Xanthohumol for Human Malignancies: Chemistry,Pharmacokinetics and Molecular Targets

Xanthohumol (XH) is an important prenylated flavonoid that is found within the inflorescence of Humulus lupulus L. (Hop plant). XH is an important ingredient in beer and is considered a significant bioactive agent due to its diverse medicinal applications, which include anti-inflammatory, antimicrobial, antioxidant, immunomodulatory, antiviral, antifungal, antigenotoxic, antiangiogenic, and antimalarial effects as well as strong anticancer activity towards various types of cancer cells. XH acts as a wide ranging chemopreventive and anticancer agent, and its isomer, 8-prenylnaringenin, is a phytoestrogen with strong estrogenic activity. The present review focuses on the bioactivity of XH on various types of cancers and its pharmacokinetics. In this paper, we first highlight, in brief, the history and use of hops and then the chemistry and structure–activity relationship of XH. Lastly, we focus on its prominent effects and mechanisms of action on various cancers and its possible use in cancer prevention and treatment. Considering the limited number of available reviews on this subject, our goal is to provide a complete and detailed understanding of the anticancer effects of XH against different cancers.     

Correction to: EDVWDD: Event‑Driven Virtual Wheel‑based Data Dissemination for Mobile Sink‑Enabled Wireless Sensor Networks

The Journal of Supercomputing - A correction to this paper has been published: https://doi.org/10.1007/s11227-021-03790-9     

Divergence of Intracellular Trafficking of Sphingosine Kinase 1 and Sphingosine-1-Phosphate Receptor 3 in MCF-7 Breast Cancer Cells and MCF-7-Derived Stem Cell-Enriched Mammospheres

Breast cancer MCF-7 cell-line-derived mammospheres were shown to be enriched in cells with a CD44+/CD24– surface profile, consistent with breast cancer stem cells (BCSC). These BCSC were previously reported to express key sphingolipid signaling effectors, including pro-oncogenic sphingosine kinase 1 (SphK1) and sphingosine-1-phosphate receptor 3 (S1P3). In this study, we explored intracellular trafficking and localization of SphK1 and S1P3 in parental MCF-7 cells, and MCF-7 derived BCSC-enriched mammospheres treated with growth- or apoptosis-stimulating agents. Intracellular trafficking and localization were assessed using confocal microscopy and cell fractionation, while CD44+/CD24- marker status was confirmed by flow cytometry. Mammospheres expressed significantly higher levels of S1P3 compared to parental MCF-7 cells (p < 0.01). Growth-promoting agents (S1P and estrogen) induced SphK1 and S1P3 translocation from cytoplasm to nuclei, which may facilitate the involvement of SphK1 and S1P3 in gene regulation. In contrast, pro-apoptotic cytokine tumor necrosis factor α (TNFα)-treated MCF-7 cells demonstrated increased apoptosis and no nuclear localization of SphK1 and S1P3, suggesting that TNFα can inhibit nuclear translocation of SphK1 and S1P3. TNFα inhibited mammosphere formation and induced S1P3 internalization and degradation. No nuclear translocation of S1P3 was detected in TNFα-stimulated mammospheres. Notably, SphK1 and S1P3 expression and localization were highly heterogenous in mammospheres, suggesting the potential for a large variety of responses. The findings provide further insights into the understanding of sphingolipid signaling and intracellular trafficking in BCs. Our data indicates that the inhibition of SphK1 and S1P3 nuclear translocation represents a novel method to prevent BCSCs proliferation.     

Nearly-Linear Work Parallel SDD Solvers,Low-Diameter Decomposition,and Low-Stretch Subgraphs

We present the design and analysis of a nearly-linear work parallel algorithm for solving symmetric diagonally dominant (SDD) linear systems. On input an SDD n-by-n matrix A with m nonzero entries and a vector b, our algorithm computes a vector \(\tilde{x}\) such that \(\|\tilde{x} - A^{+}b\|_{A} \leq\varepsilon\cdot\|{A^{+}b}\|_{A}\) in \(O(m\log^{O(1)}{n}\log {\frac{1}{\varepsilon}})\) work and \(O(m^{1/3+\theta}\log\frac{1}{\varepsilon})\) depth for any θ>0, where A ⁺ denotes the Moore-Penrose pseudoinverse of A. The algorithm relies on a parallel algorithm for generating low-stretch spanning trees or spanning subgraphs. To this end, we first develop a parallel decomposition algorithm that in O(mlog ^O(1) n) work and polylogarithmic depth, partitions a graph with n nodes and m edges into components with polylogarithmic diameter such that only a small fraction of the original edges are between the components. This can be used to generate low-stretch spanning trees with average stretch O(n ^α ) in O(mlog ^O(1) n) work and O(n ^α ) depth for any α>0. Alternatively, it can be used to generate spanning subgraphs with polylogarithmic average stretch in O(mlog ^O(1) n) work and polylogarithmic depth. We apply this subgraph construction to derive a parallel linear solver. By using this solver in known applications, our results imply improved parallel randomized algorithms for several problems, including single-source shortest paths, maximum flow, minimum-cost flow, and approximate maximum flow.     

Enhancing diamond drilling performance by the addition of non-ionic polymer to the flushing media

Drilling is a most important and crucial operation in the excavation industries.With the objective of looking into the enhancement of diamond drilling performance detailed laboratory investigations were carried out on phosphate rock.The effect of Poly (Ethylene Oxide) (PEO) added to the drilling water was studied by varying machine parameters and PEO concentration.The responses were rate of penetration and torque at the bit rock interface.Slake durability tests were also performed to understand the slaking behavior of phosphate rock in PEO solutions.     

Design by framework completion

An object-oriented framework in essence defines an architecture for a family of applications or subsystems in a given domain. Every application in the family obeys these architectural restrictions. Such frameworks are typically delivered as collections of inter-dependent abstract classes, together with their concrete subclasses. The abstract classes and their interdependencies implicitly realize the architecture. Developing a new application reusing classes of a framework requires a thorough understanding of the framework architecture.We introduce an approach called Design by Framework Completion, in which an exemplar (an executable visual model for a minimal instantiation of the architecture) is used for documenting frameworks. We propose exploration of exemplars as a means for learning the architecture, following which new applications can be built by replacing selected pieces of the exemplar. For the piece to be replaced, the inheritance lattice around its class provides the space of alternatives, one of these classes may be suitably adapted (say, by sub-classing) to create the new replacement.Design by Framework Completion proposes a paradigm shift when designing in presence of reusable components: It enables a much simpler top-down approach for creating applications, as opposed to the prevalent search for components and assemble them bottom-up strategy. We believe that this paradigm shift is essential because components can only be fitted together if they all obey the same architectural rules that govern the framework.     

On the lower bounds of the second order nonlinearities of some Boolean functions

The rth order nonlinearity of a Boolean function is an important cryptographic criterion in analyzing the security of stream as well as block ciphers. It is also important in coding theory as it is related to the covering radius of the Reed-Muller code R(r,n). In this paper we deduce the lower bounds of the second order nonlinearities of the following two types of Boolean functions:

1.

with d=2^2r+2^r+1 and , where n=6r.

2.

, where x,y∈F_2^t,n=2t,n?6 and i is an integer such that 1?i<t,gcd(2^t-1,2ⁱ+1)=1.

For some λ, the functions of the first type are bent functions, whereas Boolean functions of the second type are all bent functions, i.e., they possess the maximum first order nonlinearity. It is demonstrated that in some cases our bounds are better than the previously obtained bounds.     

Fusion of probabilistic A* algorithm and fuzzy inference system for robotic path planning

Robotic Path planning is one of the most studied problems in the field of robotics. The problem has been solved using numerous statistical, soft computing and other approaches. In this paper we solve the problem of robotic path planning using a combination of A* algorithm and Fuzzy Inference. The A* algorithm does the higher level planning by working on a lower detail map. The algorithm finds the shortest path at the same time generating the result in a finite time. The A* algorithm is used on a probability based map. The lower level planning is done by the Fuzzy Inference System (FIS). The FIS works on the detailed graph where the occurrence of obstacles is precisely known. The FIS generates smoother paths catering to the non-holonomic constraints. The results of A* algorithm serve as a guide for FIS planner. The FIS system was initially generated using heuristic rules. Once this model was ready, the fuzzy parameters were optimized using a Genetic Algorithm. Three sample problems were created and the quality of solutions generated by FIS was used as the fitness function of the GA. The GA tried to optimize the distance from the closest obstacle, total path length and the sharpest turn at any time in the journey of the robot. The resulting FIS was easily able to plan the path of the robot. We tested the algorithm on various complex and simple paths. All paths generated were optimal in terms of path length and smoothness. The robot was easily able to escape a variety of obstacles and reach the goal in an optimal manner.