A new approach based on the analysis of the influence of a single perturbation is proposed as a test for the shadowing property for a broad class of dynamical systems (in particular, nonautonomous ...ones) under a variety of perturbations. Applications to several interesting cases are considered in detail.
With their ability of CO2 fixation using sunlight as an energy source, algae and especially microalgae are moving into the focus for the production of proteins and other valuable compounds. However, ...the valorization of algal biomass depends on the effective disruption of the recalcitrant microalgal cell wall. Especially cell walls of Chlorella species proved to be very robust. The wall structures that are responsible for this robustness have been studied less so far. Here, we evaluate different common methods to break up the algal cell wall effectively and measure the success by protein and carbohydrate release. Subsequently, we investigate algal cell wall features playing a role in the wall's recalcitrance towards disruption. Using different mechanical and chemical technologies, alkali catalyzed hydrolysis of the Chlorella vulgaris cells proved to be especially effective in solubilizing up to 56 wt% protein and 14 wt% carbohydrates of the total biomass. The stepwise degradation of C. vulgaris cell walls using a series of chemicals with increasingly strong conditions revealed that each fraction released different ratios of proteins and carbohydrates. A detailed analysis of the monosaccharide composition of the cell wall extracted in each step identified possible factors for the robustness of the cell wall. In particular, the presence of chitin or chitin-like polymers was indicated by glucosamine found in strong alkali extracts. The presence of highly ordered starch or cellulose was indicated by glucose detected in strong acidic extracts. Our results might help to tailor more specific efforts to disrupt Chlorella cell walls and help to valorize microalgae biomass.
Plant natural products are a seemingly endless resource for novel chemical structures. However, their extraction often results in high prices, fluctuation in both quantity and quality, and negative ...environmental impact. The latter might result from the extraction procedure but more often from the high amount of plant biomass required. With the advent of synthetic biology, producing natural plant products in large quantities using yeasts as hosts has become possible. Here, we focus on the recent advances in metabolic engineering of the yeasts species Saccharomyces cerevisiae and Yarrowia lipolytica for the synthesis of ginsenoside triterpenoids, namely, dammarenediol-II, protopanaxadiol, protopanaxatriol, compound K, ginsenoside Rh1, ginsenoside Rh2, ginsenoside Rg3, and ginsenoside F1. A discussion is provided on advanced synthetic biology, bioprocess strategies, and current challenges for the biosynthesis of ginsenoside triterpenoids. Finally, future directions in metabolic and process engineering are summarized and may help reify sustainable ginsenoside production.
Despite the apparent partisan divide over issues such as global warming and hydraulic fracturing, little is known about what shapes citizens' willingness to accept scientific recommendations on ...political issues. We examine the extent to which Democrats, Republicans, and independents are likely to defer to scientific expertise in matters of policy. Our study draws on an October 2013 U.S. national survey of 2,000 respondents. We find that partisan differences exist: our data show that most Americans see science as relevant to policy, but that their willingness to defer to science in policy matters varies considerably across issues. While party, ideology, and religious beliefs clearly influence attitudes toward science, Republicans are not notably skeptical about accepting scientific recommendations. Rather, it seems that Democrats are particularly receptive to the advice and counsel of scientists, when compared to both independents and Republicans.
Over 359 million tons of plastics were produced worldwide in 2018, with significant growth expected in the near future, resulting in the global challenge of end-of-life management. The recent ...identification of enzymes that degrade plastics previously considered non-biodegradable opens up opportunities to steer the plastic recycling industry into the realm of biotechnology.
Here, the sequential conversion of post-consumer polyethylene terephthalate (PET) into two types of bioplastics is presented: a medium chain-length polyhydroxyalkanoate (PHA) and a novel bio-based poly(amide urethane) (bio-PU). PET films are hydrolyzed by a thermostable polyester hydrolase yielding highly pure terephthalate and ethylene glycol. The obtained hydrolysate is used directly as a feedstock for a terephthalate-degrading Pseudomonas umsongensis GO16, also evolved to efficiently metabolize ethylene glycol, to produce PHA. The strain is further modified to secrete hydroxyalkanoyloxy-alkanoates (HAAs), which are used as monomers for the chemo-catalytic synthesis of bio-PU. In short, a novel value-chain for PET upcycling is shown that circumvents the costly purification of PET monomers, adding technological flexibility to the global challenge of end-of-life management of plastics.
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•Tandem enzymatic hydrolysis and microbial conversion of PET.•Complete enzymatic hydrolysis of post-consumer PET.•ALE for enhanced ethylene glycol metabolization.•Biochemical synthesis of a novel bio-based poly(amide urethane).
The metabolome of an organism depends on environmental factors and intracellular regulation and provides information about the physiological conditions. Metabolomics helps to understand disease ...progression in clinical settings or estimate metabolite overproduction for metabolic engineering. The most popular analytical metabolomics platform is mass spectrometry (MS). However, MS metabolome data analysis is complicated, since metabolites interact nonlinearly, and the data structures themselves are complex. Machine learning methods have become immensely popular for statistical analysis due to the inherent nonlinear data representation and the ability to process large and heterogeneous data rapidly. In this review, we address recent developments in using machine learning for processing MS spectra and show how machine learning generates new biological insights. In particular, supervised machine learning has great potential in metabolomics research because of the ability to supply quantitative predictions. We review here commonly used tools, such as random forest, support vector machines, artificial neural networks, and genetic algorithms. During processing steps, the supervised machine learning methods help peak picking, normalization, and missing data imputation. For knowledge-driven analysis, machine learning contributes to biomarker detection, classification and regression, biochemical pathway identification, and carbon flux determination. Of important relevance is the combination of different omics data to identify the contributions of the various regulatory levels. Our overview of the recent publications also highlights that data quality determines analysis quality, but also adds to the challenge of choosing the right model for the data. Machine learning methods applied to MS-based metabolomics ease data analysis and can support clinical decisions, guide metabolic engineering, and stimulate fundamental biological discoveries.
The U.S. welfare reforms of the 1990s have generated extensive interest. Both the federal changes in work support programs like the EITC and the revolution in the design of state public assistance ...programs have drawn research attention. While it is far too early to draw any final conclusions about the long-term effects of these program changes, the research literature to date has produced several important results. More significant caseload declines and larger increases in labor force participation among less-skilled mothers occurred than many observers would have predicted. Entry into welfare fell, and exits from welfare rose. There remains debate as to how much these results were due to a strong economy, to program reform, or to their interactive effects. While some of this change in behavior is due to traditional labor supply responses to growing wages and increased financial incentives to work, the changes were greater than historical experience would lead one to expect.
Actions as Space-Time Shapes Gorelick, L.; Blank, M.; Shechtman, E. ...
IEEE transactions on pattern analysis and machine intelligence,
12/2007, Volume:
29, Issue:
12
Journal Article
Peer reviewed
Human action in video sequences can be seen as silhouettes of a moving torso and protruding limbs undergoing articulated motion. We regard human actions as three-dimensional shapes induced by the ...silhouettes in the space-time volume. We adopt a recent approach 14 for analyzing 2D shapes and generalize it to deal with volumetric space-time action shapes. Our method utilizes properties of the solution to the Poisson equation to extract space-time features such as local space-time saliency, action dynamics, shape structure, and orientation. We show that these features are useful for action recognition, detection, and clustering. The method is fast, does not require video alignment, and is applicable in (but not limited to) many scenarios where the background is known. Moreover, we demonstrate the robustness of our method to partial occlusions, nonrigid deformations, significant changes in scale and viewpoint, high irregularities in the performance of an action, and low-quality video.
Atypical teratoid/rhabdoid tumor is a rare malignant CNS tumor that most often affects children ≤ 3 years old. The Central Brain Tumor Registry of the United States contains the largest aggregation ...of population-based incidence data for primary CNS tumors in the US. Its data were used to describe the incidence, associated trends, and relative survival after diagnosis of atypical teratoid/rhabdoid tumor.
Using data from 50 cancer registries between 2001 and 2010, age-adjusted incidence rates per 100 000 and 95% CIs were calculated by sex, race, Hispanic ethnicity, age at diagnosis, and location of tumor in the CNS for children aged 0 to 19 years. Relative survival rates and 95% CIs were also calculated.
The average annual age-adjusted incidence rate was 0.07 (95% CI: 0.07, 0.08). Incidence rates did not significantly vary by sex, race, or ethnicity. Age had a strong effect on incidence rate, with highest incidence among children <1 year, and decreasing incidence with increasing age. The 6-month, 1-year, and 5-year relative survival rates for all ages were 65.0%, 46.8%, and 28.3%, respectively. Atypical teratoid/rhabdoid tumor can occur anywhere in the CNS, but supratentorial tumors were more common with increasing age.
We confirm differences in survival by age at diagnosis, treatment pattern, and location of tumor in the brain. This contributes to our understanding of these tumors and may stimulate research leading to improved treatment of this devastating childhood disease.
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