Carbon nanotube chirality determines properties of encapsulated linear carbon chain

Long linear carbon chains encapsulated inside carbon nanotubes are a very close realization of carbyne, the truly one-dimensional allotrope of carbon. Here we study individual pairs of double-walled carbon nanotubes and encapsulated linear carbon chains by tip-enhanced Raman scattering. We observe that the radial breathing mode of the inner nanotube correlates with the frequency of the carbon chain's Raman mode, revealing that the nanotube chirality determines the vibronic and electronic properties of the encapsulated carbon chain. We provide the missing link that connects the properties of the encapsulated long linear carbon chain with the structure of the host nanotube.Comment: keywords: linear carbon chains; carbyne; carbon nanotubes; tip-enhanced Raman scattering; TERS; Significant changes compared to first version of the manuscript. Current version includes Supporting Informatio

Triplet-singlet conversion in ultracold Cs2_2 and production of ground state molecules

We propose a process to convert ultracold metastable Cs$_2$ molecules in their lowest triplet state into (singlet) ground state molecules in their lowest vibrational levels. Molecules are first pumped into an excited triplet state, and the triplet-singlet conversion is facilitated by a two-step spontaneous decay through the coupled $A^{1}\Sigma_{u}^{+} \sim b ^{3}\Pi_{u}$ states. Using spectroscopic data and accurate quantum chemistry calculations for Cs$_2$ potential curves and transition dipole moments, we show that this process has a high rate and competes favorably with the single-photon decay back to the lowest triplet state. In addition, we demonstrate that this conversion process represents a loss channel for vibrational cooling of metastable triplet molecules, preventing an efficient optical pumping cycle down to low vibrational levels

Highly specific PCR-RFLP assays for karyotyping the widespread 2Rb inversion in malaria vectors of the Anopheles gambiae complex

Background: Chromosomal inversion polymorphisms play a role in adaptation to heterogeneous environments. Inversion polymorphisms are implicated in the very high ecological flexibility of the three main malaria vector species of the Afrotropical Anopheles gambiae complex, facilitating the exploitation of anthropogenic environmental modifications and promoting a strong association with humans. In addition to extending the species' spatial and temporal distribution, inversions are associated with epidemiologically relevant mosquito behavior and physiology, underscoring their medical importance. We here present novel PCR-RFLP based assays strongly predictive of genotype for the cosmopolitan 2Rb inversion in An. coluzzii and An. gambiae, a development which overcomes the numerous constraints inherent to traditional cytological karyotyping. Methods: We designed PCR-RFLP genotyping assays based on tag SNPs previously computationally identified as strongly predictive (> 95%) of 2Rb genotype. We targeted those tags whose alternative allelic states destroyed or created the recognition site of a commercially available restriction enzyme, and designed assays with distinctive cleavage profiles for each inversion genotype. The assays were validated on 251 An. coluzzii and 451 An. gambiae cytologically karyotyped specimens from nine countries across Africa and one An. coluzzii laboratory colony. Results: For three tag SNPs, PCR-RFLP assays (denoted DraIII, MspAI, and TatI) reliably produced robust amplicons and clearly distinguishable electrophoretic profiles for all three inversion genotypes. Results obtained with the DraIII assay are ≥ 95% concordant with cytogenetic assignments in both species, while MspAI and TatI assays produce patterns highly concordant with cytogenetic assignments only in An. coluzzii or An. gambiae, respectively. Joint application of species-appropriate pairs of assays increased the concordance levels to > 99% in An. coluzzii and 98% in An. gambiae. Potential sources of discordance (e.g. imperfect association between tag and inversion, allelic dropout, additional polymorphisms in the restriction target site, incomplete or failed restriction digestion) are discussed. Conclusions: The availability of highly specific, cost effective and accessible molecular assays for genotyping 2Rb in An. gambiae and An. coluzzii allows karyotyping of both sexes and all developmental stages. These novel tools will accelerate deeper investigations into the role of this ecologically and epidemiologically important chromosomal inversion in vector biology.[Figure not available: see fulltext.

Machine learning algorithms to infer trait-matching and predict species interactions in ecological networks

Ecologists have long suspected that species are more likely to interact if their traits match in a particular way. For example, a pollination interaction may be more likely if the proportions of a bee's tongue fit a plant's flower shape. Empirical estimates of the importance of trait‐matching for determining species interactions, however, vary significantly among different types of ecological networks. Here, we show that ambiguity among empirical trait‐matching studies may have arisen at least in parts from using overly simple statistical models. Using simulated and real data, we contrast conventional generalized linear models (GLM) with more flexible Machine Learning (ML) models (Random Forest, Boosted Regression Trees, Deep Neural Networks, Convolutional Neural Networks, Support Vector Machines, naïve Bayes, and k‐Nearest‐Neighbor), testing their ability to predict species interactions based on traits, and infer trait combinations causally responsible for species interactions. We found that the best ML models can successfully predict species interactions in plant–pollinator networks, outperforming GLMs by a substantial margin. Our results also demonstrate that ML models can better identify the causally responsible trait‐matching combinations than GLMs. In two case studies, the best ML models successfully predicted species interactions in a global plant–pollinator database and inferred ecologically plausible trait‐matching rules for a plant–hummingbird network from Costa Rica, without any prior assumptions about the system. We conclude that flexible ML models offer many advantages over traditional regression models for understanding interaction networks. We anticipate that these results extrapolate to other ecological network types. More generally, our results highlight the potential of machine learning and artificial intelligence for inference in ecology, beyond standard tasks such as image or pattern recognition

The role of Skp2 and its substrate CDKN1B (p27) in colorectal cancer

Colorectal cancer is one of the most frequent cancers worldwide, having the fourth mortality rate among cancers in both sexes. Numerous studies are investigating the signaling pathways and different factors involved in the development and progression of colorectal cancer. It has recently been shown that the S-phase kinase-associated protein 2 (Skp2) overexpression plays an important role in the pathogenesis of colorectal cancer. We review the role of Skp2 and its ubiquitin-proteasome pathway in colorectal cancer. The F-box protein Skp2, a component of the SCF (Skp1-Cullin 1-F-box) E3 ubiquitin-ligase complex, has been shown to regulate cellular proliferation, cancer progression and metastasis by targeting several cell cycle regulators for ubiquitination and subsequent 26S proteasome degradation. The best known protein substrate of the Skp2 is the cyclin-dependent kinase inhibitor 1B (CDKN1B), also known as p27Kip1. Overexpression of Skp2 and loss of CDKN1B (p27) was strongly associated with aggressive tumor behavior and poor clinical outcome in a variety of cancers, including colorectal cancer. An efficient interaction between Skp2 and CDKN1B (p27) requires the presence of an essential activator of the SCF-Skp2 complex, the cyclin-dependent kinase subunit 1 (Cks1) cofactor. Alterations in the Skp2, Cks1 and CDKN1B (p27) expression have major effects on colorectal carcinogenesis and may serve as an important and independent prognostic marker. Furthermore, we highlight that Skp2 may be a promising therapeutic target for colorectal cancer, and development of Skp2 inhibitors would have a great impact on colorectal cancer therapy.</jats:p

Doped carbon nanotubes as a model system of biased graphene

Albeit difficult to access experimentally, the density of states (DOS) is a key parameter in solid state systems which governs several important phenomena including transport, magnetism, thermal, and thermoelectric properties. We study DOS in an ensemble of potassium intercalated single-wall carbon nanotubes (SWCNT) and show using electron spin resonance spectroscopy that a sizeable number of electron states are present, which gives rise to a Fermi-liquid behavior in this material. A comparison between theoretical and the experimental DOS indicates that it does not display significant correlation effects, even though the pristine nanotube material shows a Luttinger-liquid behavior. We argue that the carbon nanotube ensemble essentially maps out the whole Brillouin zone of graphene thus it acts as a model system of biased graphene

Fine-tuning the functional properties of carbon nanotubes via the interconversion of encapsulated molecules

Tweaking the properties of carbon nanotubes is a prerequisite for their practical applications. Here we demonstrate fine-tuning the electronic properties of single-wall carbon nanotubes via filling with ferrocene molecules. The evolution of the bonding and charge transfer within the tube is demonstrated via chemical reaction of the ferrocene filler ending up as secondary inner tube. The charge transfer nature is interpreted well within density functional theory. This work gives the first direct observation of a fine-tuned continuous amphoteric doping of single-wall carbon nanotubes

Safe food and feed through an integrated toolbox for mycotoxin management: the MyToolBox approach

There is a pressing need to mobilise the wealth of knowledge from the international mycotoxin research conductedover the past 25-30 years, and to perform cutting-edge research where knowledge gaps still exist. This knowledgeneeds to be integrated into affordable and practical tools for farmers and food processors along the chain inorder to reduce the risk of mycotoxin contamination of crops, feed and food. This is the mission of MyToolBox – a four-year project which has received funding from the European Commission. It mobilises a multi-actorpartnership (academia, farmers, technology small and medium sized enterprises, food industry and policystakeholders) to develop novel interventions aimed at achieving a significant reduction in crop losses due tomycotoxin contamination. Besides a field-to-fork approach, MyToolBox also considers safe use options ofcontaminated batches, such as the efficient production of biofuels. Compared to previous efforts of mycotoxin reduction strategies, the distinguishing feature of MyToolBox is to provide the recommended measures to theend users along the food and feed chain in a web-based MyToolBox platform (e-toolbox). The project focuseson small grain cereals, maize, peanuts and dried figs, applicable to agricultural conditions in the EU and China. Crop losses using existing practices are being compared with crop losses after novel pre-harvest interventionsincluding investigation of genetic resistance to fungal infection, cultural control (e.g. minimum tillage or cropdebris treatment), the use of novel biopesticides suitable for organic farming, competitive biocontrol treatment and development of novel modelling approaches to predict mycotoxin contamination. Research into post-harvestmeasures includes real-time monitoring during storage, innovative sorting of crops using vision-technology, novelmilling technology and studying the effects of baking on mycotoxins at an industrial scale

Direct numerical simulations of a high-pressure turbine vane

In this paper, we establish a benchmark data set of a generic high-pressure (HP) turbine vane generated by direct numerical simulation (DNS) to resolve fully the flow. The test conditions for this case are a Reynolds number of 0.57 × 106 and an exit Mach number of 0.9, which is representative of a modern transonic HP turbine vane. In this study, we first compare the simulation results with previously published experimental data. We then investigate how turbulence affects the surface flow physics and heat transfer. An analysis of the development of loss through the vane passage is also performed. The results indicate that freestream turbulence tends to induce streaks within the near-wall flow, which augment the surface heat transfer. Turbulent breakdown is observed over the late suction surface, and this occurs via the growth of two-dimensional Kelvin–Helmholtz spanwise roll-ups, which then develop into lambda vortices creating large local peaks in the surface heat transfer. Turbulent dissipation is found to significantly increase losses within the trailing-edge region of the vane.Partnership for Advanced Computing in Europe (PRACE) and the UK Turbulence Consortium funded by the EPSRC under Grant No. EP/L000261/