Dynamics of Solitons and Quasisolitons of Cubic Third-Order Nonlinear Schr\"odinger Equation

The dynamics of soliton and quasisoliton solutions of cubic third order nonlinear Schr\"{o}dinger equation is studied. The regular solitons exist due to a balance between the nonlinear terms and (linear) third order dispersion; they are not important at small $\alpha_3$ ($\alpha_3$ is the coefficient in the third derivative term) and vanish at $\alpha_3 \to 0$. The most essential, at small $\alpha_3$, is a quasisoliton emitting resonant radiation (resonantly radiating soliton). Its relationship with the other (steady) quasisoliton, called embedded soliton, is studied analytically and in numerical experiments. It is demonstrated that the resonantly radiating solitons emerge in the course of nonlinear evolution, which shows their physical significance

A phenomenological approach to the simulation of metabolism and proliferation dynamics of large tumour cell populations

A major goal of modern computational biology is to simulate the collective behaviour of large cell populations starting from the intricate web of molecular interactions occurring at the microscopic level. In this paper we describe a simplified model of cell metabolism, growth and proliferation, suitable for inclusion in a multicell simulator, now under development (Chignola R and Milotti E 2004 Physica A 338 261-6). Nutrients regulate the proliferation dynamics of tumor cells which adapt their behaviour to respond to changes in the biochemical composition of the environment. This modeling of nutrient metabolism and cell cycle at a mesoscopic scale level leads to a continuous flow of information between the two disparate spatiotemporal scales of molecular and cellular dynamics that can be simulated with modern computers and tested experimentally.Comment: 58 pages, 7 figures, 3 tables, pdf onl

A gene expression signature distinguishes innate response and resistance to proteasome inhibitors in multiple myeloma

Extensive interindividual variation in response to chemotherapy is a major stumbling block in achieving desirable efficacy in the treatment of cancers, including multiple myeloma (MM). In this study, our goal was to develop a gene expression signature that predicts response specific to proteasome inhibitor (PI) treatment in MM. Using a well-characterized panel of human myeloma cell lines (HMCLs) representing the biological and genetic heterogeneity of MM, we created an in vitro chemosensitivity profile in response to treatment with the four PIs bortezomib, carfilzomib, ixazomib and oprozomib as single agents. Gene expression profiling was performed using next-generation high-throughput RNA-sequencing. Applying machine learning-based computational approaches including the supervised ensemble learning methods Random forest and Random survival forest, we identified a 42-gene expression signature that could not only distinguish good and poor PI response in the HMCL panel, but could also be successfully applied to four different clinical data sets on MM patients undergoing PI-based chemotherapy to distinguish between extraordinary (good and poor) outcomes. Our results demonstrate the use of in vitro modeling and machine learning-based approaches to establish predictive biomarkers of response and resistance to drugs that may serve to better direct myeloma patient treatment options

Pulmonary delivery of vancomycin dry powder aerosol to intubated rabbits

TGX-221 is a potent, selective, and cell membrane permeable inhibitor of the PI3K p110β catalytic subunit. Recent studies showed that TGX-221 has anti-proliferative activity against PTEN-deficient tumor cell lines including prostate cancers. The objective of this study was to develop an encapsulation system for parenterally delivering TGX-221 to the target tissue through a prostate-specific membrane aptamer (PSMAa10) with little or no side effects. In this study, PEG-PCL micelles were formulated to encapsulate the drug, and a prodrug strategy was pursued to improve the stability of the carrier system. Fluorescence imaging studies demonstrated that the cellular uptake of both drug and nanoparticles were significantly improved by targeted micelles in a PSMA positive cell line. The area under the plasma concentration time curve of the micelle formulation in nude mice was 2.27-fold greater than the naked drug, and the drug clearance rate was 17.5-fold slower. These findings suggest a novel formulation approach for improving site-specific drug delivery of a molecular-targeted prostate cancer treatment

Synthesis of Oleoylethanolamide Using Lipase

An effective process for the enzymatic synthesis of oleoylethanolamide is described in this study. The process included purification of a commercial oleic acid product and then optimization of the reaction between the purified oleic acid and ethanolamine in the presence of hexane and a lipase. Under the optimal amidation reaction conditions identified, oleoylethanolamide was obtained with 96.6% purity. The synthesis was also conducted on a large scale (50 mmol of each of the reactants), and oleoylethanolamide purity and yield after crystallization purification were 96.1 and 73.5%, respectively. Compared to the previous studies, the current method of preparing high-purity oleoylethanolamide is more effective and economically feasible. The scalability and ease for such synthesis make it possible to study the biological and nutritional functions of the cannabinoid-like oleoylethanolamide in animal or human subjects

Towards a multisensor station for automated biodiversity monitoring

Rapid changes of the biosphere observed in recent years are caused by both small and large scale drivers, like shifts in temperature, transformations in land-use, or changes in the energy budget of systems. While the latter processes are easily quantifiable, documentation of the loss of biodiversity and community structure is more difficult. Changes in organismal abundance and diversity are barely documented. Censuses of species are usually fragmentary and inferred by often spatially, temporally and ecologically unsatisfactory simple species lists for individual study sites. Thus, detrimental global processes and their drivers often remain unrevealed. A major impediment to monitoring species diversity is the lack of human taxonomic expertise that is implicitly required for large-scale and fine-grained assessments. Another is the large amount of personnel and associated costs needed to cover large scales, or the inaccessibility of remote but nonetheless affected areas. To overcome these limitations we propose a network of Automated Multisensor stations for Monitoring of species Diversity (AMMODs) to pave the way for a new generation of biodiversity assessment centers. This network combines cutting-edge technologies with biodiversity informatics and expert systems that conserve expert knowledge. Each AMMOD station combines autonomous samplers for insects, pollen and spores, audio recorders for vocalizing animals, sensors for volatile organic compounds emitted by plants (pVOCs) and camera traps for mammals and small invertebrates. AMMODs are largely self-containing and have the ability to pre-process data (e.g. for noise filtering) prior to transmission to receiver stations for storage, integration and analyses. Installation on sites that are difficult to access require a sophisticated and challenging system design with optimum balance between power requirements, bandwidth for data transmission, required service, and operation under all environmental conditions for years. An important prerequisite for automated species identification are databases of DNA barcodes, animal sounds, for pVOCs, and images used as training data for automated species identification. AMMOD stations thus become a key component to advance the field of biodiversity monitoring for research and policy by delivering biodiversity data at an unprecedented spatial and temporal resolution. (C) 2022 Published by Elsevier GmbH on behalf of Gesellschaft fur Okologie