Meltrin beta expressed in cardiac neural crest cells is required for ventricular septum formation of the heart

The heart is divided into four chambers by membranous septa and valves. Although evidence suggests that formation of the membranous septa requires migration of neural crest cells into the developing heart, the functional significance of these neural crest cells in the development of the endocardial cushion, an embryonic tissue that gives rise to the membranous appendages, is largely unknown. Mice defective in the protease region of Meltrin beta/ADAM19 show ventricular septal defects and defects in valve formation. In this study, by expressing Meltrin beta in either endothelial or neural crest cell lineages, we showed that Meltrin beta expressed in neural crest cells but not in endothelial cells was required for formation of the ventricular septum and valves. Although Meltrin beta-deficient neural crest cells migrated into the heart normally, they could not properly fuse the right and left ridges of the cushion tissues in the proximal outflow tract (OT), and this led to defects in the assembly of the OT and AV cushions forming the ventricular septum. These results genetically demonstrated a critical role of cardiac neural crest cells expressing Meltrin beta in triggering fusion of the proximal OT cushions and in formation of the ventricular septum.     

Neural tube defects, micronutrient deficiencies, and Helicobacter pylori: a new hypothesis

BACKGROUND: Previous findings for the Texas Neural Tube Defects Project suggested that while maternal access to nutrients is adequate, bioavailability of nutrients to the fetus is compromised in NTD-affected pregnancies. Helicobacter pylori could cause nutrient loss to the fetus. Folate, B12, and ferritin are depleted in H. pylori infection; these same deficiencies are related to NTD risk. METHODS: Using H. pylori IgG ELISA Test System, we tested for H. pylori serum antibodies in participants in the population-based case-control study component of the Texas Neural Tube Defect Project conducted along the Texas-Mexico border. Case-women had pregnancies affected by NTD (anencephalus, spina bifida, encephalocele) and resided and delivered in one of the 14 Texas-Mexico border counties from 1995 through 2000. Control-women were study area residents delivering normal live births during the same period. RESULTS: Of 225 case- and 378 control-women, 103 cases and 156 controls provided questionnaire and H. pylori antibody data. H. pylori seropositivity was modestly associated with NTD-affected pregnancies (OR 1.4; 95% CI: 0.8-2.4). ORs of 2.0 or greater were seen in women younger than age 25 and with less than 7 years education. CONCLUSIONS: Our findings intimate that H. pylori could play a role in NTD causation in certain populations. While results did not provide compelling support for this proposal, subgroup findings prompt us to advocate an evaluation of this hypothesis in developing nations among populations with higher prevalence of H. pylori, marginal nutrient intake, and young childbearing age.     

A robust deep attention dense convolutional neural network for plant leaf disease identification and classification from smart phone captured real world images

Plant diseases cause significant food loss and hence economic loss around the globe. Therefore, automatic plant disease identification is a primary task to take proper medications for controlling the spread of the diseases. Large variety of plants species and their dissimilar phytopathological symptoms call for the implementation of supervised machine learning techniques for efficient and reliable disease identification and classification. With the development of deep learning strategies, convolutional neural network (CNN) has paved its way for classification of multiple plant diseases by extracting rich features. However, several characteristics of the input images especially captured in real world environment, viz. complex or indistinguishable background, presence of multiple leaves with the diseased leaf, small lesion area, solemnly affect the robustness and accuracy of the CNN modules. Available strategies usually applied standard CNN architectures on the images captured in the laboratory environment and very few have considered practical in-field leaf images for their studies. However, those studies are limited with very limited number of plant species. Therefore, there is need of a robust CNN module which can successfully recognize and classify the dissimilar leaf health conditions of non-identical plants from the in-field RGB images. To achieve the above goal, an attention dense learning (ADL) mechanism is proposed in this article by merging mixed sigmoid attention learning with the basic dense learning process of deep CNN. The basic dense learning process derives new features at higher layer considering all lower layer features and that provides fast and efficient training process. Further, the attention learning process amplifies the learning ability of the dense block by discriminating the meaningful lesion portions of the images from the background areas. Other than adding an extra layer for attention learning, in the proposed ADL block the output features from higher layer dense learning are used as an attention mask to the lower layers. For an effective and fast classification process, five ADL blocks are stacked to build a new CNN architecture named DADCNN-5 for obtaining classification robustness and higher testing accuracy. Initially, the proposed DADCNN-5 module is applied on publicly available extended PlantVillage dataset to classify 38 different health conditions of 14 plant species from 54,305 images. Classification accuracy of 99.93% proves that the proposed CNN module can be used for successful leaf disease identification. Further, the efficacy of the DADCNN-5 model is checked after performing stringent experiments on a new real world plant leaf database, created by the authors. The new leaf database contains 10,851 real-world RGB leaf images of 17 plant species for classifying their 44 distinguished health conditions. Experimental outcomes reveal that the proposed DADCNN-5 outperforms the existing machine learning and standard CNN architectures, and achieved 97.33% accuracy. The obtained sensitivity, specificity and false positive rate values are 96.57%, 99.94% and 0.063% respectively. The module takes approximately 3235 min for training process and achieves 99.86% of training accuracy. Visualization of Class activation mapping (CAM) depicts that DADCNN-5 is able to learn distinguishable features from semantically important regions (i.e. lesion regions) on the leaves. Further, the robustness of the DADCNN-5 is established after experimenting with augmented and noise contaminated images of the practical database.     

海州湾春季短蛸的栖息分布特征及其与环境因子的关系

近年来,我国近海多种重要渔业资源处于不同程度的衰退状态,而短蛸具有生命周期短、生长迅速的特点,在我国近海经济渔获产量中占重要地位。然而,有关短蛸的栖息分布特征及其与环境因子的关系尚缺乏研究,不利于更好地保护和利用其资源。本研究根据2011年和2013—2017年春季海州湾的渔业资源和环境因子调查数据,采用随机森林模型、人工神经网络模型和广义提升回归模型3种机器学习方法分析了短蛸的栖息分布特征及其与环境因子的关系。结果表明: 随机森林模型的拟合效果和预测能力在3种模型中优势较大,选择该模型进行分析表明,底层水温、水深和底层盐度对短蛸的栖息分布有较大影响。短蛸的相对资源密度随底层水温、水深和底层盐度的增加均呈先上升后下降趋势。根据FVCOM模型模拟的环境数据,应用随机森林模型预测了短蛸在海州湾海域的栖息分布,发现短蛸主要分布在34.5°—35.8° N、119.7°—121° E之间的海域。     

The PDZ-GEF Gef26 regulates synapse development and function via FasII and Rap1 at the Drosophila neuromuscular junction

Guanine nucleotide exchange factors (GEFs) are essential for small G proteins to activate their downstream signaling pathways, which are involved in morphogenesis, cell adhesion, and migration. Mutants of Gef26, a PDZ-GEF (PDZ domain-containing guanine nucleotide exchange factor) in Drosophila, exhibit strong defects in wings, eyes, and the reproductive and nervous systems. However, the precise roles of Gef26 in development remain unclear. In the present study, we analyzed the role of Gef26 in synaptic development and function. We identified significant decreases in bouton number and branch length at larval neuromuscular junctions (NMJs) in Gef26 mutants, and these defects were fully rescued by restoring Gef26 expression, indicating that Gef26 plays an important role in NMJ morphogenesis. In addition to the observed defects in NMJ morphology, electrophysiological analyses revealed functional defects at NMJs, and locomotor deficiency appeared in Gef26 mutant larvae. Furthermore, Gef26 regulated NMJ morphogenesis by regulating the level of synaptic Fasciclin II (FasII), a well-studied cell adhesion molecule that functions in NMJ development and remodeling. Finally, our data demonstrate that Gef26-specific small G protein Rap1 worked downstream of Gef26 to regulate the level of FasII at NMJs, possibly through a βPS integrin-mediated signaling pathway. Taken together, our findings define a novel role of Gef26 in regulating NMJ development and function.     

Magnetoelectric nanocomposite scaffold for high yield differentiation of mesenchymal stem cells to neural-like cells

While the differentiation factors have been widely used to differentiate mesenchymal stem cells (MSCs) into various cell types, they can cause harm at the same time. Therefore, it is beneficial to propose methods to differentiate MSCs without factors. Herein, magnetoelectric (ME) nanofibers were synthesized as the scaffold for the growth of MSCs and their differentiation into neural cells without factors. This nanocomposite takes the advantage of the synergies of the magnetostrictive filler, CoFe₂O ₄ nanoparticles (CFO), and piezoelectric polymer, polyvinylidene difluoride (PVDF). Graphene oxide nanosheets were decorated with CFO nanoparticles for a proper dispersion in the polymer through a hydrothermal process. After that, the piezoelectric PVDF polymer, which contained the magnetic nanoparticles, underwent the electrospun process to form ME nanofibers, the ME property of which has the potential to be used in areas such as tissue engineering, biosensors, and actuators.     

How the Internally Organized Direction Sense Is Used to Navigate

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