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.     

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.     

One-Step Quenching and Partitioning Heat Treatment of Medium Carbon Low Alloy Steel

This paper presents the results of novel one-step quenching and partitioning (Q&P) heat treatment conducted on medium carbon low alloy steel sheet. Samples were austenitised at 1193 K followed by interrupted quenching at 473 K for different partitioning times and finally they were quenched in water. Dilatometry was employed for selection of treatment temperatures. Optical and scanning electron microscopy was carried out to examine the microstructural changes. Volume fraction of retained austenite was measured by x-ray diffraction technique. Resulting microstructures were correlated with the mechanical properties such hardness, tensile strength, elongation, impact absorbed energy, etc. The notch tensile and fracture toughness properties of Q&P steels are still lacking therefore notch tensile strength and plain strain fracture toughness tests were conducted and results are reported here. Results of Q&P treatments were also compared with the properties obtained by conventional Quenching and Tempering (Q&T) and normalizing treatments. Optimum strength-ductility balance of about 2000 MPa tensile strength with 11% elongation was achieved in samples quenched at 473 K and isothermally partitioned for 100 s. Higher ductility of Q&P steel was attributed to the presence of 6.8% film-type interlath retained austenite. Fine-grained martensitic structure with high density of interphase boundaries imparted ultrahigh strength. It was further noted that the impact toughness, notch tensile strength and fracture toughness of 1000 s partitioned samples was higher than 100 s partitioned samples. Possible reasons for high toughness are synergetic effect of recovery of dislocations, partial loss of martensite tetragonality and precipitation of fine transition carbides.