A dual receptor crosstalk model of G-protein-coupled signal transduction

Macrophage cells that are stimulated by two different ligands that bind to G-protein-coupled receptors (GPCRs) usually respond as if the stimulus effects are additive, but for a minority of ligand combinations the response is synergistic. The G-protein-coupled receptor system integrates signaling cues from the environment to actuate cell morphology, gene expression, ion homeostasis, and other physiological states. We analyze the effects of the two signaling molecules complement factors 5a (C5a) and uridine diphosphate (UDP) on the intracellular second messenger calcium to elucidate the principles that govern the processing of multiple signals by GPCRs. We have developed a formal hypothesis, in the form of a kinetic model, for the mechanism of action of this GPCR signal transduction system using data obtained from RAW264.7 macrophage cells. Bayesian statistical methods are employed to represent uncertainty in both data and model parameters and formally tie the model to experimental data. When the model is also used as a tool in the design of experiments, it predicts a synergistic region in the calcium peak height dose response that results when cells are simultaneously stimulated by C5a and UDP. An analysis of the model reveals a potential mechanism for crosstalk between the Galphai-coupled C5a receptor and the Galphaq-coupled UDP receptor signaling systems that results in synergistic calcium release.     

基于双模型融合的大熊猫头部图像分割

在大熊猫(Ailuropoda melanoleuca)的迁地保护和种群饲养管理中,及时、快速地进行个体识别和行为监测,对其健康管理具有至关重要的作用。圈养大熊猫健康状况通常由专门的饲养人员肉眼观测,人力成本高、效率低并且缺乏时效性。基于图像的动物个体识别与行为分析技术效率高、时间成本低,已经成为新的监测发展趋势。已有研究提出,通过大熊猫面部图像的检测和分析,可实现个体识别和行为分类。但该方法依然存在检测精度不足导致识别准确率难以提升的问题。本文提出一种基于YOLOv3和Mask R-CNN的双模型融合方法,实现了大熊猫头部图像分割和精准检测。包含3个部分：YOLOv3完成头部检测,Mask R-CNN完成大熊猫轮廓分割,然后将两个模型的输出进行交并比融合。结果显示,头部检测准确率为82.6%,大熊猫轮廓分割准确率为95.2%,总体头部轮廓分割准确率为87.1%。该方法对大熊猫头部图像的识别率和分割准确率高,为大熊猫的个体识别、性别分类提供了帮助,为行为分析提供了技术参考。     

A digital signal processing system for EEG frequency analysis

This paper describes the digital signal processing work of a research project for studying children's cognitive processes by analyzing EEG signals during school-related tasks. The EEG being analyzed involves two homologous channels (left and right parietal area), and is recorded on magnetic tapes. The objective of the analysis is to determine if, by examining the alpha band of the ongoing EEG, different school tasks and correct vs incorrect responses can be detected. Analysis of alpha-band calls for the determination of signal power in the 7-12 Hz frequency band (adjusted for the age of the subjects) for each channel as well as correlation between the channels. A digital signal processing scheme implemented on an Apple II microcomputer was developed for such an analysis. The results obtained are discussed.     

A three-barrier model for the hemocyanin channel

Keyhole limpet hemocyanin forms ion-conducting channels in planar lipid bilayer membranes. Ionic current through the open hemocyanin channel presents the following characteristics: (a) it is carried mainly by cations; (b) it is a nonlinear function of membrane potential; (c) channel conductance is a saturating function of ion activity; (d) it shows ionic competition. A model for the open hemocyanin channel is developed from absolute reaction rate theory. The model calls for three energy barriers in the channel. Two energy barriers represent the entrance and exit of the ion into and out of the channel. The third barrier separates two energy minima that represent two binding sites. Furthermore, only one ion is allowed inside the channel at a given time. This model is able to recreate all the hemocyanin characteristics found experimentally in negatively charged and neutral membranes.     

A dual biosensor for 2-alkyl-4-quinolone quorum-sensing signal molecules

Pseudomonas, Burkholderia and Alteromonas species produce diverse 2-alkyl-4-quinolones (AHQs) which inhibit the growth of bacteria, algae and phytoplankton, chelate iron, modulate mammalian host immune defences and act as quorum-sensing (QS) signal molecules. To facilitate the detection, identification and quantification of the major Pseudomonas aeruginosa AHQs 2-heptyl-3-hydroxy-4-quinolone (PQS) and 2-heptyl-4-quinolone (HHQ) we developed two different AHQ biosensors. These were constructed by introducing either a lecA::luxCDABE or a pqsA::luxCDABE reporter gene fusion into a P. aeruginosa pqsA mutant which cannot synthesize AHQs. While both biosensors responded similarly to PQS (EC(50) 18 +/- 4 microM), the pqsA::luxCDABE biosensor was most sensitively activated by HHQ (EC(50) 0.44 +/- 0.1 microM). This biosensor was also activated albeit less sensitively by (i) PQS analogues with alkyl chains varying from C1 to C11, (ii) HHQ analogues with C9 and C11 alkyl chains and (iii) 2-heptyl-4-hydroxyquinoline-N-oxide (HHQNO). The AHQ biosensor also responded differentially to the AHQs present in cell free culture supernatants prepared from PAO1 and isogenic strains carrying mutations in genes (pqsA, pqsH, lasR, lasI, rhlR, rhlI) known to influence AHQ production. The AHQ profiles of P. aeruginosa strains was also evaluated by overlaying thin layer chromatogram (TLC) plates with the pqsA::luxCDABE biosensor. In PAO1, three major bioluminescent spots were observed which correspond to PQS, HHQ and a mixture of 2 nonyl-4-quinolone and HHQNO. We also noted that on TLC plates the biosensor not only produced bioluminescence in response to AHQs but also the green pigment, pyocyanin which offers an alternative visual indicator for AHQ production.     

A three-phase model of arthropod segmentation

Molecular and morphological evidence (expression patterns of pair-rule genes and segmental position of the genital openings and other segmental markers) suggest that the segmental units of the arthropod body are specified, in early ontogeny, by three spatially and/or temporally distinct mechanisms and do not appear in a strict antero-posterior sequence. A first anterior set of indivisible segments (naupliar segments, possibly three in all arthropods) is followed by a set of more caudal (post-naupliar) primary units (eosegments, possibly ten in all arthropods) which then undergo a process of secondary segmentation, thus giving rise to a higher number of definitive segments (merosegments). The number of merosegments deriving from each eosegment is characteristic of the different arthropod clades and is mostly stable at the level of the traditional arthropodan classes or subclasses. All their segmentation patterns, however, including those found in the segmental organisation of highly segmented forms (such as centipedes and millipedes, notostracan, lipostracan and anostracan crustaceans, and trilobites) are reducible to the basic groundplan with three naupliar and ten postnaupliar segments. These basic units of arthropod segmentation may also have an equivalent in other Ecdysozoa, despite the lack of any segmentation (nematodes) or, at least, of an overt segmentation (kinorhynchs).     

Continuous EEG signal analysis for asynchronous BCI application

In this study, we propose a two-stage recognition system for continuous analysis of electroencephalogram (EEG) signals. An independent component analysis (ICA) and correlation coefficient are used to automatically eliminate the electrooculography (EOG) artifacts. Based on the continuous wavelet transform (CWT) and Student's two-sample t-statistics, active segment selection then detects the location of active segment in the time-frequency domain. Next, multiresolution fractal feature vectors (MFFVs) are extracted with the proposed modified fractal dimension from wavelet data. Finally, the support vector machine (SVM) is adopted for the robust classification of MFFVs. The EEG signals are continuously analyzed in 1-s segments, and every 0.5 second moves forward to simulate asynchronous BCI works in the two-stage recognition architecture. The segment is first recognized as lifted or not in the first stage, and then is classified as left or right finger lifting at stage two if the segment is recognized as lifting in the first stage. Several statistical analyses are used to evaluate the performance of the proposed system. The results indicate that it is a promising system in the applications of asynchronous BCI work.     

A segmentation/clustering model for the analysis of array CGH data

Microarray-CGH (comparative genomic hybridization) experiments are used to detect and map chromosomal imbalances. A CGH profile can be viewed as a succession of segments that represent homogeneous regions in the genome whose representative sequences share the same relative copy number on average. Segmentation methods constitute a natural framework for the analysis, but they do not provide a biological status for the detected segments. We propose a new model for this segmentation/clustering problem, combining a segmentation model with a mixture model. We present a new hybrid algorithm called dynamic programming-expectation maximization (DP-EM) to estimate the parameters of the model by maximum likelihood. This algorithm combines DP and the EM algorithm. We also propose a model selection heuristic to select the number of clusters and the number of segments. An example of our procedure is presented, based on publicly available data sets. We compare our method to segmentation methods and to hidden Markov models, and we show that the new segmentation/clustering model is a promising alternative that can be applied in the more general context of signal processing.     

A stochastic limit cycle oscillator model of the EEG

We present an empirical model of the electroencephalogram (EEG) signal based on the construction of a stochastic limit cycle oscillator using Itô calculus. This formulation, where the noise influences actually interact with the dynamics, is substantially different from the usual definition of measurement noise. Analysis of model data is compared with actual EEG data using both traditional methods and modern techniques from nonlinear time series analysis. The model demonstrates visually displayed patterns and statistics that are similar to actual EEG data. In addition, the nonlinear mechanisms underlying the dynamics of the model do not manifest themselves in nonlinear time series analysis, paralleling the situation with real, non-pathological EEG data. This modeling exercise suggests that the EEG is optimally described by stochastic limit cycle behavior.     

Kinetic analysis of the dual phospholipid model for phospholipase A2 action

A kinetic scheme is proposed for the action of cobra venom phospholipase A2 on mixed micelles of phospholipid and the nonionic detergent Triton X-100, based on the "dual phospholipid model." (formula; see text) The water-soluble enzyme binds initially to a phospholipid molecule in the micelle interface. This is followed by binding to additional phospholipid in the interface and then catalytic hydrolysis. A kinetic equation was derived for this process and tested under three experimental conditions: (i) the mole fraction of substrate held constant and the bulk substrate concentration varied; (ii) the bulk substrate concentration held constant and the Triton X-100 concentration varied (surface concentration of substrate varied); and (iii) the Triton X-100 concentration held constant and the bulk substrate concentration varied. The substrates used were chiral dithiol ester analogs of phosphatidylcholine (thio-PC) and phosphatidylethanolamine (thio-PE), and the reactions were followed by reaction of the liberated thiol with a colorimetric thiol reagent. The initial binding (Ks = k1/k-1) was apparently similar for thio-PC and thio-PE (between 0.1 and 0.2 mM) as were the apparent Michaelis constants (Km = (k-2 + k3)/k2) (about 0.1 mol fraction). The Vmax values for thio-PC and thio-PE were 440 and 89 mumol min-1 mg-1, respectively. The preference of cobra venom phospholipase A2 for PC over PE in Triton X-100 mixed micelles appears to be an effect on k3 (catalytic rate) rather than an effect on the apparent binding of phospholipid in either step of the reaction.     

A microscopic electrostatic model for the amphotericin B channel.

A microscopic model of an amphotericin B channel is proposed. The structure of the pores is generated using the atomic coordinates of the molecule in the structure determined experimentally by X-ray diffraction. The net charges of the atoms are determined by Mulliken analysis. With these charges the electrostatic energy profiles are calculated for a monovalent ion passing through the channels formed by different number of antibiotic molecules having different radii. The water inside the channel was considered through a continuum medium using the dielectric constant of the bulk, and the membrane contribution was included using the virtual images of the pore in a dielectric slab of epsilon = 3. The model satisfactorily explains the permeability and selectivity characteristics as well as other observations yet unexplained. The electrostatic profiles obtained reinforce the hypothesis of the existence of channels formed by a variable number of units.     

Complex networks and deep learning for EEG signal analysis

Electroencephalogram (EEG) signals acquired from brain can provide an effective representation of the human’s physiological and pathological states. Up to now, much work has been conducted to study and analyze the EEG signals, aiming at spying the current states or the evolution characteristics of the complex brain system. Considering the complex interactions between different structural and functional brain regions, brain network has received a lot of attention and has made great progress in brain mechanism research. In addition, characterized by autonomous, multi-layer and diversified feature extraction, deep learning has provided an effective and feasible solution for solving complex classification problems in many fields, including brain state research. Both of them show strong ability in EEG signal analysis, but the combination of these two theories to solve the difficult classification problems based on EEG signals is still in its infancy. We here review the application of these two theories in EEG signal research, mainly involving brain–computer interface, neurological disorders and cognitive analysis. Furthermore, we also develop a framework combining recurrence plots and convolutional neural network to achieve fatigue driving recognition. The results demonstrate that complex networks and deep learning can effectively implement functional complementarity for better feature extraction and classification, especially in EEG signal analysis.     

Gabor based analysis prior formulation for EEG signal reconstruction

This paper deals with the problem of tele-monitoring EEG signals. In EEG tele-monitoring system, the integral step is to compress the signals in computationally efficient manner so that they can be transmitted over a limited bandwidth. In such a situation a Compressed Sensing (CS) framework for compressing and recovering the signals is the most viable approach. Previously the well known synthesis prior formulation is used for reconstruction. For the first time in this work, we show that the lesser known analysis prior formulation is a more appropriate way to frame the reconstruction problem. We show that our method yields better results than the previous synthesis prior formulation.     

A model for channel noise,including the effect of diffusion

A model (based on a proposal by Schick (1974), is developed for channels composed of four species; the channels allow conduction when all species are in the correct configuration. Allowance is also made for diffusion resulting from depletion of ions in the neighbourhood of the channel. The result is a series of pulses in which the current falls as ^–1/2 upon closing the channel. The resulting power spectrum is calculated according to the method given by Schick and earlier workers, and is found to be, at least qualitatively, in agreement with many of the features of noise measured for certain types of nerve membranes. The calculations are not linearized, but are carried through numerically.     

不同思维作业电信号的高准确度模式分类

本文采用独立分量(ICA)分析对不同思维作业的脑电(EEG)信号进行预处理,再用自回归(AR)参数模型提取EEG信号特征,最后利用BP网络完成对特征样本集的训练和分类。实验结果表明,所采用的方法提高了脑电思维模式作业的准确度,对两种到五种不同思维作业未经训练的数据的平均分类准确度达到79％以上,超过现有文献报道的结果。     

A possible model for the inner wall of the acetylcholine receptor channel

A structural model of the inner wall of the acetylcholine receptor (AChR) channel is developed using assumptions derived from the results of the recent labelling experiments of the MII helices by noncompetitive blockers. The assumption of steric blocking of the channel by chlorpromazine (CPZ) in the neighbourhood of the labelled serines imposes the MII helices to be in contact at this level and allows the calculation of their minimal interaxial distance. The assumption that CPZ diffuses to this position through the upper crowded part of the channel imposes that the helices are more distant in this region and permits the determination of a tilt of about 7 degrees with respect to the central axis. Electrostatic potentials are used to demonstrate the effect of the charged residues at the exit of the pore. A discussion is given on the possible aptitude of MI to satisfy the contacts necessary with the MII/s at the different heights of the model.     

A gating motif in the translocation channel sets the hydrophobicity threshold for signal sequence function

A critical event in protein translocation across the endoplasmic reticulum is the structural transition between the closed and open conformations of Sec61, the eukaryotic translocation channel. Channel opening allows signal sequence insertion into a gap between the N- and C-terminal halves of Sec61. We have identified a gating motif that regulates the transition between the closed and open channel conformations. Polar amino acid substitutions in the gating motif cause a gain-of-function phenotype that permits translocation of precursors with marginally hydrophobic signal sequences. In contrast, hydrophobic substitutions at certain residues in the gating motif cause a protein translocation defect. We conclude that the gating motif establishes the hydrophobicity threshold for functional insertion of a signal sequence into the Sec61 complex, thereby allowing the wild-type translocation channel to discriminate between authentic signal sequences and the less hydrophobic amino acid segments in cytosolic proteins. Bioinformatic analysis indicates that the gating motif is conserved between eubacterial and archaebacterial SecY and eukaryotic Sec61.     

MCFSA-Net: A multi-scale channel fusion and spatial activation network for retinal vessel segmentation

As the only vascular tissue that can be directly viewed in vivo, retinal vessels are medically important in assisting the diagnosis of ocular and cardiovascular diseases. They generally appear as different morphologies and uneven thickness in fundus images. Therefore, the single-scale segmentation method may fail to capture abundant morphological features, suffering from the deterioration in vessel segmentation, especially for tiny vessels. To alleviate this issue, we propose a multi-scale channel fusion and spatial activation network (MCFSA-Net) for retinal vessel segmentation with emphasis on tiny ones. Specifically, the Hybrid Convolution-DropBlock (HC-Drop) is first used to extract deep features of vessels and construct multi-scale feature maps by progressive down-sampling. Then, the Channel Cooperative Attention Fusion (CCAF) module is designed to handle different morphological vessels in a multi-scale manner. Finally, the Global Spatial Activation (GSA) module is introduced to aggregate global feature information for improving the attention on tiny vessels in the spatial domain and realizing effective segmentation for them. Experiments are carried out on three datasets including DRIVE, CHASE_DB1, and STARE. Our retinal vessel segmentation method achieves Accuracy of 96.95%, 97.57%, and 97.83%, and F1 score of 82.67%, 81.82%, and 82.95% in the above datasets, respectively. Qualitative and quantitative analysis show that the proposed method outperforms current advanced vessel segmentation methods, especially for tiny vessels.     

A statistically driven approach for image segmentation and signal extraction in cDNA microarrays.

The increasing use of cDNA microarrays necessitates the development of methods for extracting quality data. Here, we set forth hurdles to overcome in image analysis of microarrays. We emphasize the importance of objective data extraction methods resulting in reliable signal estimates. Based on statistical principles, we describe a method for automated grid alignment, spot detection, background estimation, flagging, and signal extraction. A software application that we call SignalViewer has been implemented for this method. We identify areas where we improved upon current methods used for array image analysis at each step in the process. Finally, we give examples to illustrate the performance of our algorithms on raw data.