Distinctive Microbial Signatures and Gut-Brain Crosstalk in Pediatric Patients with Coeliac Disease and Type 1 Diabetes Mellitus

Coeliac disease (CD) and Type 1 diabetes mellitus (T1DM) are immune-mediated diseases. Emerging evidence suggests that dysbiosis in the gut microbiome plays a role in the pathogenesis of both diseases and may also be associated with the development of neuropathy. The primary goal in this cross-sectional pilot study was to identify whether there are distinct gut microbiota alterations in children with CD (n = 19), T1DM (n = 18) and both CD and T1DM (n = 9) compared to healthy controls (n = 12). Our second goal was to explore the relationship between neuropathy (corneal nerve fiber damage) and the gut microbiome composition. Microbiota composition was determined by 16S rRNA gene sequencing. Corneal confocal microscopy was used to determine nerve fiber damage. There was a significant difference in the overall microbial diversity between the four groups with healthy controls having a greater microbial diversity as compared to the patients. The abundance of pathogenic proteobacteria Shigella and E. coli were significantly higher in CD patients. Differential abundance analysis showed that several bacterial amplicon sequence variants (ASVs) distinguished CD from T1DM. The tissue transglutaminase antibody correlated significantly with a decrease in gut microbial diversity. Furthermore, the Bacteroidetes phylum, specifically the genus Parabacteroides was significantly correlated with corneal nerve fiber loss in the subjects with neuropathic damage belonging to the diseased groups. We conclude that disease-specific gut microbial features traceable down to the ASV level distinguish children with CD from T1DM and specific gut microbial signatures may be associated with small fiber neuropathy. Further research on the mechanisms linking altered microbial diversity with neuropathy are warranted.     

Effect of Postweld Heat Treatment on Microstructure, Hardness, and Tensile Properties of Laser-Welded Ti-6Al-4V

The effects of postweld heat treatment (PWHT) on 3.2-mm- and 5.1-mm-thick Ti-6Al-4V butt joints welded using a continuous wave (CW) 4-kW Nd:YAG laser welding machine were investigated in terms of microstructural transformations, welding defects, and hardness, as well as global and local tensile properties. Two postweld heat treatments, i.e., stress-relief annealing (SRA) and solution heat treatment followed by aging (STA), were performed and the weld qualities were compared with the as-welded condition. A digital image correlation technique was used to determine the global tensile behavior for the transverse welding samples. The local tensile properties including yield strength and maximum strain were determined, for the first time, for the laser-welded Ti-6Al-4V. The mechanical properties, including hardness and the global and local tensile properties, were correlated to the microstructure and defects in the as-welded, SRA, and STA conditions.     

Global and Local Mechanical Properties of Autogenously Laser Welded Ti-6Al-4V

Ti-6Al-4V sheets, 3.2-mm in thickness, were butt welded using a continuous wave 4 kW Nd:YAG laser welding system. The effect of two main process parameters, laser power and welding speed, on the joint integrity was characterized in terms of the joint geometry, defects, microstructure, hardness, and tensile properties. In particular, a digital image correlation technique was used to determine the local tensile properties of the welds. It was determined that a wide range of heat inputs can be used to fully penetrate the Ti-6Al-4V butt joints during laser welding. At high laser power levels, however, significant defects such as underfill and porosity, can occur and cause marked degradation in the joint integrity and performance. At low welding speeds, however, significant porosity occurs due to its growth and the potential collapse of instable keyholes. Intermediate to relatively high levels of heat input allow maximization of the joint integrity and performance by limiting the underfill and porosity defects. In considering the effect of the two main defects on the joint integrity, the underfill defect was found to be more damaging to the mechanical performance of the weldment than the porosity. Specifically, it was determined that the maximum tolerable underfill depth for Ti-6Al-4V is approximately 6 pct of the workpiece thickness, which is slightly stricter than the value of 7 pct specified in AWS D17.1 for fusion welding in aerospace applications. Hence, employing optimized laser process parameters allows the underfill depth to be maintained within the tolerable limit (6 pct), which in turn prevents degradation in both the weld strength and ductility. To this end, the ability to maintain weld ductility in Ti-6Al-4V by means of applying a high energy density laser welding process presents a significant advantage over conventional arc welding for the assembly of aerospace components.     

EXTRACTION KINETICS OF COPPER BY TRILAURYLAMMONIUM CHLORIDE IN TOLUENE FROM AQUEOUS CHLORIDE MEDIA

Abstract

The rate of extraction of copper(II) from aqueous chloride solutions by tri-n-laurylammonium chloride (TLAHC1) dissolved in toluene has been investigated. The extraction rate was evaluated as function of the chemical composition of the system. The experiments were performed by using a stirred cell with constant inter-facial area. The experiments were performed at a stirring rate of the two phases where the influence of film diffusion is minimized. The data have been interpreted by a mechanism which assumes interfacial chemical reactions between the bulk species containing copper(II) and the molecules of TLAHC1 absorbed at the interface as rate determining.     

Thermodynamic and Experimental Study of the Mg-Sn-Ag-In Quaternary System

Phase equilibria in the Mg-rich region of the Mg-Sn-Ag ternary system were determined by quenching experiments, differential scanning calorimetry, electron probe micro-analysis, and X-ray diffraction techniques. No ternary compounds were found in the studied isothermal sections. A critical evaluation of the available experimental data and a thermodynamic optimization of the Mg-Sn-Ag-In quaternary system were carried out using the calculation of phase diagrams method. The modified quasichemical model in the pair approximation was used for the liquid solution, which exhibits a high degree of short-range order. The solid phases were modeled with the compound energy formalism. All available and reliable experimental data were reproduced within experimental error limits. A self-consistent thermodynamic database was constructed for the Mg-Sn-Ag-In quaternary system, which can be used as a guide for Mg-based alloys development.     

Experimental Investigation of the Ce-Mg-Mn Isothermal Section at 723 K (450 °C) via Diffusion Couples Technique

The isothermal section of the Ce-Mg-Mn phase diagram at 723 K (450 °C) was established experimentally by means of diffusion couples and key alloys. The phase relationships in the complete composition range were determined based on six solid–solid diffusion couples and twelve annealed key alloys. No ternary compounds were found in the Ce-Mg-Mn system at 723 K (450 °C). X-ray diffraction and energy-dispersive X-ray spectroscopy spot analyses were used for phase identification. EDS line-scans, across the diffusion layers, were performed to determine the binary and ternary homogeneity ranges. Mn was observed in the diffusion couples and key alloys microstructures as either a solute element in the Ce-Mg compounds or as a pure element, because it has no tendency to form intermetallic compounds with either Ce or Mg. The fast at. interdiffusion of Ce and Mg produces several binary compounds (Ce _x Mg _y ) during the diffusion process. Thus, the diffusion layers formed in the ternary diffusion couples were similar to those in the Ce-Mg binary diffusion couples, except that the ternary diffusion couples contain layers of Ce-Mg compounds that dissolve certain amount of Mn. Also, the ternary diffusion couples showed layers containing islands of pure Mn distributed in most diffusion zones. As a result, the phase boundary lines were pointing toward Mn-rich corner, which supports the tendency of Mn to be in equilibrium with all the phases in the system.     

The effect of microstructure on the permeability of metallic foams

Pressure drop was measured across complex and simple structure metallic foams at different velocity ranges using air as working fluid. Darcian and non-Darcian permeability parameters, K and C, were determined by fitting experimental data with widely accepted quadratic model of Hazen-Dupuit-Darcy. Generally, the experimental results are in good agreement with the model. The differences in K and C values between the two types of metallic foams are due to the different microstructure. For the simple structure specimens, permeability K increased whereas non-Darcian permeability C decreased with increasing pore diameter. The effect of pore size on the permeability of complex structure metallic foams seems to be opposite to that observed with the simple structure specimens and to results reported by other researchers on other porous medium. This discrepancy mainly stems from the differences in window concentration in addition to some heterogeneity in the foam that impeded the gas flow on one side of the specimens. The difference in pressure drop observed in the different metallic foams is due to combined effect of K and C. However, for simple structure foams, K and C could be predicted by Ergun-like model using appropriate values for the empirical constants. The permeability K is significantly affected by pore size and porosity. The quadratic term of Hazen-Dupuit-Darcy equation is mainly due to the inertia of the flow and partially to the drag exerted by the microstructure of the metallic foam. For both foams, as the porosity increases, pressure drop decreases and permeability, K, increases. The introduction of the open cross sectional area term enabled better understanding of the permeability of metallic foams with intricate morphologies.     

A new intrusion detection system using support vector machines and hierarchical clustering

Whenever an intrusion occurs, the security and value of a computer system is compromised. Network-based attacks make it difficult for legitimate users to access various network services by purposely occupying or sabotaging network resources and services. This can be done by sending large amounts of network traffic, exploiting well-known faults in networking services, and by overloading network hosts. Intrusion Detection attempts to detect computer attacks by examining various data records observed in processes on the network and it is split into two groups, anomaly detection systems and misuse detection systems. Anomaly detection is an attempt to search for malicious behavior that deviates from established normal patterns. Misuse detection is used to identify intrusions that match known attack scenarios. Our interest here is in anomaly detection and our proposed method is a scalable solution for detecting network-based anomalies. We use Support Vector Machines (SVM) for classification. The SVM is one of the most successful classification algorithms in the data mining area, but its long training time limits its use. This paper presents a study for enhancing the training time of SVM, specifically when dealing with large data sets, using hierarchical clustering analysis. We use the Dynamically Growing Self-Organizing Tree (DGSOT) algorithm for clustering because it has proved to overcome the drawbacks of traditional hierarchical clustering algorithms (e.g., hierarchical agglomerative clustering). Clustering analysis helps find the boundary points, which are the most qualified data points to train SVM, between two classes. We present a new approach of combination of SVM and DGSOT, which starts with an initial training set and expands it gradually using the clustering structure produced by the DGSOT algorithm. We compare our approach with the Rocchio Bundling technique and random selection in terms of accuracy loss and training time gain using a single benchmark real data set. We show that our proposed variations contribute significantly in improving the training process of SVM with high generalization accuracy and outperform the Rocchio Bundling technique.