Mixed Micelles of Surface Active Ionic Liquid (SAIL)–Octylphenol Ethoxylate: A Novel Reaction Medium for Selective Oxidation of Toluene to Benzaldehyde

Ionic liquids have been found to be suitable alternatives to volatile organic solvents in chemical transformation. Through a proper choice of cations and anions, the properties of an ionic liquid can be tuned so that it resembles an amphiphile. Such specially designed molecules are known as surface-active ionic liquids (SAIL). Like conventional surfactants, SAIL also form aggregates in an aqueous medium. Studies show that the mixing of SAIL with conventional surfactants leads to synergistic micellization. However, very few reports are available on the application of such systems as reaction media. Present study focuses on the application of mixed micelles of 1-tetradecyl-3-methylimidazol-1-ium bromide, ([C₁₄mim]Br) with nonionic surfactant, Octylphenol ethoxylate with 10 moles of ethylene oxide (OPE-10). Enhanced solubilization and selective catalytic oxidation of toluene using hydrogen peroxide as an oxidant and tungstic acid as a catalyst have been studied in detail using this system.     

Cumin Prevents 17β-Estradiol-Associated Breast Cancer in ACI Rats

Breast cancer (BC) is a leading cause of cancer deaths in women in less developed countries and the second leading cause of cancer death in women in the U.S. In this study, we report the inhibition of E2-mediated mammary tumorigenesis by Cuminum cyminum (cumin) administered via the diet as cumin powder, as well as dried ethanolic extract. Groups of female ACI rats were given either an AIN-93M diet or a diet supplemented with cumin powder (5% and 7.5%, w/w) or dried ethanolic cumin extract (1%, w/w), and then challenged with subcutaneous E2 silastic implants (1.2 cm; 9 mg). The first appearance of a palpable mammary tumor was significantly delayed by both the cumin powder and extract. At the end of the study, the tumor incidence was 96% in the control group, whereas only 55% and 45% animals had palpable tumors in the cumin powder and extract groups, respectively. Significant reductions in tumor volume (660 ± 122 vs. 138 ± 49 and 75 ± 46 mm³) and tumor multiplicity (4.21 ± 0.43 vs. 1.16 ± 0.26 and 0.9 ± 0.29 tumors/animal) were also observed by the cumin powder and cumin extract groups, respectively. The cumin powder diet intervention dose- and time-dependently offset E2-related pituitary growth, and reduced the levels of circulating prolactin and the levels of PCNA in the mammary tissues. Mechanistically, the cumin powder diet resulted in a significant reversal of E2-associated modulation in ERα, CYP1A1 and CYP1B1. Further, the cumin powder diet reversed the expression levels of miRNAs (miR-182, miR-375, miR-127 and miR-206) that were highly modulated by E2 treatment. We analyzed the composition of the extract by GC/MS and established cymene and cuminaldehyde as major components, and further detected no signs of gross or systemic toxicity. Thus, cumin bioactives can significantly delay and prevent E2-mediated mammary tumorigenesis in a safe and effective manner, and warrant continued efforts to develop these clinically translatable spice bioactives as chemopreventives and therapeutics against BC.     

Thermal degradation of cellulose and its phosphorylated products in air and nitrogen

The thermal degradation of cellulose and its phosphorylated products (phosphates, diethylphosphate, and diphenylphosphate) were studied in air and nitrogen by differential thermal analysis and dynamic thermogravimetry from ambient temperature to 750°C. From the resulting data various thermodynamic parameters were obtained following the methods of Broido and Freeman and Carroll. The values of E_a for decomposition for phosphorylated cellulose were found to be in the range 55–138 kJ mol^?1 in air and 85–152 kJ mol^?1 in nitrogen and depended upon the percent of phosphorus contents in the samples. The mass spectrum of cellobiose phosphate indicated the absence of the molecular ion, indicating that the compound was thermally unstable. The IR spectra of the pyrolysis residues of cellulose phosphate gave indication of formation of a compound having C?O and P?O groups. A fire retardancy mechanism for the thermal degradation of cellulose phosphate has been proposed.     

Characterization of acidic groups in oxycelluloses III. Effect of cation freeing and blocking on estimation of carboxyl groups and lactones by the idometric method

A differentiation between free carboxyl and various lactone groups in different types of oxycelluloses was carried out based on the variation in their reaction rate with KI/KIO₃ solution. For oxycelluloses prepared under acidic conditions, a large proportion of carboxyl groups are present in free form. Each oxycellulose was also studied after cation freeing as well as after blocking the free carboxyl groups by treatment with sodium chloride. Cation freeing was found to cause considerable lactonization of carboxyl groups; sodium chloride treatment blocked them only partially.     

Phosphate sorption isotherms in relation to P nutrition of wheat (Triticum aestivum)

The phosphate sorption isotherms are needed to explain differential plant responses to P fertilization in soils. Laboratory and greenhouse experiments investigated the use of phosphorus sorption isotherms in relation to P fertilizer requirement of wheat in ten benchmark soils of Punjab, India. The modified Mitscherlich Equation (3) was used to describe plant response observed in different soils. Maximum obtainable yield (MOY) ranged from 11.6 g pot^–1 in Gurdaspur (I) sandy clay loam to 7.0 g pot^–1 in Nabha sandy clay loam. Response to P applied @ 25 mg P kg^–1 soil was maximum (77%) in Bathinda sand and minimum in Chuharpur clay loam (33%). The response curvature varied from 3.74 × 10^–2 in Nabha sandy clay loam to 4.43 × 10^–2 in Kanjli sandy loam. The soil solution P required to produce optimum yield (90% MOY) varied from 1.61 µg ml^–1 in Bathinda sand to 0.10 µg ml^–1 in Sadhugarh clay. Dry matter yield obtained at 0.2 µg ml^–1 solution P concentration ranged from 55% in Bathinda sand to 85% of MOY in Gurdaspur (II) clay loam. At the same solution P concentration (0.1 µg P ml^–1), dry matter yield was 91% in Sadhugarh clay, 80% in Gurdaspur (II) clay loam and, 43% of MOY in Bathinda sand and eventually coincided with the decreasing maximum buffer capacity (MBC) in these soils. At the same level of sorbed P (100 mg P kg^–1 soil) the yield was observed to be inversely proportional to MBC. The study, therefore, concludes that, soils should be grouped according to their P sorption characteristics and MBC before using critical soil solution P as a criterion for obtaining optimum yields.     

Micro-computed tomography scanning approaches to quantify,parameterize and visualize bioturbation activity in clogged streambeds: A proof of concept

Fine particle clogging and faunal bioturbation are two key processes co-occurring in the hyporheic zone that potentially affect hyporheic exchange through modifications in the sediment structure of streambeds. Clogging results from excessive fine sediment infiltration and deposition in rivers, and it is known to decrease matrix porosity and potentially reduce permeability. Faunal bioturbation activity may compensate for the negative effect of clogging by reworking the sediment, increasing porosity, and preventing further infiltration of fines. Although both processes of clogging and bioturbation have received significant attention in the literature separately, their combined effects on streambed sediment structure are not well understood, mostly due to the lack of a standard methodology for their assessment. Here, we illustrate a novel methodology using X-ray computed tomography (CT), as proof of concept, to investigate how, together, clogging and bioturbation affect streambed porosity in a controlled flow-through flume. By visualising gallery formations of an upward conveyor macroinvertebrate; Lumbriculus variegatus as a model species, we quantified bioturbation activity in a clogged streambed, focusing on orientation, depth, and volume at downwelling and upwelling areas of the flume. Gallery creation increased the porosity of the streambed sediment, suggesting a potential improvement in permeability and a possible offset of clogging effects. We illustrate the promising use of X-ray CT as a tool to assess bioturbation in clogged streambeds, and the potential role of bioturbation activity supporting hyporheic exchange processes in streambeds, warranting further studies to understand the extent of bioturbation impacts in natural systems.     

Global optimization methods for engineering applications: A review

A review of the methods for global optimization reveals that most methods have been developed for unconstrained problems. They need to be extended to general constrained problems because most of the engineering applications have constraints. Some of the methods can be easily extended while others need further work. It is also possible to transform a constrained problem to an unconstrained one by using penalty or augmented Lagrangian methods and solve the problem that way. Some of the global optimization methods find all the local minimum points while others find only a few of them. In any case, all the methods require a very large number of calculations. Therefore, the computational effort to obtain a global solution is generally substantial. The methods for global optimization can be divided into two broad categories: deterministic and stochastic. Some deterministic methods are based on certain assumptions on the cost function that are not easy to check. These methods are not very useful since they are not applicable to general problems. Other deterministic methods are based on certain heuristics which may not lead to the true global solution. Several stochastic methods have been developed as some variation of the pure random search. Some methods are useful for only discrete optimization problems while others can be used for both discrete and continuous problems. Main characteristics of each method are identified and discussed. The selection of a method for a particular application depends on several attributes, such as types of design variables, whether or not all local minima are desired, and availability of gradients of all the functions.Notation Number of equality constraints - () ^T A transpose of a vector - A A hypercubic cell in clustering methods - Distance between two adjacent mesh points - Probability that a uniform sample of sizeN contains at least one point in a subsetA ofS - A(v, x) Aspiration level function - A The set of points with cost function values less thanf(x _G ^* ) +. Same asA _f () - A _f () A set of points at which the cost function value is within off(x _G ^* ) - A () A set of points x with[f(x)] smaller than - A _N The set ofN random points - A _q The set of sample points with the cost function value f _q - _Q The contraction coefficient; –1 _Q 0 - _R The expansion coefficient; _E > 1 - _R The reflection coefficient; 0 < _R 1 - A _x () A set of points that are within the distance from x _G ^* - D Diagonal form of the Hessian matrix - det() Determinant of a matrix - d _j A monotonic function of the number of failed local minimizations - d _t Infinitesimal change in time - d _x Infinitesimal change in design - A small positive constant - (t) A real function called the noise coefficient - ₀ Initial value for(t) - exp() The exponential function - f ^(c) The record; smallest cost function value over X^(C) - [f(x)] Functional for calculating the volume fraction of a subset - Second-order approximation tof(x) - f(x) The cost function - An estimate of the upper bound of global minimum - f ^E The cost function value at x^E - f ^L The cost function value at x^L - f _opt The current best minimum function value - f ^P The cost function value at x ^P - f ^Q The cost function value at x ^Q - f _q A function value used to reduce the random sample - f ^R The cost function value at x ^R - f ^S The cost function value at x^S - f _{T F min} A common minimum cost function value for several trajectories - f _{TF opt} The best current minimum value found so far forf _{TF min} - f ^W The cost function value at x ^W - G Minimum number of points in a cell (A) to be considered full - The gamma function - A factor used to scale the global optimum cost in the zooming method - Minimum distance assumed to exist between two local minimum points - gi(x) Constraints of the optimization problem - H The size of the tabu list - H(x^*) The Hessian matrix of the cost function at x^* - h _j Half side length of a hypercube - h _m Minimum half side lengths of hypercubes in one row - I The unity matrix - ILIM A limit on the number of trials before the temperature is reduced - J The set of active constraints - K Estimate of total number of local minima - k Iteration counter - The number of times a clustering algorithm is executed - L Lipschitz constant, defined in Section 2 - L The number of local searches performed - _i The corresponding pole strengths - log () The natural logarithm - LS Local search procedure - M Number of local minimum points found inL searches - m Total number of constraints - m(t) Mass of a particle as a function of time - m() TheLebesgue measure of thea set - Average cost value for a number of random sample of points inS - N The number of sample points taken from a uniform random distribution - n Number of design variables - n(t) Nonconservative resistance forces - n _c Number of cells;S is divided inton _c cells - NT Number of trajectories - Pi (3.1415926) - P _i ^(j) Hypersphere approximating thej-th cluster at stagei - p(x ⁽ⁱ⁾⁾ Boltzmann-Gibbs distribution; the probability of finding the system in a particular configuration - pg A parameter corresponding to each reduced sample point, defined in (36) - Q An orthogonal matrix used to diagonalize the Hessian matrix - _i (i = 1, K) The relative size of thei-th region of attraction - r _i ^(j) Radius of thej-th hypersp here at stagei - R _x ^* Region of attraction of a local minimum x^* - r _j Radius of a hypersphere - r A critical distance; determines whether a point is linked to a cluster - R ⁿ A set ofn tuples of real numbers - A hyper rectangle set used to approximateS - S The constraint set - A user supplied parameter used to determiner - s The number of failed local minimizations - T The tabu list - t Time - T(x) The tunneling function - T _c (x) The constrained tunneling function - T _i The temperature of a system at a configurationi - TLIMIT A lower limit for the temperature - TR A factor between 0 and 1 used to reduce the temperature - u(x) A unimodal function - V(x) The set of all feasible moves at the current design - v(x) An oscillating small perturbation. - V(y⁽ⁱ⁾) Voronoi cell of the code point y⁽ⁱ⁾ - v^–1 An inverse move - v _k A move; the change from previous to current designs - w(t) Ann-dimensional standard. Wiener process - x Design variable vector of dimensionn - x^# A movable pole used in the tunneling method - x⁽⁰⁾ A starting point for a local search procedure - X^(c) A sequence of feasible points {x⁽¹⁾, x⁽²⁾,,x^(c)} - x(t) Design vector as a function of time - X^* The set of all local minimum points - x^* A local minimum point forf(x) - x^*(i) Poles used in the tunneling method - x _G ^* A global minimum point forf(x) - Transformed design space - The velocity vector of the particle as a function of time - Acceleration vector of the particle as a function of time - x ^C Centroid of the simplex excluding x ^L - x ^c A pole point used in the tunneling method - x ^E An expansion point of x ^R along the direction x ^C x ^R - x ^L The best point of a simplex - x ^P A new trial point - x ^Q A contraction point - x ^R A reflection point; reflection of x ^W on x ^C - x ^S The second worst point of a simplex - x ^W The worst point of a simplex - The reduced sample point with the smallest function value of a full cell - Y The set of code points - y ⁽ⁱ⁾ A code point; a point that represents all the points of thei-th cell - z A random number uniformly distributed in (0,1) - Z ^(c) The set of points x where [f ^(c) – ] is smaller thanf(x) - []₊ Max (0,) - | | Absolute value - The Euclidean norm - f[x(t)] The gradient of the cost function     

Learning Noise-Assisted Robust Image Features for Fine-Grained Image Retrieval

Fine-grained image search is one of the most challenging tasks in computer vision that aims to retrieve similar images at the fine-grained level for a given query image. The key objective is to learn discriminative fine-grained features by training deep models such that similar images are clustered, and dissimilar images are separated in the low embedding space. Previous works primarily focused on defining local structure loss functions like triplet loss, pairwise loss, etc. However, training via these approaches takes a long training time, and they have poor accuracy. Additionally, representations learned through it tend to tighten up in the embedded space and lose generalizability to unseen classes. This paper proposes a noise-assisted representation learning method for fine-grained image retrieval to mitigate these issues. In the proposed work, class manifold learning is performed in which positive pairs are created with noise insertion operation instead of tightening class clusters. And other instances are treated as negatives within the same cluster. Then a loss function is defined to penalize when the distance between instances of the same class becomes too small relative to the noise pair in that class in embedded space. The proposed approach is validated on CARS-196 and CUB-200 datasets and achieved better retrieval results (85.38% recall@1 for CARS-196% and 70.13% recall@1 for CUB-200) compared to other existing methods.     

Antineoplastic and antioxidant potential of phycofabricated silver nanoparticles using microalgae Chlorella minutissima

This study for the first time reports on fresh water microalgae Chlorella minutissima aqueous extract (CmAe) which was utilized for the biogenic synthesis of silver nanoparticles and tested their antineoplastic potential against Liver Hepatocellular Carcinoma (HepG2) cell line. The characteristic colour change of the reaction mixture from greenish yellow to yellowish brown confirmed the synthesis of Chlorella minutissima silver nanoparticles (CmAgNPs). Microscopic analysis revealed CmAgNPs to be spherical‐shaped with particle size ranging from 10 to 30 nm. The carbohydrates and proteins distinctive peaks were observed in Fourier transform infrared spectroscopy (FTIR) spectra which suggested these biomolecules acted as reducing and capping agents. Further, the crystalline nature of CmAgNPs was confirmed by X‐ray diffraction (XRD) analysis. CmAgNPs showed maximum free radical scavenging proving it to be more potent antioxidant agent as compared to CmAe. The mortality rate of HepG2 cells treated with CmAgNPs was found to be 91.8 % at 120 μg/ml with IC₅₀ value 12.42 ± 1.096 μg/ml after 48 h whereas no effect was observed on normal Human Embryonic Kidney (HEK 293) cells. Fluorescent images of the treated HepG2 cells revealed the formation of apoptotic bodies, condensed nuclei and cell shrinkage indicating their effectiveness against the cancer cells.Inspec keywords: silver, nanoparticles, nanomedicine, microorganisms, cellular biophysics, nanofabrication, scanning electron microscopy, transmission electron microscopy, atomic force microscopy, proteins, Fourier transform infrared spectra, molecular biophysics, X‐ray chemical analysis, X‐ray diffraction, kidney, cancer, biomedical materialsOther keywords: antineoplastic potential, antioxidant potential, phycofabricated silver nanoparticle, Chlorella minutissima, freshwater microalgae, aqueous extract, liver hepatocellular carcinoma cell line, CmAgNP synthesis, field emission scanning electron microscopy, high‐resolution transmission electron microscopy, atomic force microscopy, dynamic light scattering, carbohydrate, protein, Fourier transform infrared spectroscopy, biomolecule, energy‐dispersive X‐ray spectroscopy, elemental silver signal, CmAgNP crystalline, X‐ray diffraction analysis, antioxidant agent, HepG2 cell mortality rate, human embryonic kidney, HEK 293 cell, fluorescent image, apoptotic body formation, condensed nuclei, cell shrinkage, cancer cell, antineoplastic agent, Ag