Counting eosinophils in bronchoalveolar lavage fluid images with fuzzy methodology

This paper proposes a new fuzzy approach to count eosinophils, as a measure of inflammation, in bronchoalveolar lavage fluid images, provided by digital camera through microscope. We use fuzzy cluster analysis and fuzzy classification algorithm to determine the number of objects in an image. For this purpose, a fuzzy image processing procedure consisting of five main stages is presented. The first stage is pre-highlighting the objects in the images by using an image pre-processing method for enhancement, which is sharpening the image with the Laplaian high pass filter in order to have acceptable contrast in the image. The second stage is segmentation by clustering with fuzzy c-mean algorithm for portioning. In this stage the clustered data are the rough symbols of objects in the image containing noise. In the third step, first, a Gaussian low pass filter is used for noise reduction. Then, a contrast adoption in the image is done by modifying the membership functions in the image [H.R. Tizhoosh, G. Krell, B. Michaelis, Knowledge-based enhancement of megavoltage images in radiation therapy using a hybrid neuro-fuzzy system, Image and Vision Computing 19(July) (2000) 217–233]. Object recognition, the fourth stage, will be done by using fuzzy labeling for the objects in the image, using a fuzzy classification method. The number of labeled images shows the number of eosinophils in an image which is an index for diagnosing inflammation. The last stage is tuning parameters and verification of the system performance by using a feed forward Neural Network.     

Glyphosate Adsorption from Water Using Hierarchically Porous Metal–Organic Frameworks

The selective removal of one ligand in mixed-ligand MOFs upon thermolysis provides a powerful strategy to introduce additional mesopores without affecting the overall MOF structure. By varying the initial ligand ratio, MOFs of the MIL-125-Ti family with two distinct hierarchical pore architectures are synthesized, resembling either large cavities or branching fractures. The performance of the resulting hierarchically porous MOFs is evaluated toward the adsorptive removal of glyphosate (N-(phosphonomethyl)glycine) from water, and the adsorption kinetics and mechanism are examined. Due to their strong affinity for phosphoric groups, the numerous Ti–OH groups resulting from the selective ligand removal act as natural anchor points for effective glyphosate uptake. The relationships between contact duration, glyphosate concentration, and adsorbent dosage are investigated, and the impact of these parameters on the effectiveness of glyphosate removal from contaminated water samples is examined. The introduction of additional mesopores has increased the adsorption capacities by nearly 3 times with record values exceeding 440.9 mg g⁻¹, which ranks these MOFs among the best-reported adsorbents.     

Parkinson’s Disease Detection Using Biogeography-Based Optimization

In recent years, Parkinson's Disease (PD) as a progressive syndrome of the nervous system has become highly prevalent worldwide. In this study, a novel hybrid technique established by integrating a Multi-layer Perceptron Neural Network (MLP) with the Biogeography-based Optimization (BBO) to classify PD based on a series of biomedical voice measurements. BBO is employed to determine the optimal MLP parameters and boost prediction accuracy. The inputs comprised of 22 biomedical voice measurements. The proposed approach detects two PD statuses: 0-disease status and 1- good control status. The performance of proposed methods compared with PSO, GA, ACO and ES method. The outcomes affirm that the MLP-BBO model exhibits higher precision and suitability for PD detection. The proposed diagnosis system as a type of speech algorithm detects early Parkinson’s symptoms, and consequently, it served as a promising new robust tool with excellent PD diagnosis performance.     

Control of Superelastic Behavior of NiTi Wires Aided by Thermomechanical Treatment with Reference to Three-Point Bending

The aim of this study is to investigate the effect of thermomechanical treatment on the superelastic behavior of a Ti-50.5 at.%Ni wire in terms of loading/unloading plateau, mechanical hysteresis, and permanent set to optimize these parameters for orthodontic applications. A new three-point bending fixture, oral cavity configuration three-point bending (OCTPB) test, was utilized to determine the superelastic property in clinical condition, and therefore, the tests were carried out at 37 °C. The results indicate that the thermomechanical treatment is crucial for thermal transformation and mechanically induced transformation characteristics of the wire. Annealing of thermomechanically treated specimens at 300 and 400 °C for 1/2 and 1 h leads to good superelasticity for orthodontic applications. However, the best superelasticity at body temperature is obtained after annealing at 300 °C for 1/2 h with regard to low and constant unloading force and minimum permanent set.     

Purification of metallurgical silicon using iron as an impurity getter part I: Growth and separation of Si

The purification of metallurgical grade silicon (MG-Si) using a combination of solvent refining and physical separation is studied. MG-Si was alloyed with iron and solidified under different cooling rates in order to grow pure Si dendrites from the alloy. The Si dendrites were then separated using a gravity-based method. The separation method relies on the significantly different densities of Si and FeSi₂, and it uses a heavy liquid with specific gravity between the two phases to float the light Si particles to the surface of the liquid, while the heavy iron silicide sinks. The effects of the particle size and cooling rate on the yield and separation efficiency of the Si phase were investigated by quantifying the fraction of Si in the sinks and floats. The results demonstrate that the crushing size of the particles prior to separation should be approximately the same as the width of the dendrites in order to maximize the separation efficiency while simultaneously lowering the grinding cost.     

Nanosilicon carbide/hydroxyapatite nanocomposites: structural,mechanical and in vitro cellular properties

In this study, bioceramic nanocomposites were synthesized by sintering compacted bodies of hydroxyapatite (HA) mixed with 5 or 15 wt% nanosilicon carbide at 1100 or 1200°C in a reducing atmosphere. Pure hydroxyapatite was also prepared for comparison. Phase compositions, structural and physical properties of the composites were studied using appropriate techniques. Some in vitro biological properties of the composites were also investigated by using newrat calvaria osteoblastic cells. X-ray diffraction analysis indicated that tricalcium phosphate (TCP) comprising negligible α-TCP and considerable β-TCP were formed in composites during sintering meanwhile hydroxyapatite and silicon carbide (SiC) were also existed in the composition. Based on the results, that composite made of 5 wt% nanosilicon carbide exhibited higher bending strength, fracture toughness and bulk density than pure HA and composite with 15 wt% silicon carbide. The scanning electron microscopy coupled with energy dispersive X-ray analysis revealed that the addition of nanosilicon carbide suppressed the grain growth and yielded a feature of island-type clusters consisting of blistered calcium phosphate (HA and TCP) and SiC grains. Also, in this study, better proliferation rate and alkaline phosphatase activity were observed for the osteoblastic cells seeded on top of the composites compared to pure HA. Overall, the results indicated that the composite of 95 wt% hydroxyapatite and 5 wt% SiC exhibited better mechanical and biological properties than pure HA and further addition of SiC failed strength and toughness.     

Probiotic-Induced Tolerogenic Dendritic Cells: A Novel Therapy for Inflammatory Bowel Disease?

Inflammatory bowel diseases (IBDs) are immune-mediated, chronic relapsing diseases with a rising prevalence worldwide in both adult and pediatric populations. Treatment options for immune-mediated diseases, including IBDs, are traditional steroids, immunomodulators, and biologics, none of which are capable of inducing long-lasting remission in all patients. Dendritic cells (DCs) play a fundamental role in inducing tolerance and regulating T cells and their tolerogenic functions. Hence, modulation of intestinal mucosal immunity by DCs could provide a novel, additional tool for the treatment of IBD. Recent evidence indicates that probiotic bacteria might impact immunomodulation both in vitro and in vivo by regulating DCs’ maturation and producing tolerogenic DCs (tolDCs) which, in turn, might dampen inflammation. In this review, we will discuss this evidence and the mechanisms of action of probiotics and their metabolites in inducing tolDCs in IBDs and some conditions associated with them.     

Superelastic behavior of aged and thermomechanical treated NiTi alloy at Af + 10 °C

The phase transformation in NiTi shape memory alloys is very sensitive to aging and thermomechanical treatment (cold work and annealing). In this research, a Ni-rich alloy has been cold-rolled and then subjected to various annealing treatments and also aging treatments at different temperatures and times. To investigate the superelastic behavior, mechanical testing was carried out at A_f + 10 °C to eliminate the effect of testing temperature on the superelasticity parameters including loading/unloading plateau and mechanical hysteresis. The effect of dislocations and precipitates resulted from thermomechanical and aging treatments on the transformation behavior was studied by Differential Scanning Calorimetry (DSC) and three point bending (TPB) tests. The results showed that these treatments are effective on the thermal and mechanical-induced phase transformation characteristics. Transformation temperatures increase and loading/unloading plateau and thermal and mechanical hystereses decrease by increasing aging and annealing time. Increasing of aging and annealing temperatures results in decreasing of loading/unloading plateau and thermal and mechanical hysteresis.     

Orchestrated structure evolution: modeling growth-regulated nanomanufacturing

Orchestrated structure evolution (OSE) is a scalable manufacturing method that combines the advantages of top-down (tool-directed) and bottom-up (self-propagating) approaches. The method consists of a seed patterning step that defines where material nucleates, followed by a growth step that merges seeded islands into the final patterned thin film. We develop a model to predict the completed pattern based on a computationally efficient approximate Green's function solution of the diffusion equation plus a Voronoi diagram based approach that defines the final grain boundary structure. Experimental results rely on electron beam lithography to pattern the seeds, followed by the mass transfer limited growth of copper via electrodeposition. The seed growth model is compared with experimental results to quantify nearest neighbor seed-to-seed interactions as well as how seeds interact with the pattern boundary to impact the local growth rate. Seed-to-seed and seed-to-pattern interactions are shown to result in overgrowth of seeds on edges and corners of the shape, where seeds have fewer neighbors. We explore how local changes to the seed location can be used to improve the patterning quality without increasing the manufacturing cost. OSE is shown to enable a unique set of trade-offs between the cost, time, and quality of thin film patterning.