Multiwall carbon nanotube reinforced HA/HDPE biocomposite for bone reconstruction

The healing of bone fractures naturally occurs without surgical intervention. Some damage and fractures in bone tissue are complex and leave remnant deformation, and this requires the use of bone replacement material. Hydroxyapatite (HA) is the main element of the bone mineral form and consider as a bioactive material which supports bone growth. Nevertheless, the HA has poor mechanical properties, such as low tensile strength. Thus the applications in bone replacement have been limited, especially in high load-bearing applications. A Carbone nanotube has newly obtained considerable concern because of their mechanical properties, potentially enhancing the bone implant's clinical efficiency. This study attempted to explain the effect of adding Multi-walled carbon nanotubes MWCNT Nanoparticles to the HDPE/HA bio-composites. Two groups of the composites samples were produced 20HA/80 HDPE and 40 HA/ 60 HDPE with adding (0.6, 1, 1.4, 2) % weights of (MWCNT) to each group. The composites were fabricated using a hot pressing technique with various pressing pressures (29, 57, 86, and 114 Mpa) at a compounding temperature of 150 C° and a holding time of 15 minutes. To evaluate samples' characteristics and performance, X-ray powder diffraction (XRD), surface topography by Field Emission Scanning Electron Microscopy (FE-SEM), tensile strength and, microhardness test were investigated. The results showed that the hybrid bio-composites demonstrated excellent structural integrity, homogeneous with the fibrous structure, and improved mechanical properties. When increasing in MWNT additions and increasing hot-press pressure, enhancing the composites' fracture strength and microhardness is beneficial. The excellent properties of hybrids bio-composite (HA/HDPE/MWCNT) samples for homogeneous fibrous structure and high mechanical properties could be applied in bone tissue engineering for bone reconstruction

Effect of Wiper Edge Geometry on Machining Performance While Turning AISI 1045 Steel in Dry Conditions Using the VIKOR-ML Approach

AISI 1045 can be machined well in all machining operations, namely drilling, milling, turning, broaching and grinding. It has many applications, such as crankshafts, rollers, spindles, shafts, and gears. Wiper geometry has a great influence on cutting forces (Fr, Ff, Fc and R), temperature, material removal rate (MRR) and surface roughness (Ra). Wiper inserts are used to achieve good surface quality and avoid the need to buy a grinding machine. In this paper, an optimization-based investigation into previously reported results for Taguchi’s based L27 orthogonal array experimentations was conducted to further examine effect of the edge geometry on the turning performance of AISI 1045 steel in dry conditions. Three input parameters used in current research include the cutting speed (Vc), feed (f) and depth of cut (ap), while performance measures in this research were Ra, Fr, Ff, Fc, R, temperature (temp) and MRR. The Vise Kriterijumska Optimizacija Kompromisno Resenje (VIKOR) method was used to normalize and convert all the performance measures to a single response known as the VIKOR-based performance index (Vi). The machine learning (ML) approach was used for the prediction and optimization of the input variables. A correlation plot is developed between the input variable and Vi using the ML approach. The optimized setting suggested by Vi-ML is Vc: 160 m/min; ap: 1 mm and f: 0.135 mm/rev, and the corresponding value of Vi was 0.2883, while the predicted values of Ra, Fr, Ff, Fc, R, temp and MRR were 2.111 µm, 43.85 N, 159.33 N, 288.13 N, 332,16 N, 554.4 °C and 21,600 mm3/min, respectively

Data Driven Models as A Powerful Tool to Simulate Emerging Bioprocesses: An Artificial Neural Network Model to Describe Methanotrophic Microbial Activity

The vision for sewage treatment plants is being revised and they are no longer considered as pollutant removing facilities but rather as water resources recovery facilities (WRRFs). However, the newly adopted bioprocesses in WRRFs are not fully understood from the microbiological and kinetic perspectives. Thus, large variations in the outputs of the kinetics-based numerical models are evident. In this research, data driven models (DDM) are proposed as a robust alternative towards modelling emerging bioprocesses. Methanotrophs are multi-use bacterium that can play key role in revalorizing the biogas in WRRFs, and thus, a Multi-Layer Perceptron Artificial Neural Network (ANN) model was developed and optimized to simulate the cultivation of mixed methanotrophic culture considering multiple environmental conditions. The influence of the input variables on the outputs was assessed through developing and analyzing several different ANN model configurations. The constructed ANN models demonstrate that the indirect and complex relationships between the inputs and outputs can be accurately considered prior to the full understanding of the physical or mathematical processes. Furthermore, it was found that ANN models can be used to better understand and rank the influence of different input variables (i.e., the physical parameters that influence methanotrophs) on the microbial activity. Methanotrophic-based bioprocesses are complex due to the interactions between the gaseous, liquid and solid phases. Yet, for the first time, this study successfully utilized DDM to model methanotrophic- based bioprocesses. The findings of this research suggest that DDM are a powerful, alternative modeling tool that can be used to model emerging bioprocesses towards their implementation in WRRFs

Development and validation of a high-performance thin-layer chromatographic method for the quantitative analysis of vitexin in Passiflora foetida herbal formulations

© 2019 Dehon et al. Introduction: Formative evaluations of clinical teaching for emergency medicine (EM) faculty are limited. The goal of this study was to develop a behaviorally-based tool for evaluating and providing feedback to EM faculty based on their clinical teaching skills during a shift. Methods: We used a three-phase structured development process. Phase 1 used the nominal group technique with a group of faculty first and then with residents to generate potential evaluation items. Phase 2 included separate focus groups and used a modified Delphi technique with faculty and residents, as well as a group of experts to evaluate the items generated in Phase 1. Following this, residents classified the items into novice, intermediate, and advanced educator skills. Once items were determined for inclusion and subsequently ranked they were built into the tool by the investigators (Phase 3). Results: The final instrument, the Faculty Shift Card, is a behaviorally-anchored evaluation and feedback tool used to facilitate feedback to EM faculty about their teaching skills during a shift. The tool has four domains: teaching clinical decision-making; teaching interpersonal skills; teaching procedural skills; and general teaching strategies. Each domain contains novice, intermediate, and advanced sections with 2-5 concrete examples for each level of performance. Conclusion: This structured process resulted in a well-grounded and systematically developed evaluation tool for EM faculty that can provide real-time actionable feedback to faculty and support improved clinical teaching

Early assessment of lung function in coronavirus patients using invariant markers from chest X-rays images

The primary goal of this manuscript is to develop a computer assisted diagnostic (CAD) system to assess pulmonary function and risk of mortality in patients with coronavirus disease 2019 (COVID-19). The CAD system processes chest X-ray data and provides accurate, objective imaging markers to assist in the determination of patients with a higher risk of death and thus are more likely to require mechanical ventilation and/or more intensive clinical care.To obtain an accurate stochastic model that has the ability to detect the severity of lung infection, we develop a second-order Markov-Gibbs random field (MGRF) invariant under rigid transformation (translation or rotation of the image) as well as scale (i.e., pixel size). The parameters of the MGRF model are learned automatically, given a training set of X-ray images with affected lung regions labeled. An X-ray input to the system undergoes pre-processing to correct for non-uniformity of illumination and to delimit the boundary of the lung, using either a fully-automated segmentation routine or manual delineation provided by the radiologist, prior to the diagnosis. The steps of the proposed methodology are: (i) estimate the Gibbs energy at several different radii to describe the inhomogeneity in lung infection; (ii) compute the cumulative distribution function (CDF) as a new representation to describe the local inhomogeneity in the infected region of lung; and (iii) input the CDFs to a new neural network-based fusion system to determine whether the severity of lung infection is low or high. This approach is tested on 200 clinical X-rays from 200 COVID-19 positive patients, 100 of whom died and 100 who recovered using multiple training/testing processes including leave-one-subject-out (LOSO), tenfold, fourfold, and twofold cross-validation tests. The Gibbs energy for lung pathology was estimated at three concentric rings of increasing radii. The accuracy and Dice similarity coefficient (DSC) of the system steadily improved as the radius increased. The overall CAD system combined the estimated Gibbs energy information from all radii and achieved a sensitivity, specificity, accuracy, and DSC of 100%, 97% ± 3%, 98% ± 2%, and 98% ± 2%, respectively, by twofold cross validation. Alternative classification algorithms, including support vector machine, random forest, naive Bayes classifier, K-nearest neighbors, and decision trees all produced inferior results compared to the proposed neural network used in this CAD system. The experiments demonstrate the feasibility of the proposed system as a novel tool to objectively assess disease severity and predict mortality in COVID-19 patients. The proposed tool can assist physicians to determine which patients might require more intensive clinical care, such a mechanical respiratory support

Computer Aided Autism Diagnosis Using Diffusion Tensor Imaging

© 2013 IEEE. Autism Spectrum Disorder (ASD), commonly known as autism, is a lifelong developmental disorder associated with a broad range of symptoms including difficulties in social interaction, communication skills, and restricted and repetitive behaviors. In autism spectrum disorder, numerous studies suggest abnormal development of neural networks that manifest itself as abnormalities of brain shape, functionality, and/ or connectivity. The aim of this work is to present our automated computer aided diagnostic (CAD) system for accurate identification of autism spectrum disorder based on the connectivity of the white matter (WM) tracts. To achieve this goal, two levels of analysis are provided for local and global scores using diffusion tensor imaging (DTI) data. A local analysis using the Johns Hopkins WM atlas is exploited for DTI atlas-based segmentation. Furthermore, WM integrity is examined by extracting the most notable features representing WM connectivity from DTI. Interactions of WM features between different areas in the brain, demonstrating correlations between WM areas were used, and feature selection among those associations were made. Finally, a leave-one-subject-out classifier is employed to yield a final per-subject decision. The proposed system was tested on a large dataset of 263 subjects from the National Database of Autism Research (NDAR) with their Autism Diagnostic Observation Schedule (ADOS) scores and diagnosis (139 typically developed: 66 males, and 73 females, and 124 autistics: 66 males, and 58 females), with ages ranging from 96 to 215 months, achieving an overall accuracy of 73%. In addition to this achieved global accuracy, diagnostically-important brain areas were identified, allowing for a better understanding of ASD-related brain abnormalities, which is considered as an essential step towards developing early personalized treatment plans for children with autism spectrum disorder

Identification of potential transcription factors that enhance human iPSC generation

Although many factors have been identified and used to enhance the iPSC reprogramming process, its efficiency remains quite low. In addition, reprogramming efficacy has been evidenced to be affected by disease mutations that are present in patient samples. In this study, using RNA-seq platform we have identified and validated the differential gene expression of five transcription factors (TFs) (GBX2, NANOGP8, SP8, PEG3, and ZIC1) that were associated with a remarkable increase in the number of iPSC colonies generated from a patient with Parkinson's disease. We have applied different bioinformatics tools (Gene ontology, protein–protein interaction, and signaling pathways analyses) to investigate the possible roles of these TFs in pluripotency and developmental process. Interestingly, GBX2, NANOGP8, SP8, PEG3, and ZIC1 were found to play a role in maintaining pluripotency, regulating self-renewal stages, and interacting with other factors that are involved in pluripotency regulation including OCT4, SOX2, NANOG, and KLF4. Therefore, the TFs identified in this study could be used as additional transcription factors that enhance reprogramming efficiency to boost iPSC generation technology.This study was supported by QBRI internal grant (QB16) and the Qatar University Student grant (QUST-2-CMED-2019-1)

Allosteric Inhibition of Parkinson's-Linked LRRK2 by Constrained Peptides

Leucine-Rich Repeat Kinase 2 (LRRK2) is a large, multidomain protein with dual kinase and GTPase function that is commonly mutated in both familial and idiopathic Parkinson's Disease (PD). While dimerization of LRRK2 is commonly detected in PD models, it remains unclear whether inhibition of dimerization can regulate catalytic activity and pathogenesis. Here, we show constrained peptides that are cell-penetrant, bind LRRK2, and inhibit LRRK2 activation by downregulating dimerization. We further show that inhibited dimerization decreases kinase activity and inhibits ROS production and PD-linked apoptosis in primary cortical neurons. While many ATP-competitive LRRK2 inhibitors induce toxicity and mislocalization of the protein in cells, these constrained peptides were found to not affect LRRK2 localization. The ability of these peptides to inhibit pathogenic LRRK2 kinase activity suggests that disruption of dimerization may serve as a new allosteric strategy to downregulate PD-related signaling pathways.</p

Robust Ad-hoc Sensor Routing (RASeR) protocol for mobile wireless sensor networks

Robust Ad-hoc Sensor Routing (RASeR) is a novel protocol for data routing in mobile wireless sensor networks (MWSNs). It is designed to cope with the demanding requirements of emerging technologies, which require the reliable and low-latency delivery of packets in highly mobile conditions. RASeR uses blind forwarding, which is facilitated by a novel method of gradient maintenance. The problem of maintaining a gradient field in a changing topology, without flooding, is solved by using a global time division multiple access MAC. Furthermore, it is enhanced with the additional options of a supersede mode, to aid time-critical applications, reverse flooding, to allow sink-to-sensor commands and energy saving sleep cycles to reduce power consumption. Analytical expressions are derived and verified by simulation. RASeR is compared with the state-of-the-art MWSN routing protocols, PHASeR and MACRO, as well as the MANET protocols, AODV and OLSR. The results indicate that RASeR is a high performance protocol, which shows improvements over PHASeR, MACRO, AODV and OLSR. Tested over varying levels of mobility, scalability and traffic, the simulations yield near perfect PDR in many scenarios, as well as a low end-to-end delay, high throughput, low overhead and low energy consumption. The robustness of this protocol and its consistent reliability, low latency and additional features, makes it highly suitable to a wide number of applications. It is specifically applicable to highly mobile situations with a fixed number of nodes and small payloads