Jadavpur University

In 2008 we published the first set of guidelines for standardizing research in autophagy. Since then, research on this topic has continued to accelerate, and many new scientists have entered the field. Our knowledge base and relevant new technologies have also been expanding. Accordingly, it is important to update these guidelines for monitoring autophagy in different organisms. Various reviews have described the range of assays that have been used for this purpose. Nevertheless, there continues to be confusion regarding acceptable methods to measure autophagy, especially in multicellular eukaryotes. For example, a key point that needs to be emphasized is thatthere is a difference between measurements that monitor the numbers or volume of autophagic elements (e.g., autophagosomes or autolysosomes) at any stage of the autophagic process versus those that measure flux through the autophagy pathway (i.e., the completeprocess including the amount and rate of cargo sequestered and degraded). In particular, a block in macroautophagy that results in autophagosome accumulation must be differentiated from stimuli that increase autophagic activity, defined as increasedautophagy induction coupled with increased delivery to, and degradation within, lysosomes (inmost higher eukaryotes and some protists such as Dictyostelium) or the vacuole (in plants and fungi). In other words, it is especially important that investigators new to the field understand that the appearance of more autophagosomes does not necessarily equate with more autophagy. In fact, in manycases, autophagosomes accumulate because of a block in trafficking to lysosomes without a concomitant change in autophagosome biogenesis, whereas an increase in autolysosomes may reflect a reduction in degradative activity. It is worth emphasizing here that lysosomal digestion is a stage of autophagy and evaluating its competence is a crucial part of the evaluation of autophagic flux, or complete autophagy. Here, we present a set of guidelines for the selection and interpretation of methods for use by investigators who aim to examine macroautophagy and related processes, as well as forreviewers who need to provide realistic and reasonable critiques of papers that are focused on these processes. These guidelines are not meant to be a formulaic set of rules, because the appropriate assays depend in part on the question being asked and the system being used. In addition, we emphasize that no individual assay is guaranteed to be the most appropriate one in every situation, and we strongly recommend the use of multipleassays to monitor autophagy. Along these lines, because of the potential for pleiotropic effects due to blocking autophagy through genetic manipulation, it is imperative to target by gene knockout or RNA interference more than one autophagyrelated protein. In addition, some individual Atg proteins, or groups of proteins, are involved in other cellular pathways implying that not all Atg proteins can be used as a specific marker for an autophagic process. In these guidelines, we consider these various methods of assessing autophagy and what information can, or cannot, be obtained from them. Finally, by discussing the merits and limits of particular assays, we hope to encourage technical innovation in the field.

Differential evolution (DE) is arguably one of the most powerful stochastic real-parameter optimization algorithms in current use. DE operates through similar computational steps as employed by a standard evolutionary algorithm (EA). However, unlike traditional EAs, the DE-variants perturb the current-generation population members with the scaled differences of randomly selected and distinct population members. Therefore, no separate probability distribution has to be used for generating the offspring. Since its inception in 1995, DE has drawn the attention of many researchers all over the world resulting in a lot of variants of the basic algorithm with improved performance. This paper presents a detailed review of the basic concepts of DE and a survey of its major variants, its application to multiobjective, constrained, large scale, and uncertain optimization problems, and the theoretical studies conducted on DE so far. Also, it provides an overview of the significant engineering applications that have benefited from the powerful nature of DE.

autophagic responses. Here, we critically discuss current methods of assessing autophagy and the information they can, or cannot, provide. Our ultimate goal is to encourage intellectual and technical innovation in the field.

Cancer is an abnormal state of cells where they undergo uncontrolled proliferation and produce aggressive malignancies that causes millions of deaths every year. With the new understanding of the molecular mechanism(s) of disease progression, our knowledge about the disease is snowballing, leading to the evolution of many new therapeutic regimes and their successive trials. In the past few decades, various combinations of therapies have been proposed and are presently employed in the treatment of diverse cancers. Targeted drug therapy, immunotherapy, and personalized medicines are now largely being employed, which were not common a few years back. The field of cancer discoveries and therapeutics are evolving fast as cancer type-specific biomarkers are progressively being identified and several types of cancers are nowadays undergoing systematic therapies, extending patients' disease-free survival thereafter. Although growing evidence shows that a systematic and targeted approach could be the future of cancer medicine, chemotherapy remains a largely opted therapeutic option despite its known side effects on the patient's physical and psychological health. Chemotherapeutic agents/pharmaceuticals served a great purpose over the past few decades and have remained the frontline choice for advanced-stage malignancies where surgery and/or radiation therapy cannot be prescribed due to specific reasons. The present report succinctly reviews the existing and contemporary advancements in chemotherapy and assesses the status of the enrolled drugs/pharmaceuticals; it also comprehensively discusses the emerging role of specific/targeted therapeutic strategies that are presently being employed to achieve better clinical success/survival rate in cancer patients.

Evolutionary programming has emerged as a useful optimization tool for handling nonlinear programming problems. Various modifications to the basic method have been proposed with a view to enhance speed and robustness and these have been applied successfully on some benchmark mathematical problems. But few applications have been reported on real-world problems such as economic load dispatch (ELD). The performance of evolutionary programs on ELD problems is examined and presented in this paper in two parts. In Part I, modifications to the basic technique are proposed, where adaptation is based on scaled cost. In Part II, evolutionary programs are developed with adaptation based on an empirical learning rate. Absolute, as well as relative, performance of the algorithms are investigated on ELD problems of different size and complexity having nonconvex cost curves where conventional gradient-based methods are inapplicable.

<para xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> Differential evolution (DE) is well known as a simple and efficient scheme for global optimization over continuous spaces. It has reportedly outperformed a few evolutionary algorithms (EAs) and other search heuristics like the particle swarm optimization (PSO) when tested over both benchmark and real-world problems. DE, however, is not completely free from the problems of slow and/or premature convergence. This paper describes a family of improved variants of the DE/target-to-best/1/bin scheme, which utilizes the concept of the neighborhood of each population member. The idea of small neighborhoods, defined over the index-graph of parameter vectors, draws inspiration from the community of the PSO algorithms. The proposed schemes balance the exploration and exploitation abilities of DE without imposing serious additional burdens in terms of function evaluations. They are shown to be statistically significantly better than or at least comparable to several existing DE variants as well as a few other significant evolutionary computing techniques over a test suite of 24 benchmark functions. The paper also investigates the applications of the new DE variants to two real-life problems concerning parameter estimation for frequency modulated sound waves and spread spectrum radar poly-phase code design. </para>

Nine districts in West Bengal, India, and 42 districts in Bangladesh have arsenic levels in groundwater above the World Health Organization maximum permissible limit of 50 microg/L. The area and population of the 42 districts in Bangladesh and the 9 districts in West Bengal are 92,106 km(2) and 79.9 million and 38,865 km(2) and 42.7 million, respectively. In our preliminary study, we have identified 985 arsenic-affected villages in 69 police stations/blocks of nine arsenic-affected districts in West Bengal. In Bangladesh, we have identified 492 affected villages in 141 police stations/blocks of 42 affected districts. To date, we have collected 10,991 water samples from 42 arsenic-affected districts in Bangladesh for analysis, 58,166 water samples from nine arsenic-affected districts in West Bengal. Of the water samples that we analyzed, 59 and 34%, respectively, contained arsenic levels above 50 microg/L. Thousands of hair, nail, and urine samples from people living in arsenic-affected villages have been analyzed to date; Bangladesh and West Bengal, 93 and 77% samples, on an average, contained arsenic above the normal/toxic level. We surveyed 27 of 42 districts in Bangladesh for arsenic patients; we identified patients with arsenical skin lesions in 25 districts. In West Bengal, we identified patients with lesions in seven of nine districts. We examined people from the affected villages at random for arsenical dermatologic features (11,180 and 29,035 from Bangladesh and West Bengal, respectively); 24.47 and 15.02% of those examined, respectively, had skin lesions. After 10 years of study in West Bengal and 5 in Bangladesh, we feel that we have seen only the tip of iceberg.

This paper describes a simulated annealing based multiobjective optimization algorithm that incorporates the concept of archive in order to provide a set of tradeoff solutions for the problem under consideration. To determine the acceptance probability of a new solution vis-a-vis the current solution, an elaborate procedure is followed that takes into account the domination status of the new solution with the current solution, as well as those in the archive. A measure of the amount of domination between two solutions is also used for this purpose. A complexity analysis of the proposed algorithm is provided. An extensive comparative study of the proposed algorithm with two other existing and well-known multiobjective evolutionary algorithms (MOEAs) demonstrate the effectiveness of the former with respect to five existing performance measures, and several test problems of varying degrees of difficulty. In particular, the proposed algorithm is found to be significantly superior for many objective test problems (e.g., 4, 5, 10, and 15 objective problems), while recent studies have indicated that the Pareto ranking-based MOEAs perform poorly for such problems. In a part of the investigation, comparison of the real-coded version of the proposed algorithm is conducted with a very recent multiobjective simulated annealing algorithm, where the performance of the former is found to be generally superior to that of the latter.

Abstract This paper tries to explore the optimum variable selection strategy for Partial Least Squares (PLS) regression using a model dataset of cytoprotection data. The compounds of the dataset were classified using K ‐means clustering technique applied on standardized descriptor matrix and ten combinations of training and test sets were generated based on the obtained clusters. For a particular training set, PLS models were developed with a number of components optimized by leave‐one‐out Q 2 and then the developed models were validated (externally) using the test set compounds. For each set, PLS model was initially constructed using all descriptors (variables). The variables having least standardized values of regression coefficients were deleted and the next model was developed with a reduced set of variables. These steps were performed several times until further reduction in number of variables did not improve Q 2 value. In each case, statistical parameters like predictive R 2 ( R 2 pred ), squared correlation coefficient between observed and predicted values with ( r 2 ) and without ( $\rm{ r_0^{\rm{2}} }$ ) intercept and Root Mean Square Error of Prediction (RMSEP) were calculated from the test set compounds. In case of all ten sets, Q 2 values steadily increase on deletion of variables while R 2 pred values do not show any specific trend. In no case, the highest Q 2 and highest R 2 pred appear in the same trial, i.e. , with the same combinations of variables. This suggests that from the viewpoint of external predictability, choice of variables for PLS based on Q 2 value may not be optimum. Moreover, a clear separation of r 2 and r 0 2 curves in some sets suggests that such models may not be truly predictive in spite of acceptable R 2 pred values. Another observation is that coefficient of determination R 2 for the training set is more immune to changes on deletion of variables than the validation parameters like Q 2 and R 2 pred . Finally, a new parameter r m 2 has been suggested to indicate external predictability of QSAR models.

Abstract Background In recent years, green synthesis of silver nanoparticles (AgNPs) has gained much interest from chemists and researchers. In this concern, Indian flora has yet to divulge innumerable sources of cost-effective non-hazardous reducing and stabilizing compounds utilized in preparing AgNPs. This study investigates an efficient and sustainable route of AgNP preparation from 1 mM aqueous AgNO 3 using leaf extracts of three plants, Musa balbisiana (banana), Azadirachta indica (neem) and Ocimum tenuiflorum (black tulsi), well adorned for their wide availability and medicinal property. Methods AgNPs were prepared by the reaction of 1 mM silver nitrate and 5% leaf extract of each type of plant separately. the AgNPs were duely characterized and tested for their antibacterial activity and toxicity. Results The AgNPs were characterized by UV-visible (vis) spectrophotometer, particle size analyzer (DLS), scanning electron microscopy (SEM), transmission electron microscopy (TEM) and energy-dispersive spectroscopy (EDS). Fourier transform infrared spectrometer (FTIR) analysis was carried out to determine the nature of the capping agents in each of these leaf extracts. AgNPs obtained showed significantly higher antimicrobial activities against Escherichia coli ( E. coli ) and Bacillus sp. in comparison to both AgNO 3 and raw plant extracts. Additionally, a toxicity evaluation of these AgNP containing solutions was carried out on seeds of Moong Bean ( Vigna radiata ) and Chickpea ( Cicer arietinum ). Results showed that seeds treated with AgNP solutions exhibited better rates of germination and oxidative stress enzyme activity nearing control levels, though detailed mechanism of uptake and translocation are yet to be analyzed. Conclusion In totality, the AgNPs prepared are safe to be discharged in the environment and possibly utilized in processes of pollution remediation. AgNPs may also be efficiently utilized in agricultural research to obtain better health of crop plants as shown by our study.

Particle size dependent anti-bacterial and anti-diabetic activities of green synthesized ZnO nanoparticles.

<para xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> Differential evolution (DE) has emerged as one of the fast, robust, and efficient global search heuristics of current interest. This paper describes an application of DE to the automatic clustering of large unlabeled data sets. In contrast to most of the existing clustering techniques, the proposed algorithm requires no prior knowledge of the data to be classified. Rather, it determines the optimal number of partitions of the data “on the run.” Superiority of the new method is demonstrated by comparing it with two recently developed partitional clustering techniques and one popular hierarchical clustering algorithm. The partitional clustering algorithms are based on two powerful well-known optimization algorithms, namely the genetic algorithm and the particle swarm optimization. An interesting real-world application of the proposed method to automatic segmentation of images is also reported. </para>

The authors report a power-flow-minimum heuristic algorithm for determining the minimum loss configuration of radial distribution networks. The algorithm is based on the concept of optimum flow pattern which is determined by solving the KVL and KCL (Kirchoff's voltage and current laws) equations of the network. The optimum flow pattern of a single loop formed by closing a normally open switch is found, and the flow pattern is established in the radial network by opening a closed switch. This process is repeated until the minimum loss configuration is obtained. A simple, fast and approximate power flow method has also been developed to assist the reconfiguration algorithm. The proposed reconfiguration algorithm has been found to give better network configuration than those obtained by some other methods.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

This work investigates the role of oxidation state in the antibacterial activity of copper oxide nanoparticles (NPs).

Differential evolution (DE) is one of the most powerful stochastic real parameter optimizers of current interest. In this paper, we propose a new mutation strategy, a fitness-induced parent selection scheme for the binomial crossover of DE, and a simple but effective scheme of adapting two of its most important control parameters with an objective of achieving improved performance. The new mutation operator, which we call DE/current-to-gr_best/1, is a variant of the classical DE/current-to-best/1 scheme. It uses the best of a group (whose size is q% of the population size) of randomly selected solutions from current generation to perturb the parent (target) vector, unlike DE/current-to-best/1 that always picks the best vector of the entire population to perturb the target vector. In our modified framework of recombination, a biased parent selection scheme has been incorporated by letting each mutant undergo the usual binomial crossover with one of the p top-ranked individuals from the current population and not with the target vector with the same index as used in all variants of DE. A DE variant obtained by integrating the proposed mutation, crossover, and parameter adaptation strategies with the classical DE framework (developed in 1995) is compared with two classical and four state-of-the-art adaptive DE variants over 25 standard numerical benchmarks taken from the IEEE Congress on Evolutionary Computation 2005 competition and special session on real parameter optimization. Our comparative study indicates that the proposed schemes improve the performance of DE by a large magnitude such that it becomes capable of enjoying statistical superiority over the state-of-the-art DE variants for a wide variety of test problems. Finally, we experimentally demonstrate that, if one or more of our proposed strategies are integrated with existing powerful DE variants such as jDE and JADE, their performances can also be enhanced.

Over the past several years, treatment of infectious diseases and immunisation has undergone a revolutionary shift. With the advancement of biotechnology and genetic engineering, not only a large number of disease-specific biological have been developed, but also emphasis has been made to effectively deliver these biologicals. Niosomes are vesicles composed of non-ionic surfactants, which are biodegradable, relatively nontoxic, more stable and inexpensive, an alternative to liposomes. This article reviews the current deepening and widening of interest of niosomes in many scientific disciplines and, particularly its application in medicine. This article also presents an overview of the techniques of preparation of niosome, types of niosomes, characterisation and their applications.

The pandemic of arsenic poisoning due to contaminated groundwater in West Bengal, India, and all of Bangladesh has been thought to be limited to the Ganges Delta (the Lower Ganga Plain), despite early survey reports of arsenic contamination in groundwater in the Union Territory of Chandigarh and its surroundings in the northwestern Upper Ganga Plain and recent findings in the Terai area of Nepal. Anecdotal reports of arsenical skin lesions in villagers led us to evaluate arsenic exposure and sequelae in the Semria Ojha Patti village in the Middle Ganga Plain, Bihar, where tube wells replaced dug wells about 20 years ago. Analyses of the arsenic content of 206 tube wells (95% of the total) showed that 56.8% exceeded arsenic concentrations of 50 micro g/L, with 19.9% > 300 micro g/L, the concentration predicting overt arsenical skin lesions. On medical examination of a self-selected sample of 550 (390 adults and 160 children), 13% of the adults and 6.3% of the children had typical skin lesions, an unusually high involvement for children, except in extreme exposures combined with malnutrition. The urine, hair, and nail concentrations of arsenic correlated significantly (r = 0.72-0.77) with drinking water arsenic concentrations up to 1,654 micro g/L. On neurologic examination, arsenic-typical neuropathy was diagnosed in 63% of the adults, a prevalence previously seen only in severe, subacute exposures. We also observed an apparent increase in fetal loss and premature delivery in the women with the highest concentrations of arsenic in their drinking water. The possibility of contaminated groundwater at other sites in the Middle and Upper Ganga Plain merits investigation.

Drug delivery technology has a wide spectrum, which is continuously being upgraded at a stupendous speed. Different fabricated nanoparticles and drugs possessing low solubility and poor pharmacokinetic profiles are the two major substances extensively delivered to target sites. Among the colloidal carriers, nanolipid dispersions (liposomes, deformable liposomes, virosomes, ethosomes, and solid lipid nanoparticles) are ideal delivery systems with the advantages of biodegradation and nontoxicity. Among them, nano-structured lipid carriers and solid lipid nanoparticles (SLNs) are dominant, which can be modified to exhibit various advantages, compared to liposomes and polymeric nanoparticles. Nano-structured lipid carriers and SLNs are non-biotoxic since they are biodegradable. Besides, they are highly stable. Their (nano-structured lipid carriers and SLNs) morphology, structural characteristics, ingredients used for preparation, techniques for their production, and characterization using various methods are discussed in this review. Also, although nano-structured lipid carriers and SLNs are based on lipids and surfactants, the effect of these two matrixes to build excipients is also discussed together with their pharmacological significance with novel theranostic approaches, stability and storage.

Validation is a crucial aspect of quantitative structure-activity relationship (QSAR) modeling. The present paper shows that traditionally used validation parameters (leave-one-out Q(2) for internal validation and predictive R(2) for external validation) may be supplemented with two novel parameters r(m)(2) and R(p)(2) for a stricter test of validation. The parameter r(m)(2)((overall)) penalizes a model for large differences between observed and predicted values of the compounds of the whole set (considering both training and test sets) while the parameter R(p)(2) penalizes model R(2) for large differences between determination coefficient of nonrandom model and square of mean correlation coefficient of random models in case of a randomization test. Two other variants of r(m)(2) parameter, r(m)(2)((LOO)) and r(m)(2)((test)), penalize a model more strictly than Q(2) and R(2)(pred) respectively. Three different data sets of moderate to large size have been used to develop multiple models in order to indicate the suitability of the novel parameters in QSAR studies. The results show that in many cases the developed models could satisfy the requirements of conventional parameters (Q(2) and R(2)(pred)) but fail to achieve the required values for the novel parameters r(m)(2) and R(p)(2). Moreover, these parameters also help in identifying the best models from among a set of comparable models. Thus, a test for these two parameters is suggested to be a more stringent requirement than the traditional validation parameters to decide acceptability of a predictive QSAR model, especially when a regulatory decision is involved.

Considering that Coulomb interactions contribute a negligible amount to the cohesive energy in ${\mathrm{C}}_{60}$, which has an fcc structure at room temperature, we used Girifalco potential function in our calculations; ${\mathrm{C}}_{60}$ is spherical in nature and rotates rather freely at room temperature. From this potential we evaluated the second-order elastic constants (SOEC's), their pressure derivatives, and the third-order elastic constants (TOEC's). The SOEC's are found to be in good agreement with other literature values reported so far. The pressure derivative of bulk modulus is found to be 3.8 which compares favorably with Duclos values while the theoretical literature value is reported to be high. The pressure derivatives of the SOEC's are found to be high compared with those of glassy, amorphous materials, alloys, and metals. Furthermore, the derivatives are all found to be positive. The TOEC's are all found to be negative resembling those of polymers like polystyrene. We evaluated the thermal Gruneisen's constant from the potential function and the value obtained is in excellent agreement with experiment.