosthetic limbs.

One-step controllable synthesis of amorphous (Ni-Fe)Sx/NiFe(OH)y hollow microtube/sphere films as superior bifunctional electrocatalysts for quasi-industrial water splitting at large-current-density

Abstract: Crystalline transition-metal chalcogenides with (oxy)-hydroxides hybrids multiple nanoarchitectures are a new type of promising bifunctional electrocatalysts for electrolysis of water, but their amorphous states are scarcely studied. Herein, a facile electrodeposited approach is presented to fabricate hierarchically amorphous (Ni-Fe)Sx/NiFe(OH)y films on Nickel foam. By accurately tuning multi-components and electrochemical-parameters resulting in abundant micro-tube/sphere morphologies and phase evolution to obtain unique amorphous nano-cluster architectures, the (Ni-Fe)Sx/NiFe(OH)y catalyst performs super electrocatalytic performance, driving the current density of 100 mA·cm−2 at ultralow overpotential of 124 mV and 290 mV for hydrogen and oxygen evolution reaction in 1 M KOH solution with first-class long-term stability for at least 50 h, respectively. In addition, the bimetallic Ni-Fe sulfides and NiFe hydroxides are confirmed to be highly-intrinsic active components for HER and OER. More importantly, the (Ni-Fe)Sx/NiFe(OH)y material directly as cathode and anode electrodes, achieves 10 mA·cm−2 at low electrolytic voltage of 1.46 V in 1 M KOH, even at large current density of 1200 mA·cm-2 only needing 2.2 V as well as super-durability at 1000 mA·cm-2 for 50 h in quasi-industrial conditions. Further experimental results reveal that both temperature and appropriate alkalinity are in favour of reducing the overall hydrolytic overpotential due to accelerating sluggish thermodynamics and dynamics. Parallelly, the bifunctional (Ni-Fe)Sx/NiFe(OH)y electrode is one of the best efficient electrocatalysts in alkaline electrolyte up to now and expected for large-scale industrial water-splitting at large-current-density.