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.     

Leucokinin and Associated Neuropeptides Regulate Multiple Aspects of Physiology and Behavior in Drosophila

Leucokinins (LKs) constitute a family of neuropeptides identified in numerous insects and many other invertebrates. LKs act on G-protein-coupled receptors that display only distant relations to other known receptors. In adult Drosophila, 26 neurons/neurosecretory cells of three main types express LK. The four brain interneurons are of two types, and these are implicated in several important functions in the fly’s behavior and physiology, including feeding, sleep–metabolism interactions, state-dependent memory formation, as well as modulation of gustatory sensitivity and nociception. The 22 neurosecretory cells (abdominal LK neurons, ABLKs) of the abdominal neuromeres co-express LK and a diuretic hormone (DH44), and together, these regulate water and ion homeostasis and associated stress as well as food intake. In Drosophila larvae, LK neurons modulate locomotion, escape responses and aspects of ecdysis behavior. A set of lateral neurosecretory cells, ALKs (anterior LK neurons), in the brain express LK in larvae, but inconsistently so in adults. These ALKs co-express three other neuropeptides and regulate water and ion homeostasis, feeding, and drinking, but the specific role of LK is not yet known. This review summarizes Drosophila data on embryonic lineages of LK neurons, functional roles of individual LK neuron types, interactions with other peptidergic systems, and orchestrating functions of LK.     

Novel energy-saving window coating based on F doped TiO2 nanocrystals with enhanced NIR shielding performance

To reduce the energy consumption of cooling in the hot summer days, searching for novel NIR shielding materials for buildings is of great value. In this report, monodispersed F doped TiO₂ nanocrystals with an average size of 8.6 nm were synthesized as novel solar shielding materials for energy-saving windows. All the products adopted an anatase TiO₂ structure. After doping of F ions, the morphology of TiO₂ was transformed from an irregular shape to a pseudospherical shape. The Raman shift and XPS depth analysis confirmed the successful doping of F⁻ ions into the lattice oxygen sites in the TiO₂ structure. The introduction of F⁻ ions generated free electrons and bulk Ti³⁺ in TiO₂ crystals, which activated a localized surface plasmon resonance (LSPR) absorption in the NIR region. Correspondingly, the NIR shielding performance of the TiO₂ films improved with increasing F doping amounts. The NIR shielding value of the films increased from 1.3% to 43.2% when the molar ratio of F to Ti increased from 0 to 0.3. The reason can be attributed to the enhanced NIR absorption induced by the increased electron concentration after doping of fluorine ions. The F–TiO₂ films showed superior visible transmittance (90.1–96.7%). Moreover, the F–TiO₂ films lowered the indoor temperature of the heat box by 5.3 °C in the thermal tests. Overall, the prepared F–TiO₂ nanocrystals show a great potential to be used for energy-saving windows.     

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).     

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.     

改进的区间犹豫算子应用于物流企业选择决策

针对现有区间犹豫模糊Hamacher算子存在的缺陷，构建了一种基于改进的区间犹豫模糊Hamacher加权算子的群决策方法。在分析现有区间犹豫模糊Hamacher算子不能满足幂等性的基础上，定义新的区间犹豫模糊Hamacher四则运算；提出两种改进的区间犹豫模糊Hamacher加权算子，包括改进的区间犹豫模糊Hamacher有序加权平均（I-IVHFHOWA）算子和改进的区间犹豫模糊Hamacher有序加权几何（I-IVHFHOWG）算子，并详细探究它们的常用算子形式以及算子之间的内在联系；建立基于I-IVHFHOWA算子和I-IVHFHOWG算子的物流企业选择决策模型，并通过实例说明模型的有效性。     

云南野生黄毛草莓的营养价值评价和香气成分分析

分析黄毛草莓的果实品质和香气成分并对比其他栽培品种的差异，为其品质特性评价和香气利用奠定基础。通过直接滴定法、酸碱滴定法、福林酚法、直接法、2,6-二氯靛酚滴定法和质构仪测定法对4种草莓（黄毛草莓、密宝、哈尼、R7）的还原糖、总酸、总酚、花色苷、维生素C和硬度共6项指标进行测定，综合比较4种草莓的品质；运用顶空固相微萃取和气相色谱—质谱联用技术分析草莓果实的香气成分并做聚类分析。结果显示，在营养价值方面，黄毛草莓综合评分值仅次于哈尼排在第2位，高于密宝和R7。在果实香气方面，黄毛草莓的香气鉴定物质种类最多为83种，密宝有66种，哈尼有64种，R7有57种。在4种草莓的挥发物中，酯类是主要的挥发物（30.23%~52.70%），其中黄毛草莓的酯类相对含量最高（52.70%）。构成黄毛草莓特征香气的主要成分是香味酯：异戊酸乙酯、己酸乙酯、辛酸乙酯、乙酸辛酯、甲酸辛酯、癸酸乙酯、乙酸癸酯、香味醇反式-2-己烯-1-醇。黄毛草莓香气主成分聚类分析的结果显示，黄毛草莓和其他3个栽培品种分为两类，在香气主成分上与其他3种栽培品种存在显著差异。黄毛草莓中富含多种香精香料与洗护产品等行业所需的特殊香气成分，具有极大应用潜力。     

双相区加速冷却处理对X80管线钢组织与性能的影响

采用双相区加速冷却法(加速冷却始冷温度为700 ℃)对X80管线钢进行热处理,获得了贝氏体和铁素体(B+F)双相组织。然后通过组织表征、力学性能测试以及在3.5wt%NaCl溶液中的耐蚀性进行研究。结果表明:热处理后获得的管线钢组织由板条状贝氏体、多边形铁素体及少量马氏体/奥氏体岛组成。与热处理前相比,(B+F)双相管线钢屈强比较低,为0.65,初始加工硬化指数为0.31,均匀伸长率达8.3%,塑性显著提升;双相组织中含有52.4%的铁素体,因而耐腐蚀性明显提高。通过双相区加速冷却法获得的(B+F)两相组织在塑变过程中发生协调变形,可以适应大变形的需求,同时耐蚀性优异,为大变形管线钢实际生产提供一定的借鉴。     

基于DoG检测图像特征点的快速二进制描述子

针对SIFT描述子实时性差和传统二进制描述子对尺度、旋转和视角变化鲁棒性差的问题,本文通过优化采样模式和添加灰度差分不变量比较测试进行改进,提出了一种鲁棒性更高的二进制描述子。首先,设计了一种尺度关联、编号标记的采样模式;然后,旋转采样模式中各采样点到特定位置,确保描述子尺度、旋转不变性;接着,分析了采样点点对模式对描述子的影响,选择使用机器学习训练后的128对采样点对;最后,选择灰度值比较测试及梯度绝对值和比较测试构建二进制描述子。实验中采用DoG检测图像关键点,结果表明:本文提出的描述子在描述子构建和描述子匹配上比SIFT描述子分别快84%和67%;在有视角变化的图像匹配上,准确率比传统的二进制描述子高3%~5%,召回率平均要高30%以上。本文提出的特征点描述方法适用于时间要求高的图像匹配领域。