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.     

Thermosensitive nanosized micelles from cholesteryl-modified hydroxypropyl cellulose as a novel carrier of hydrophobic drugs

The thermosensitive micelles based on the two series of cholesteryl-modified hydroxypropyl cellulose (series 1 and 2, respectively) were used as a promising drug carrier. The polymers 1a and 2a with side chain substitution degrees D _Chol?=?0.7 and 2.1?mol% were selected for micelle preparation, respectively. Polymeric micelles were prepared by the co-solvent evaporation method. The aqueous self-assembly of the polymers was studied using fluorescence analysis and transmission electron microscopy (TEM). The critical micelle concentrations (CMCs) values of the various D _Chol of polymers were evaluated in the range of ca. 0.13?C0.29?g/L which decreased with the increase of D _Chol in both series. Furthermore, the CMC values displayed a downtrend profile, with increasing the temperature. The polymer 1a with less D _Chol had lower CMC than that of polymer 2a. By using the naproxen as a hydrophobic model drug, the drug-loaded micelles were prepared. The TEM image of naproxen-loaded micelles of polymer 1a with 40?% drug-loading efficiency and 8?% loading capacity showed that micelles were regularly spherical in shape with a mean diameter of 70?nm. The unmodified HPC exhibited a lower critical solution temperature (LCST) of more than 41?°C in water, while polymeric micelles in aqueous solution presented an LCST of 38.7?°C. A drug release study was performed by dialysis method in phosphate-buffered solution at 25, 37 and 40?°C, respectively. The release kinetics of naproxen from the polymeric micelles revealed a thermosensitivity, since its release rate was higher at 40?°C than at 25?°C.     

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.     

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.     

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.     

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.     

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.     

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.     

Effect of Slag Composition on the Crystallization Kinetics of Synthetic CaO-SiO<Subscript>2</Subscript>-Al<Subscript>2</Subscript>O<Subscript>3</Subscript>-MgO Slags

The crystallization kinetics of CaO-SiO₂-Al₂O₃-MgO (CSAM) slags was studied with the aid of single hot thermocouple technique (SHTT). Kinetic parameters such as the Avrami exponent (n), rate coefficient (K), and effective activation energy of crystallization (E _A ) were obtained by kinetic analysis of data obtained from in situ observation of glassy to crystalline transformation and image analysis. Also, the dependence of nucleation and growth rates of crystalline phases were quantified as a function of time, temperature, and slag basicity. Together with the observations of crystallization front, they facilitated establishing the dominant mechanisms of crystallization. In an attempt to predict crystallization rate under non-isothermal conditions, a mathematical model was developed that employs the rate data of isothermal transformation. The model was validated by reproducing an experimental continuous cooling transformation diagram purely from isothermal data.     

The UCR Time Series Archive

The UCR time series archive – introduced in 2002, has become an important resource in the time series data mining community, with at least one thousand published papers making use of at least one data set from the archive. The original incarnation of the archive had sixteen data sets but since that time, it has gone through periodic expansions. The last expansion took place in the summer of 2015 when the archive grew from 45 to 85 data sets. This paper introduces and will focus on the new data expansion from 85 to 128 data sets. Beyond expanding this valuable resource, this paper offers pragmatic advice to anyone who may wish to evaluate a new algorithm on the archive. Finally, this paper makes a novel and yet actionable claim: of the hundreds of papers that show an improvement over the standard baseline (1-nearest neighbor classification), a fraction might be mis-attributing the reasons for their improvement. Moreover, the improvements claimed by these papers might have been achievable with a much simpler modification, requiring just a few lines of code.