Design of Trehalose-Based Amide/Sulfonamide C-type Lectin Receptor Signaling Compounds

Mincle agonists have been shown to induce inflammatory cytokine production, such as tumor necrosis factor-alpha (TNF) and promote the development of a Th1/Th17 immune response that might be crucial to development of effective vaccination against pathogens such as Mycobacterium tuberculosis. As an expansion of our previous work, a library of 6,6′-amide and sulfonamide α,α-d -trehalose compounds with various substituents on the aromatic ring was synthesized efficiently in good to excellent yields. These compounds were evaluated for their ability to activate the human C-type lectin receptor Mincle by the induction of cytokines from human peripheral blood mononuclear cells. A preliminary structure–activity relationship (SAR) of these novel trehalose diamides and sulfonamides revealed that aryl amide-linked trehalose compounds demonstrated improved activity and relatively high potency cytokine production compared to the Mincle ligand trehalose dibehenate adjuvant (TDB) and the natural ligand trehalose dimycolate (TDM) inducing dose-dependent and human-Mincle-specific stimulation in a HEK reporter cell line.     

Zooplankton population in the river tigris: Effects of samarra impoundment

The influences of the Samarra impoundment on the ecology of the downstream sector of the river Tigris are investigated. Zooplankton were collected monthly from July 1987 to July 1988. It was found that zooplankton were most abundant during high river discharges in March. During the month of low river discharge, September, the zooplankton population was greatly reduced immediately below the barrage and remained so to the last downstream station. Several factors seem to explain such differences. The high discharge from the impoundment was found to be the dominating factor and may flush the small backwaters in which zooplankton were abundant, thus increasing the population in the river. Zooplankton species vary in their ability to sustain populations in the river, variations which are due mainly to species-specific characteristics.     

The Complex and Critical Role of Glycine 12 (G12) in Beta-Connexins of Human Skin

Glycine is an amino acid with unique properties because its side chain is composed of a single hydrogen atom. It confers conformational flexibility to proteins and conserved glycines are often indicative of protein domains involving tight turns or bends. All six beta-type connexins expressed in human epidermis (Cx26, Cx30, Cx30.3, Cx31, Cx31.1 and Cx32) contain a glycine at position 12 (G12). G12 is located about halfway through the cytoplasmic amino terminus and substitutions alter connexin function in a variety of ways, in some cases altering protein interactions and leading to cell death. There is also evidence that alteration of G12 changes the structure of the amino terminus in connexin- and amino acid- specific ways. This review integrates structural, functional and physiological information about the role of G12 in connexins, focusing on beta-connexins expressed in human epidermis. The importance of G12 substitutions in these beta-connexins is revealed in two hereditary skin disorders, keratitis ichthyosis and erythrokeratodermia variabilis, both of which result from missense mutations affecting G12.     

云环境下基于本体的用户行为分析引擎研究

针对当前大型互联网站提升用户体验的需求,本研究创造性地提出基于本体的用户行为分析引擎的思想,它通过上下文感知技术,实时获取用户访问页面时的上下文信息(以下也称为动态行为),基于本体对用户行为建模,建立基于规则的上下文推理算法,对监测到的动态用户行为进行实时推理,及时反馈结果信息给用户。实验表明:本研究理论上独辟蹊径,设计实现上结合了云存储技术,能够一定程度改善用户体验效果。     

The Role of Deep Learning in Parking Space Identification andPredictionSystems

In today’s smart city transportation, traffic congestion is a vexing issue, and vehicles seeking parking spaces have been identified as one of the causes leading to approximately 40% of traffic congestion. Identifying parking spaces alone is insufficient because an identified available parking space may have been taken by another vehicle when it arrives, resulting in the driver’s frustration and aggravating traffic jams while searching for another parking space. This explains the need to predict the availability of parking spaces. Recently, deep learning (DL) has been shown to facilitate drivers to find parking spaces efficiently, leading to a promising performance enhancement in parking identification and prediction systems. However, no work reviews DL approaches applied to solve parking identification and prediction problems. Inspired by this gap, the purpose of this work is to investigate, highlight, and report on recent advances in DL approaches applied to predict and identify the availability of parking spaces. A taxonomy of DL-based parking identification and prediction systems is established as a methodology by classifying and categorizing existing literature, and by doing so, the salient and supportive features of different DL techniques for providing parking solutions are presented. Moreover, several open research challenges are outlined. This work identifies that there are various DL architectures, datasets, and performance measures used to address parking identification and prediction problems. Moreover, there are some open-source implementations available that can be used directly either to extend existing works or explore a new domain. This is the first short survey article that focuses on the use of DL-based techniques in parking identification and prediction systems for smart cities. This study concludes that although the deployment of DL in parking identification and prediction systems provides various benefits, the convergence of these two types of systems and DL brings about new issues that must be resolved in the near future.     

Diversity-based combination of non-parametric classifiers for EMG signal decomposition

Non-parametric classification procedures based on a certainty measure and nearest neighbour rule for motor unit potential classification (MUP) during electromyographic (EMG) signal decomposition were explored. A diversity-based classifier fusion approach is developed and evaluated to achieve improved classification performance. The developed system allows the construction of a set of non-parametric base classifiers and then automatically chooses, from the pool of base classifiers, subsets of classifiers to form candidate classifier ensembles. The system selects the classifier ensemble members by exploiting a diversity measure for selecting classifier teams. The kappa statistic is used as the diversity measure to estimate the level of agreement between base classifier outputs, i.e., to measure the degree of decision similarity between base classifiers. The pool of base classifiers consists of two kinds of classifiers: adaptive certainty-based classifiers (ACCs) and adaptive fuzzy k-NN classifiers (AFNNCs) and both utilize different types of features. Once the patterns are assigned to their classes, by the classifier fusion system, firing pattern consistency statistics for each class are calculated to detect classification errors in an adaptive fashion. Performance of the developed system was evaluated using real and simulated EMG signals and was compared with the performance of the constituent base classifiers and the performance of the fixed ensemble containing the full set of base classifiers. Across the EMG signal data sets used, the diversity-based classifier fusion approach had better average classification performance overall, especially in terms of reducing classification errors.     

MODIFICATION OF GEOMETRY AND TECHNOLOGY OF CYLINDRICAL WORMS

The objective of this research is to develop a new method of grinding process that can efficiently make concave profile of the worm thread smooth. The grinding is carried out using a wheel with curvature profile in the axial plane in the form of an arc of logarithmic spiral. The proposed method will provide maximum line of contact between the tool and the worm with minimum stress concentration at any point. The generating equation of the grinding tool profile and its arrangement with respect to the worm during thread grinding were determined.     

Hydrogenation of p‐Nitrophenol to p‐Aminophenol as a Test Reaction for the Catalytic Activity of Supported Pt Catalysts

Hydrogenation of p‐nitrophenol (PNP) to p‐aminophenol (PAP) using NaBH₄ as a reducing agent was studied as a test reaction for determining the catalytic activity of supported Pt catalysts. The initial reaction rate, which is accessible within less than 10 min via online UV‐vis spectroscopy at room temperature, ambient pressure, and in water as solvent, was applied as measure for catalytic activity. For three Pt catalysts supported on porous SiO₂, porous glass, and Al₂O₃, respectively, significant differences in the catalytic activity by almost one order of magnitude were observed. However, especially in the case of very active catalysts, limitations of the reaction by internal or external mass transfer have to be considered.     

Toward a better self-regulation: degree of certainty through fuzzy logic in a formative assessment

In the present paper, we plan to introduce a new procedure for learner’s assessment in the learning environment in a way true to reality by using fuzzy logic. In this evaluation, learner’s responses are accompanied by a degree of certainty expressed by him. This method allows detection of problems encountered by the learner and also to fix the concepts mastered and those that are not. It is a diagnostic procedure that improves the process of content adaptation and self-adjustment on the one hand and makes the knowledge model clearly interpretable and more understandable to learner and tutor/teacher/head teacher on the other hand.