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In the era of large-scale pretrained models, artificial neural networks (ANNs) have excelled in natural language understanding (NLU) tasks. However, their success often necessitates substantial computational resources and energy consumption. To address this, we explore the potential of spiking neural networks (SNNs) in NLU—a promising avenue with demonstrated advantages, including reduced power consumption and improved efficiency due to their event-driven characteristics. We propose the SpikingMiniLM, a novel spiking Transformer model tailored for natural language understanding. We first introduce a multistep encoding method to convert text embeddings into spike trains. Subsequently, we redesign the attention mechanism and residual connections to make our model operate on the pure spike-based paradigm without any normalization technique. To facilitate stable and fast convergence, we propose a general parameter initialization method grounded in the stable firing rate principle. Furthermore, we apply an ANN-to-SNN knowledge distillation to overcome the challenges of pretraining SNNs. Our approach achieves a macro-average score of 75.5 on the dev sets of the GLUE benchmark, retaining 98% of the performance exhibited by the teacher model MiniLMv2. Our smaller model also achieves similar performance to BERTMINI with fewer parameters and much lower energy consumption, underscoring its competitiveness and resource efficiency in NLU tasks. [ABSTRACT FROM AUTHOR]

The identification of prototypical waveforms, such as sleep spindles and epileptic spikes, is crucial for the diagnosis of neurological disorders. These prototypical waveforms are usually recurrently presented in certain brain states, serving as potential biomarkers for clinical evaluations. Convolutional sparse coding (CSC) approaches have demonstrated strength in identifying recurrent patterns in time-series. However, existing CSC approaches do not explicitly explore state-specific patterns, making it difficult to identify state-related biomarkers. To address this problem, we propose state-sensitive CSC to learn state-specific prototypical waveforms. Specifically, we model signals of a certain state with specific waveforms that only appear frequently in this state and background waveforms that are independent of states. Based on this, state-sensitive CSC separates state-specific waveforms from background ones explicitly by incorporating incoherence constraints into optimizations. Experiments with epilepsy brain signals demonstrate that our approach can effectively identify prototypical waveforms in pre-ictal states, providing potential biomarkers for seizure prediction. Our approach provides a promising tool for automatic biomarker candidate identification. [ABSTRACT FROM AUTHOR]

Purpose: Grb2 associated binding protein 1 (Gab1) is an adaptor protein that is important for intracellular signal transduction which involved in several pathological process. However, the role of Gab1 in pressure overload-induced ventricular arrhythmias (VAs) remain poorly understood. In the current study, we aimed to test the role of Gab1 in VA susceptibility induced by pressure overload. Methods: We overexpressed Gab1 in the hearts using an adeno-associated virus 9 (AAV9) system through tail vein injection. Aortic banding (AB) surgery was performed in C57BL6/J mice to induce heart failure (HF). Four weeks following AB, histology, echocardiography, and biochemical analysis were conducted to investigate cardiac structural remodeling and electrophysiological studies were performed to check the electrical remodeling. Western blot analysis was used to explore the underlying mechanisms. Results: The mRNA and protein expression were downregulated in AB hearts compared to sham hearts. Gab1 overexpression significantly reversed AB-induced cardiac structural remodeling including ameliorated AB-induced cardiac dysfunction, cardiac fibrosis, and inflammatory response. Moreover, Gab1 overexpression also markedly alleviated AB-induced electrical remodeling including ion channel alterations and VA susceptibility. Mechanistically, we found that TLR4/MyD88/NF-κB contributes to the cardio protective effect of Gab1 overexpression on AB-induced VAs. Conclusions: Our study manifested that Gab1 may serve as a promising anti-arrhythmic target via inhibiting TLR4/MyD88/NF-κB signaling pathway induced by AB. [ABSTRACT FROM AUTHOR]

Significant progress has been made in image inpainting methods in recent years. However, they are incapable of producing inpainting results with reasonable structures, rich detail, and sharpness at the same time. In this paper, we propose the Pyramid-VAE-GAN network for image inpainting to address this limitation. Our network is built on a variational autoencoder (VAE) backbone that encodes high-level latent variables to represent complicated high-dimensional prior distributions of images. The prior assists in reconstructing reasonable structures when inpainting. We also adopt a pyramid structure in our model to maintain rich detail in low-level latent variables. To avoid the usual incompatibility of requiring both reasonable structures and rich detail, we propose a novel cross-layer latent variable transfer module. This transfers information about long-range structures contained in high-level latent variables to low-level latent variables representing more detailed information. We further use adversarial training to select the most reasonable results and to improve the sharpness of the images. Extensive experimental results on multiple datasets demonstrate the superiority of our method. Our code is available at https://github.com/thy960112/Pyramid-VAE-GAN. [ABSTRACT FROM AUTHOR]

Emerging memories have been developed as new physical infrastructures for hosting neural networks owing to their low-power analog computing characteristics. However, accurately and efficiently programming devices in an analog-valued array is still largely limited by the intrinsic physical non-idealities of the devices, thus hampering their applications in in-situ training of neural networks. Here, we demonstrate a passive electrochemical memory (ECRAM) array with many important characteristics necessary for accurate analog programming. Different image patterns can be open-loop and serially programmed into our ECRAM array, achieving high programming accuracies without any feedback adjustments. The excellent open-loop analog programmability has led us to in-situ train a bilayer neural network and reached software-like classification accuracy of 99.4% to detect poisonous mushrooms. The training capability is further studied in simulation for large-scale neural networks such as VGG-8. Our results present a new solution for implementing learning functions in an artificial intelligence hardware using emerging memories. Memory devices with open-loop analog programmability are highly desired in training tasks. Here, the authors developed an electrochemical memory array that can be accurately programmed without any feedback, offering unique capabilities for training. [ABSTRACT FROM AUTHOR]

Crime analysis is important for social security management. With the advance of crowd sensing techniques, abundant multisource crowd sensed data could be used for crime analysis. The occurrence of crimes usually has some patterns in terms of temporal and spatial aspects. Investigating the spatio-temporal correlation of crimes could provide more useful cues for crime analysis and help discover underlying crime patterns. In this paper, we conduct a spatio-temporal study to understand urban crimes leveraging multisource crowd sensed data, including crime data, meteorological data, POI distribution, and taxi trips. Specifically, we first present monthly temporal trends and spatial distribution of crimes. We then investigate the spatio-temporal correlation using meteorological data (e.g., weather conditions and air temperature) and POI distribution and taxi trips. It is found that taxi trips and air temperature have a strong correlation with the crime, and some POI categories have a valuable correlation with the crime, e.g., College & University. We also find that Overcast days would witness more crime than other weather conditions. [ABSTRACT FROM AUTHOR]

Copyright of Rare Metals is the property of Springer Nature and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Key message: An alanine to valine mutation of glutamyl-tRNA reductase's 510th amino acid improves 5-aminolevulinic acid synthesis in rice. 5-aminolevulinic acid (ALA) is the common precursor of all tetrapyrroles and plays an important role in plant growth regulation. ALA is synthesized from glutamate, catalyzed by glutamyl-tRNA synthetase (GluRS), glutamyl-tRNA reductase (GluTR), and glutamate-1-semialdehyde aminotransferase (GSAT). In Arabidopsis, ALA synthesis is the rate-limiting step in tetrapyrrole production via GluTR post-translational regulations. In rice, mutations of GluTR and GSAT homologs are known to confer chlorophyll deficiency phenotypes; however, the enzymatic activity of rice GluRS, GluTR, and GSAT and the post-translational regulation of rice GluTR have not been investigated experimentally. We have demonstrated that a suppressor mutation in rice partially reverts the xantha trait. In the present study, we first determine that the suppressor mutation results from a G → A nucleotide substitution of OsGluTR (and an A → V change of its 510th amino acid). Protein homology modeling and molecular docking show that the OsGluTRA510V mutation increases its substrate binding. We then demonstrate that the OsGluTRA510V mutation increases ALA synthesis in Escherichia coli without affecting its interaction with OsFLU. We further explore homologous genes encoding GluTR across 193 plant species and find that the amino acid (A) is 100% conserved at the position, suggesting its critical role in GluTR. Thus, we demonstrate that the gain-of-function OsGluTRA510V mutation underlies suppression of the xantha trait, experimentally proves the enzymatic activity of rice GluRS, GluTR, and GSAT in ALA synthesis, and uncovers conservation of the alanine corresponding to the 510th amino acid of OsGluTR across plant species. [ABSTRACT FROM AUTHOR]

Push chain moist-mix concrete spraying machine is widely used in shotcrete construction processes, but shotcrete surplus has always been the main problem affecting work efficiency. To improve the efficiency, the orifice plate is employed in the discharge cylinder, considering three different distributions (equal, left concentrated and both side distribution) of the holes and two hole diameters (6 mm and 8 mm). In different orifice plate, the discharge cylinder flow field is studied employing the FLUENT software by the computational fluid dynamic method. Besides, dimensionless evaluation index of the discharge cylinder flow field is defined based on the force of particles. By comparing dimensionless index and velocity vector, the best orifice plate (both side distribution and 8 mm) is presented. Compared without orifice plate, the experiment showed the shotcrete surplus ability is reduced by 26 %–87 %. This study provides guidance for the design and optimization of pneumatic conveying equipment. [ABSTRACT FROM AUTHOR]

Aftershocks can cause disasters again after mainshocks, which result in threat to life and economic loss. In order to avoid secondary disasters, it is necessary to predict whether aftershocks would happen in a given region. There have been studies using different features and methods to predict aftershocks spatial distribution. However, it is still unclear which are more important for aftershock prediction, input features or models; which type of features is more predictive for the prediction task. In this paper, we predict aftershock spatial distribution by combining different types of features and applying different machine learning methods. We introduce five different types of features and combine them together for prediction: the stress change sensors, their logarithmic values, the physical quantities, the magnitude of mainshocks, and the distance between the grid cell and the epicenter of mainshocks. We train different classifiers: Naive Bayes, Support Vector Machine,Gradient Boosting Decision Tree, k-Nearest Neighbors, Logistic regression, and DMAP (a Deep Neural Network model). Based on the 62,811 aftershocks of 171 distinct mainshocks in the past about 40 years in China, we conduct comprehensive experiments and analyses. We find that features play a more important role for this prediction task. Using the same feature type, different classifiers obtain quite similar performance. With different features, the same model performs differently. Taking the combined features as input, we achieve the state-of-the-art performance, with an AUC of 0.9530, about 4% higher than that of DeVries et al., showing the superiority of the combined features. Among all the features, adding the distance to the stress change sensors contributes the most to improve the prediction performance. In addition, it is found that the model prediction performance varies in terms of the time spans after mainshocks and the aftershock magnitudes. [ABSTRACT FROM AUTHOR]

Crime risk prediction is helpful for urban safety and citizens’ life quality. However, existing crime studies focused on coarse-grained prediction, and usually failed to capture the dynamics of urban crimes. The key challenge is data sparsity, since that 1) not all crimes have been recorded, and 2) crimes usually occur with low frequency. In this paper, we propose an effective framework to predict fine-grained and dynamic crime risks in each road using heterogeneous urban data. First, to address the issue of unreported crimes, we propose a cross-aggregation soft-impute (CASI) method to deal with possible unreported crimes. Then, we use a novel crime risk measurement to capture the crime dynamics from the perspective of influence propagation, taking into consideration of both time-varying and location-varying risk propagation. Based on the dynamically calculated crime risks, we design contextual features (i.e., POI distributions, taxi mobility, demographic features) from various urban data sources, and propose a zero-inflated negative binomial regression (ZINBR) model to predict future crime risks in roads. The experiments using the real-world data from New York City show that our framework can accurately predict road crime risks, and outperform other baseline methods. [ABSTRACT FROM AUTHOR]

Solving the optimization problem to approach a Nash Equilibrium point plays an important role in imperfect information games, e.g., StarCraft and poker. Neural Fictitious Self-Play (NFSP) is an effective algorithm that learns approximate Nash Equilibrium of imperfect-information games from purely self-play without prior domain knowledge. However, it needs to train a neural network in an off-policy manner to approximate the action values. For games with large search spaces, the training may suffer from unnecessary exploration and sometimes fails to converge. In this paper, we propose a new Neural Fictitious Self-Play algorithm that combines Monte Carlo tree search with NFSP, called MC-NFSP, to improve the performance in real-time zero-sum imperfect-information games. With experiments and empirical analysis, we demonstrate that the proposed MC-NFSP algorithm can approximate Nash Equilibrium in games with large-scale search depth while the NFSP can not. Furthermore, we develop an Asynchronous Neural Fictitious Self-Play framework (ANFSP). It uses asynchronous and parallel architecture to collect game experience and improve both the training efficiency and policy quality. The experiments with th e games with hidden state information (Texas Hold’em), and the FPS (firstperson shooter) games demonstrate effectiveness of our algorithms. [ABSTRACT FROM AUTHOR]

The area of East China Sea (ECS) inner shelf is an important sink of suspended particulates from Yangtze River (YR) and materials originated from YR basin. In this study, the parameters of lignin and alkane biomarkers in surface sediment samples from ECS inner shelf were determined to distinguish the sources and to trace the transport of terrigenous organic matters in the region. Our dataset showed that total alkanes with carbon numbers from 10 to 38 (T-alkanes) were significantly correlated to both TAR (terrigenous/aquatic ratio) and HMW/LMW (the ratio of high molecular weight to low molecular weight alkanes) (r =0.88, P<0.05 for both), indicating that the majority of T-alkanes was predominantly originated from terrestrial sources, and T-alkanes are important constituents of terrestrial organic matters in the study area. The area was probably affected by petroleum pollution to a certain degree, as indicated by the values of carbon preference index (CPI), odd-over-even carbon number predominance (OEP) and the ratio of pristane to phytane (Pr/Ph). The values of Pr/n-C17 and Ph/n-C18 suggested a strong reductive sedimentary condition in the region with no obvious biodegradation. The content of eight lignin phenols (Σ8) decreased from the coast to the open sea, indicative of riverine input and hydrodynamic transport of terrigenous organic matters. Lignin degradation parameters presented an increasing trend from the coastline toward the open sea. The lignin vegetation parameters and alkane index (AI) suggested the predominance of non-woody angiosperms in the YR basin. The obvious correlation between Λ8 (Σ8 normalized to 100 mg organic carbon), TAR and HMW/LMW reveals the significantly concurrent input of lignin and alkanes from terrestrial sources. [ABSTRACT FROM AUTHOR]

Alteration of various metabolites has been linked to type 2 diabetes (T2D) and insulin resistance. However, identifying significant associations between metabolites and tissue-specific phenotypes requires a multi-omics approach. In a cohort of 42 subjects with different levels of glucose tolerance (normal, prediabetes and T2D) matched for age and body mass index, we calculated associations between parameters of whole-body positron emission tomography (PET)/magnetic resonance imaging (MRI) during hyperinsulinemic euglycemic clamp and non-targeted metabolomics profiling for subcutaneous adipose tissue (SAT) and plasma. Plasma metabolomics profiling revealed that hepatic fat content was positively associated with tyrosine, and negatively associated with lysoPC(P-16:0). Visceral adipose tissue (VAT) and SAT insulin sensitivity (Ki), were positively associated with several lysophospholipids, while the opposite applied to branched-chain amino acids. The adipose tissue metabolomics revealed a positive association between non-esterified fatty acids and, VAT and liver Ki. Bile acids and carnitines in adipose tissue were inversely associated with VAT Ki. Furthermore, we detected several metabolites that were significantly higher in T2D than normal/prediabetes. In this study we present novel associations between several metabolites from SAT and plasma with the fat fraction, volume and insulin sensitivity of various tissues throughout the body, demonstrating the benefit of an integrative multi-omics approach. [ABSTRACT FROM AUTHOR]

A critical and challenging problem in reinforcement learning is how to learn the state-action value function from the experience replay buffer and simultaneously keep sample efficiency and faster convergence to a high quality solution. In prior works, transitions are uniformly sampled at random from the replay buffer or sampled based on their priority measured by temporal-difference (TD) error. However, these approaches do not fully take into consideration the intrinsic characteristics of transitions distribution in the state space and could result in redundant and unnecessary TD updates, slowing down the convergence of the learning procedure. To overcome this problem, we propose a novel state distribution-aware sampling method to balance the replay times for transitions with imbalanced distribution, which takes into account both the occurrence frequencies of transitions and the uncertainty of state-action values. Consequently, our approach could reduce the unnecessary TD updates and increase the TD updates for state-action value with more uncertainty, making the experience replay more effective and efficient. Extensive experiments are conducted on both classic control tasks and Atari 2600 games based on OpenAI gym platform and the experimental results demonstrate the effectiveness of our approach in comparison with the standard DQN approach. [ABSTRACT FROM AUTHOR]

Methylmercury (MeHg) in sediment is difficult to be determined due to its low concentration and binding compounds like sulfide and organic matter. Moreover, wet sediment samples have been suggested to behave differently from certified reference materials in MeHg analysis. Optimal pretreatment procedure for MeHg determination in sediments has not been ascertained and whether the procedure could apply to sediment samples with complex matrix merits further research. This work firstly compared recovery results of five pretreatment procedures for MeHg determination using ERM-CC580. Using the optimal pretreatment procedure, recovery results were analyzed in different sediment samples after manipulation of moisture content, organic matter, and acid volatile sulfide. The procedure using CuSO4/HNO3 as leaching solutions and mechanical shaking as extraction method was proved to produce the most satisfactory recovery results (100.67 ± 6.75%, mean ± standard deviation). And when moisture content varied from 20 to 80%, average recovery results in sediment samples ranged from 100 to 125%. Furthermore, before and after the manipulation of organic matter or acid volatile sulfide, spiking recovery results varied little and were all within acceptable limit (85~105%). Therefore, the procedure of CuSO4/HNO3-mechanical is proposed as a universal pretreatment method for MeHg determination in sediment samples with various characteristics. [ABSTRACT FROM AUTHOR]

Key message: The OsPLS2 locus was isolated and cloned by map-based cloning that encodes a Upf1-like helicase. Disruption of OsPLS2 accelerated light-dependent leaf senescence in the rice mutant of ospls2. Leaf senescence is a very complex physiological process controlled by both genetic and environmental factors, however its underlying molecular mechanisms remain elusive. In this study, we report a novel Oryza sativa premature leaf senescence mutant (ospls2). Through map-based cloning, a G-to-A substitution was determined at the 1st nucleotide of the 13th intron in the OsPLS2 gene that encodes a Upf1-like helicase. This mutation prompts aberrant splicing of OsPLS2 messenger and consequent disruption of its full-length protein translation, suggesting a negative role of OsPLS2 in regulating leaf senescence. Wild-type rice accordingly displayed a progressive drop of OsPSL2 protein levels with age-dependent leaf senescence. Shading and light filtration studies showed that the ospls2 phenotype, which was characteristic of photo-oxidative stress and reactive oxygen species (ROS) accumulation, was an effect of irritation by light. When continuously exposed to far-red light, exogenous H2O2 and/or abscisic acid (ABA), the ospls2 mutant sustained hypersensitive leaf senescence. In consistence, light and ROS signal pathways in ospls2 were activated by down-regulation of phytochrome genes, and up-regulation of PHYTOCHROME-INTERACTING FACTORS (PIFs) and WRKY genes, all promoting leaf senescence. Together, these data indicated that OsPLS2 played an essential role in leaf senescence and its disruption triggered light-dependent leaf senescence in rice. [ABSTRACT FROM AUTHOR]

Abstract: Key messageTheOsPLS3locus was isolated by map-based cloning that encodes a DUF266-containing protein. OsPLS3regulates the onset of leaf senescence in rice.Abstract: Glycosyltransferases (GTs) are one of the most important enzyme groups required for the modification of plant secondary metabolites and play a crucial role in plant growth and development, however the biological functions of most GTs remain elusive. We reported here the identification and characterization of a novel Oryza sativa premature leaf senescence mutant (ospls3). Through map-based cloning strategy, we determined that 22-bp deletion in the OsPLS3 gene encoding a domain of unknown function 266 (DUF266)-containing protein, a member of GT14-like, underlies the premature leaf senescence phenotype in the ospls3 mutant. The OsPLS3 mRNA levels progressively declined with the age-dependent leaf senescence in wild-type rice, implying a negative role of OsPLS3 in regulating leaf senescence. Physiological analysis, and histochemical staining and transmission electron microscopy assays indicated that the ospls3 mutant accumulated higher levels of ethylene and reactive oxygen species than its wild type. Furthermore, the ospls3 mutant showed hypersensitivity to exogenous 1-aminocyclopropane-1-carboxylic acid, H2O2 and high level of cytokinins. Our results indicated that the DUF266-containing gene OsPLS3 plays an important role in the onset of leaf senescence, in part through cytokinin and ethylene signaling in rice. [ABSTRACT FROM AUTHOR]

Key message: HT-induced ROS burst in developing anther is closely related to the lowered CAT activity as the result of the markedly suppressed OsCATB transcript, thereby causing severe fertility injury for rice plants exposed to HT at meiosis stage.Abstract: The reproductive stage of rice plants is highly sensitive to heat stress. In this paper, different rice cultivars were used to investigate the relationship of HT-induced floret sterility with reactive oxygen species (ROS) detoxification in rice anthers under well-controlled climatic conditions. Results showed that high temperature (HT) exposure significantly enhanced the ROS level and malondialdehyde (MDA) content in developing anther, and the increase in ROS amount in rice anther under HT exposure was closely associated with HT-induced decline in the activities of several antioxidant enzymes. For various antioxidant enzymes, SOD and CAT were more susceptible to the ROS burst in rice anther induced by HT exposure than APX and POD, in which SOD and CAT activity in developing anther decreased significantly by HT exposure, whereas APX activity was relatively stable among different temperature regimes. HT-induced decrease in CAT activity was attributable to the suppressed transcript of OsCATB. This occurrence was strongly responsible for HT-induced increase in ROS level and oxidative-damage in rice anther, thereby it finally caused significant reduction in pollen viability and floret fertility for the rice plants exposed to HT during meiosis. Exogenous application of 1000 µM salicylic acid (SA) may alleviate HT-induced reduction in pollen viability and floret fertility, concomitantly with the increased CAT activity and reduced ROS level in rice anther. [ABSTRACT FROM AUTHOR]

Bike sharing systems are booming globally as a green and flexible transportationmode, but the flexibility also brings difficulties in keeping the bike stations balanced with enough bikes and docks. Understanding the spatio-temporal bike trip patterns in a bike sharing system, such as the popular trip origins and destinations during rush hours, is important for researchers to design models for bike scheduling and station management. However, due to privacy and operational concerns, bike trip data are usually not publicly available in many cities. Instead, the station feeds about real-time bike and dock number in stations are usually public, which we refer to as bike sharing system open data. In this paper, we propose an approach to infer the spatio-temporal bike trip patterns from the public station feeds. Since the number of possible trips (i.e., origin-destination station pairs) is much larger than the number of stations, we define the trip inference as an ill-posed inverse problem. To solve this problem, we identify the sparsity and locality properties of bike trip patterns, and propose a sparse and weighted regularization model to impose both properties in the solution. We evaluate our method using real-world data fromWashington, D.C. and New York City. Results show that our method can effectively infer the spatio-temporal bike trip patterns and outperform the baselines in both cities. [ABSTRACT FROM AUTHOR]

Wet shotcrete can reduce dust and improve supporting strength, however, safe and efficient pipage is a key technical part of wet shotcrete process. The paper studied the pipe flow law of wet shotcrete based on lubrication layer by build the experimental pumping circuit of wet shotcrete that can carry out a number of full-scale pumping tests. The experimental results show there was a linear relationship between pressure loss and flow rate. Combined with the Buckingham rheological equation, the computing equations of the yield shear stress and plastic viscosity were deduced through linear regression. A simple analytical method allowing for a rough estimation of the pumping pressure was proposed and used when considering the lubrication layer of wet shotcrete in pipes. In addition, two kinds of particulate distributive models were established along the time axial to analyze the formation of lubrication layer which is related with particles migration. By computational fluid dynamics simulation, the lubrication layer thickness of different mix proportions was estimated. A new method for measuring the thickness of lubrication layer was proposed to verify it by binarization processing. Finally, according to the comparative analysis of experiments, simulation and computed value, it can be seen that the lubrication layer plays a key role in the process of wet shotcrete flow and with the increase of lubrication layer thickness pipe pressure declines gradually. [ABSTRACT FROM AUTHOR]

Genome-wide association studies (GWAS) have identified a large number of disease-associated SNPs, but in few cases the functional variant and the gene it controls have been identified. To systematically identify candidate regulatory variants, we sequenced ENCODE cell lines and used public ChIP-seq data to look for transcription factors binding preferentially to one allele. We found 9962 candidate regulatory SNPs, of which 16 % were rare and showed evidence of larger functional effect than common ones. Functionally rare variants may explain divergent GWAS results between populations and are candidates for a partial explanation of the missing heritability. The majority of allele-specific variants (96 %) were specific to a cell type. Furthermore, by examining GWAS loci we found >400 allele-specific candidate SNPs, 141 of which were highly relevant in our cell types. Functionally validated SNPs support identification of an SNP in SYNGR1 which may expose to the risk of rheumatoid arthritis and primary biliary cirrhosis, as well as an SNP in the last intron of COG6 exposing to the risk of psoriasis. We propose that by repeating the ChIP-seq experiments of 20 selected transcription factors in three to ten people, the most common polymorphisms can be interrogated for allele-specific binding. Our strategy may help to remove the current bottleneck in functional annotation of the genome. [ABSTRACT FROM AUTHOR]

A new environmental friendly method was developed for cyanobacterial bloom mitigation using local lake shore soil modified by protein with high isoelectric point (pI) and chitosan jointly. Results suggested that 5 mg L lysozyme- (pI ≈11) and 100 mg L bromelain (pI ≈9.5)-modified 10 mg L soil can both reduce the surface charge of Microcystis aeruginosa, the dominant species forming cyanobacterial blooms, from −26 to −10 mV and remove 73 and 60 % of algal cells in 30 min, respectively. The limited improvement of removal efficiency was due to the small flocs (<60 μm) formed by charge neutralization, which need more than 90 min to settle in static condition. However, when the small flocs were linked and bridged by the other modifier, chitosan with long polymer chain, large flocs of about 800 and 300 μm were formed and more than 80 % of algal cells were removed in 5 and 30 min by lysozyme-chitosan-modified soil and bromelain-chitosan-modified soil, respectively. The lower removal ability of bromelain-modified soil was due to the lower charge density leading to less powerful in destabilization of algal cells. Depending on the bicomponent modification mechanism including charge neutralization of proteins with high pI and netting and bridging function of chitosan with long polymer chain, it is possible to flocculate cyanobacterial blooms in natural waters effectively using locally available materials. [ABSTRACT FROM AUTHOR]

Copyright of SCIENCE CHINA Information Sciences is the property of Springer Nature and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Multifunctional sorbents, activated carbons (AC), were prepared by one-step microwave activation utilizing peanut shells and sunflower seed husks. The influence of the original particle size of raw materials on the yield and specific surface area of AC was studied, which reached 33.5 % and 1133.27 m/g, respectively. The repetitive and competitive uptakes of perfluorooctane sulfonate (PFOS) and chromium were applied to investigate the sorption properties of AC. The sorption mechanisms were demonstrated using sulfur K-edge X-ray absorption near edge structure analysis (XANES). In the repetitive experiment, AC made from peanut shells (AC) still retained 70 % removal efficiency of PFOS after the fourth sorption because sorbed PFOS might form a new organic phase that supplied effective sites for the hydrophobic partition of PFOS. However, the removal efficiency of Cr(VI) decreased dramatically from 60 to 11 % after the fourth uptake because electrostatic attraction was its only removal pathway. In the binary solutes system, AC possessed perfect sorption performance for both PFOS and Cr(VI), which were 885 and 192 mg/g, respectively. In the multivariate solutes system, the XANES spectra indicated that the thiol functional group existed in the resulting AC and a metal chelate was formed between thiol and Zn/Cu. Hence, the presence of Zn/Cu further promoted the removal of PFOS and Cr(VI) through the electrostatic attraction between the anions and positive metal chelate. [ABSTRACT FROM AUTHOR]

Automatic facial expression recognition has made considerable gains in the body of research available due to its vital role in human-computer interaction. So far, research on this problem or problems alike has proposed a wide verity of techniques and algorithms for both information representation and classification. Very recently, Farajzadeh et al. in Int J Pattern Recognit Artif Intell 25(8):1219-1241, () proposed a novel information representation approach that uses machine-learning techniques to derive a set of new informative and descriptive features from the original features. The new features, so called meta probability codes (MPC), have shown a good performance in a wide range of domains. In this paper, we aim to study the performance of the MPC features for the recognition of facial expression via proposing an MPC-based framework. In the proposed framework any feature extractor and classifier can be incorporated using the meta-feature generation mechanism. In the experimental studies, we use four state-of-the-art information representation techniques; local binary pattern, Gabor-wavelet, Zernike moment and facial fiducial point, as the original feature extractors; and four multiclass classifiers, support vector machine, k-nearest neighbor, radial basis function neural network, and sparse representation-based classifier. The results of the extensive experiments conducted on three facial expression datasets, Cohn-Kanade, JAFFE, and TFEID, show that the MPC features promote the performance of facial expression recognition inherently. [ABSTRACT FROM AUTHOR]

A spotted-leaf mutant Y181, produced by Co-radiation treatment of Zixuan1, was identified in rice. The result of TEM demonstrated the number of starch grains decreased significantly, the distribution and volumes of osmiophilic granules and lipid balls were larger in the leaf cells around the lesion mimics, the chloroplasts in mesophyll cells disappeared and just leaving a few rupture thylakoid membranes in the cytoplasm and some transmutative starch granules in Y181. Compared with the wild type, the content of chlorophyll was decreased significantly, while the HO content, and SOD, POD and CAT activity were obviously increased in mutant. DAB staining and HO treatment further revealed oxidative burst in spotted leaves. Expression of lesion-related gene showed RuBisCo-L, RuBisCo-S, PDI and TPX was down-regulated, while PBZ1 was up-regulated in the leaf cell at the lesion mimics. Disease reaction to Magnaporthe grisea indicated that the resistance of Y181 was reduced. Genetic analysis showed that the mutant was controlled by a recessive nuclear gene, which was delimited to a 390 kb region between CH12-936 and RM7195. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Zhejiang University: Science B is the property of Springer Nature and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)