Babesia Bovis Ligand-Receptor Interaction: AMA-1 Contains Small Regions Governing Bovine Erythrocyte Binding

Apical membrane antigen 1 is a microneme protein which plays an indispensable role during Apicomplexa parasite invasion. The detailed mechanism of AMA-1 molecular interaction with its receptor on bovine erythrocytes has not been completely defined in Babesia bovis. This study was focused on identifying the minimum B. bovis AMA-1-derived regions governing specific and high-affinity binding to its target cells. Different approaches were used for detecting ama-1 locus genetic variability and natural selection signatures. The binding properties of twelve highly conserved 20-residue-long peptides were evaluated using a sensitive and specific binding assay based on radio-iodination. B. bovis AMA-1 ectodomain structure was modelled and refined using molecular modelling software. NetMHCIIpan software was used for calculating B- and T-cell epitopes. The B. bovis ama-1 gene had regions under functional constraint, having the highest negative selective pressure intensity in the Domain I encoding region. Interestingly, B. bovis AMA-1-DI (¹⁰⁰YMQKFDIPRNHGSGIYVDLG¹¹⁹ and ¹²⁰GYESVGSKSYRMPVGKCPVV¹³⁹) and DII (³⁰²CPMHPVRDAIFGKWSGGSCV³²¹)-derived peptides had high specificity interaction with erythrocytes and bound to a chymotrypsin and neuraminidase-treatment sensitive receptor. DI-derived peptides appear to be exposed on the protein’s surface and contain predicted B- and T-cell epitopes. These findings provide data (for the first-time) concerning B. bovis AMA-1 functional subunits which are important for establishing receptor-ligand interactions which could be used in synthetic vaccine development.     

Convolutional Neural Network Auto Encoder Channel Estimation Algorithm in MIMO-OFDM System

Higher transmission rate is one of the technological features of prominently used wireless communication namely Multiple Input Multiple Output-Orthogonal Frequency Division Multiplexing (MIMO–OFDM). One among an effective solution for channel estimation in wireless communication system, specifically in different environments is Deep Learning (DL) method. This research greatly utilizes channel estimator on the basis of Convolutional Neural Network Auto Encoder (CNNAE) classifier for MIMO-OFDM systems. A CNNAE classifier is one among Deep Learning (DL) algorithm, in which video signal is fed as input by allotting significant learnable weights and biases in various aspects/objects for video signal and capable of differentiating from one another. Improved performances are achieved by using CNNAE based channel estimation, in which extension is done for channel selection as well as achieve enhanced performances numerically, when compared with conventional estimators in quite a lot of scenarios. Considering reduction in number of parameters involved and re-usability of weights, CNNAE based channel estimation is quite suitable and properly fits to the video signal. CNNAE classifier weights updation are done with minimized Signal to Noise Ratio (SNR), Bit Error Rate (BER) and Mean Square Error (MSE).     

Pattern design and optimization of yarn-dyed plaid fabric using isolation niche genetic algorithm and rough set theory

Under the circumstance of perceptual consumption, it is still challenging to grasp consumer's emotions and demands due to the large search space, diversified preferences, and easy fatigue of consumers. To reduce user fatigue and enlarge search space, a novel method was presented to design and optimize the pattern of yarn-dyed plaid fabric using the isolation niche genetic algorithm and rough set theory. Each pattern was encoded as a chromosome based on the real number code. The population was initialized and evolved using INGA to maintain the diversity. The rough set theory was adopted as the fitness function of isolation niche genetic algorithm to extract the consumer's demands. After multiple evolutions, a large set of practical patterns of the yarn-dyed plaid fabric are obtained. Experiments were carried out by 24 testers of different ages and genders. The results prove that the proposed method based on the isolation niche genetic algorithm and rough set theory is feasible and effective, supplying references to the designer.     

加氢反应器头盖密封质量控制探讨

加氢反应器是加氢装置的重要组成部分,反应器头盖因工况复杂,导致密封难度较大,密封不严,会导致整个装置不能正常运转。为此,就其头盖拆装及密封问题进行探讨,提出有效的质量控制措施。     

犹豫模糊环境下准则具有偏好关系的TODIM方法

针对智慧制造评估时专家的决策信息具有犹豫模糊不确定性问题，提出了一种关于准则具有犹豫模糊偏好关系的改进交互式多准则决策（TODIM）方法。首先提出了准则间的犹豫模糊偏好关系概念，并证明了其基本性质。在TODIM方法优势度的计算过程中，将准则权重犹豫模糊偏好关系替代原有的精确值权重，使信息的准确性最大化。将该方法用于智能制造的评估上，实例分析结果表明所提方法是可行和有效的。     

Some q-rung orthopair uncertain linguistic aggregation operators and their application to multiple attribute group decision making

q-Rung orthopair fuzzy sets (q-ROFSs), originally presented by Yager, are a powerful fuzzy information representation model, which generalize the classical intuitionistic fuzzy sets and Pythagorean fuzzy sets and provide more freedom and choice for decision makers (DMs) by allowing the sum of the $q\mathrm{t}\mathrm{h}$ power of the membership and the $q\mathrm{t}\mathrm{h}$ power of the nonmembership to be less than or equal to 1. In this paper, a new class of fuzzy sets called q-rung orthopair uncertain linguistic sets (q-ROULSs) based on the q-ROFSs and uncertain linguistic variables (ULVs) is proposed, and this can describe the qualitative assessment of DMs and provide them more freedom in reflecting their belief about allowable membership grades. On the basis of the proposed operational rules and comparison method of q-ROULSs, several q-rung orthopair uncertain linguistic aggregation operators are developed, including the q-rung orthopair uncertain linguistic weighted arithmetic average operator, the q-rung orthopair uncertain linguistic ordered weighted average operator, the q-rung orthopair uncertain linguistic hybrid weighted average operator, the q-rung orthopair uncertain linguistic weighted geometric average operator, the q-rung orthopair uncertain linguistic ordered weighted geometric operator, and the q-rung orthopair uncertain linguistic hybrid weighted geometric operator. Then, some desirable properties and special cases of these new operators are also investigated and studied, in particular, some existing intuitionistic fuzzy aggregation operators and Pythagorean fuzzy aggregation operators are proved to be special cases of these new operators. Furthermore, based on these proposed operators, we develop an approach to solve the multiple attribute group decision making problems, in which the evaluation information is expressed as q-rung orthopair ULVs. Finally, we provide several examples to illustrate the specific decision-making steps and explain the validity and feasibility of two methods by comparing with other methods.     

A new linearly constrained minimum variance beamformer for reconstructing EEG sparse sources

Brain source imaging based on EEG aims to reconstruct the neural activities producing the scalp potentials. This includes solving the forward and inverse problems. The aim of the inverse problem is to estimate the activity of the brain sources based on the measured data and leadfield matrix computed in the forward step. Spatial filtering, also known as beamforming, is an inverse method that reconstructs the time course of the source at a particular location by weighting and linearly combining the sensor data. In this paper, we considered a temporal assumption related to the time course of the source, namely sparsity, in the Linearly Constrained Minimum Variance (LCMV) beamformer. This assumption sounds reasonable since not all brain sources are active all the time such as epileptic spikes and also some experimental protocols such as electrical stimulations of a peripheral nerve can be sparse in time. Developing the sparse beamformer is done by incorporating L1-norm regularization of the beamformer output in the relevant cost function while obtaining the filter weights. We called this new beamformer SParse LCMV (SP-LCMV). We compared the performance of the SP-LCMV with that of LCMV for both superficial and deep sources with different amplitudes using synthetic EEG signals. Also, we compared them in localization and reconstruction of sources underlying electric median nerve stimulation. Results show that the proposed sparse beamformer can enhance reconstruction of sparse sources especially in the case of sources with high amplitude spikes.