The thermal degradation behaviour of a series of siloxane copolymers - a study by thermal volatilisation analysis

The thermal degradation behaviour of novel high number average molecular mass polysilalkylenesiloxanes is reported. These have been synthesised using anionic ring-opening polymerisation of 1,1,3,3,14,14,16,16-octamethyl-2,15-dioxa-1,3,14,16-tetrasilacyclohexacosane and octamethylcyclotetrasiloxane (D4) mixtures. The thermal degradation behaviour of these materials was evaluated by a combination of thermogravimetric analysis (TGA) and thermal volatilisation analysis (TVA) and compared with a commercial sample of PDMS. The results demonstrated that the thermal degradation of the polysilalkylenesiloxanes is more complex than the PDMS, with the polysilalkylenesiloxanes exhibiting a lower degradation peak maximum temperature. The major volatile degradation products evolved from the PDMS were identified as D3 to D6 cyclic siloxane oligomers, in addition to higher molecular mass cyclic siloxane oligomers. The polysilalkylenesiloxanes, on the other hand, evolved short chain aliphatic hydrocarbons, cyclic and linear siloxane oligomers and silanes. The TVA results indicate that the polysilalkylenesiloxanes degrade mostly by random chain scission of the polymer backbone, whereas the commercial PDMS degrades by the accepted depolymerisation reaction which involves “back-biting” reactions

Proteasome Lid Bridges Mitochondrial Stress with Cdc53/Cullin1 NEDDylation Status

Cycles of Cdc53/Cullin1 rubylation (a.k.a NEDDylation) protect ubiquitin-E3 SCF (Skp1-Cullin1-F-box protein) complexes from self-destruction and play an important role in mediating the ubiquitination of key protein substrates involved in cell cycle progression, development, and survival. Cul1 rubylation is balanced by the COP9 signalosome (CSN), a multi-subunit derubylase that shows 1:1 paralogy to the 26 S proteasome lid. The turnover of SCF substrates and their relevance to various diseases is well studied, yet, the extent by which environmental perturbations influence Cul1 rubylation/derubylation cycles per se is still unclear. In this study, we show that the level of cellular oxidation serves as a molecular switch, determining Cullin1 rubylation/derubylation ratio. We describe a mutant of the proteasome lid subunit, Rpn11 that exhibits accumulated levels of Cullin1-Rub1 conjugates, a characteristic phenotype of csn mutants. By dissecting between distinct phenotypes of rpn11 mutants, proteasome and mitochondria dysfunction, we were able to recognize the high reactive oxygen species (ROS) production during the transition of cells into mitochondrial respiration, as a checkpoint of Cullin1 rubylation in a reversible manner. Thus, the study adds the rubylation cascade to the list of cellular pathways regulated by redox homeostasis

Chronic pelvic pain in endometriosis: from therapeutic Abstraction to clinical definition: A review

Currently, endometriosis is one of the most common and relevant gynecological conditions. It is characterized by a wide range of symptoms, with highly heterogeneous pain being one of the most typical. The unifying concept of the algological patterns of endometriosis is chronic pelvic pain, which contributes to a pronounced decrease in patients' quality of life. Therefore, it is necessary to analyze the key pathogenetic mechanisms of pelvic pain associated with endometriosis and to determine a personalized therapeutic approach based on them

Pere Alberch's developmental morphospaces and the evolution of cognition

In this article we argue for an extension of Pere Alberch's notion of developmental morphospace into the realm of cognition and introduce the notion of cognitive phenotype as a new tool for the evolutionary and developmental study of cognitive abilities

Minimization and management of wastes from biomedical research.

Several committees were established by the National Association of Physicians for the Environment to investigate and report on various topics at the National Leadership Conference on Biomedical Research and the Environment held at the 1--2 November 1999 at the National Institutes of Health in Bethesda, Maryland. This is the report of the Committee on Minimization and Management of Wastes from Biomedical Research. Biomedical research facilities contribute a small fraction of the total amount of wastes generated in the United States, and the rate of generation appears to be decreasing. Significant reductions in generation of hazardous, radioactive, and mixed wastes have recently been reported, even at facilities with rapidly expanding research programs. Changes in the focus of research, improvements in laboratory techniques, and greater emphasis on waste minimization (volume and toxicity reduction) explain the declining trend in generation. The potential for uncontrolled releases of wastes from biomedical research facilities and adverse impacts on the general environment from these wastes appears to be low. Wastes are subject to numerous regulatory requirements and are contained and managed in a manner protective of the environment. Most biohazardous agents, chemicals, and radionuclides that find significant use in research are not likely to be persistent, bioaccumulative, or toxic if they are released. Today, the primary motivations for the ongoing efforts by facilities to improve minimization and management of wastes are regulatory compliance and avoidance of the high disposal costs and liabilities associated with generation of regulated wastes. The committee concluded that there was no evidence suggesting that the anticipated increases in biomedical research will significantly increase generation of hazardous wastes or have adverse impacts on the general environment. This conclusion assumes the positive, countervailing trends of enhanced pollution prevention efforts by facilities and reductions in waste generation resulting from improvements in research methods will continue

Languages as hyperplanes: grammatical inference with string kernels

Using string kernels, languages can be represented as hyperplanes in a high dimensional feature space. We present a new family of grammatical inference algorithms based on this idea. We demonstrate that some mildly context sensitive languages can be represented in this way and it is possible to efficiently learn these using kernel PCA. We present some experiments demonstrating the effectiveness of this approach on some standard examples of context sensitive languages using small synthetic data sets