NeSiFC: Neighbors' Similarity-Based Fuzzy Community Detection Using Modified Local Random Walk.

This article proposes a neighbors' similarity-based fuzzy community detection (FCD) method, which we call "NeSiFC." In the proposed NeSiFC approach, we compute the similarity between two neighbors by introducing a modified local random walk (mLRW). Basically, in a network, a node and its' neighbors with noticeable similarities among them construct a community. To measure this similarity, we introduce a new metric, called the peripheral similarity index (PSI). This PSI is used to construct the transition probability matrix for the mLRW. The mLRW is applied for each node until it meets a parameter called step coefficient. The mLRW gives better neighbors' similarity for community detection. Finally, a fuzzy membership function is used iteratively to compute the membership degrees for all nodes with reference to existing communities. The proposed NeSiFC has no dependence on the network characteristics, and no adjustment or fine tuning of more than one parameter is needed. To show the efficacy of the proposed NeSiFC approach, we provide a thorough comparative performance analysis considering a set of well-known FCD algorithms viz., the genetic algorithm for fuzzy community detection, membership degree propagation, center-based fuzzy graph clustering, FMM/H2, and FuzAg on a set of popular benchmarks, as well as real-world datasets. For both disjoint and overlapping community structures, results of various accuracy and quality metrics indicate the outstanding performance of our proposed NeSiFC approach. The asymptotic complexity of the proposed NeSiFC is found as O(n2).

Tuning Dunaliella tertiolecta for Enhanced Antioxidant Production by Modification of Culture Conditions.

Microalgae, a popular source of food and bioactive compounds, accumulate antioxidants in response to culture condition stresses. Using a factorial design (3 × 3), the effect of light, temperature, and nitrogen level on chlorophyll and carotenoids, total protein, total phenolic, ascorbate and glutathione content, and enzyme (catalase (CAT), superoxide dismutase (SOD), and peroxidase (POD)) activities in Dunaliella tertiolecta was studied. Data were analysed using Design of Experiments (DoE), and recommendations are made for optimum cultivation conditions to achieve the highest antioxidant content (phenolics, ascorbate and glutathione) or enzyme (CAT, SOD, and POD) activities. This is the first study to apply three levels of three factors during cultivation to tune Dunaliella tertiolecta for optimal antioxidant production.

Purification of lectin and Kunitz trypsin inhibitor from soya seeds.

The search for potent and selective therapeutic agents is progressing by the study of natural compounds in plants. Plant-derived macromolecules are considered emerging therapeutic agents and an alternative to synthetic and small molecule drugs. Where it has long been known that plants possess medicinal properties, the compounds responsible for their action are in many cases still unknown: often only whole crude plant extracts or fractionated extracts are tested for the ability to inhibit common pathogens. Here, we present a fast protein liquid chromatography method for the separation of crude plant proteins. Kunitz trypsin inhibitor (KTI; 24.2 kDa) and lectin (31 kDa) were purified from Glycine max by liquid extraction followed by ion exchange column chromatography. The need for serial chromatographic separation steps has been eliminated by introducing more complex elution profiles hence reducing cost, time and improving recovery. The identity of KTI-A and lectin was confirmed by matrix assisted laser desorption ionization time of flight mass spectrometry (MALDI-ToF MS). Cell proliferation assays using B16F1 melanoma cells revealed that both KTI and the monomeric lectin retained some antiproliferative activity. This method could be useful for rapid and cost-effective purification of bioactive compounds from plant material.