Lack of mutagenic effect by multi-walled functionalized carbon nanotubes in the somatic cells of Drosophila melanogaster

AbstractCarbon nanotubes (CNTs) are formed by rolling up a single graphite sheet into a tube. Among the different types of CNTs, the multi-walled carbon nanotubes (MWCNTs) comprise a set of concentric nanotubes with perfect structures. Several uses for MWCNTs have been suggested to be included in biological applications such as manufacturing of biosensors, carriers of drugs. However, before these materials can be put on the market, it is necessary to know their genotoxic effects. Thus, this study aims to evaluate the mutagenicity of multi-walled carbon nanotubes (MWCNTs) functionalized in somatic cells of Drosophila melanogaster, using the somatic mutation and recombination test (SMART). This assay detects the loss of heterozygosity of marker genes expressed phenotypically on the wings of the fly. Larvae of three days were used, resulting from ST cross, with basal levels of the cytochrome P450 and larvae of high metabolic bioactivity capacity (HB cross). They were treated with different concentrations of MWCNTs functionalized. The MH descendants, analyzed in both ST and HB crosses, had no significant effects on the frequency of mutant. Based on the results and on the experimental conditions mentioned in this study, it was concluded that MWCNTs were not mutagenic in D. melanogaster

Multidimensional continued fractions, dynamical renormalization and KAM theory

The disadvantage of `traditional' multidimensional continued fraction algorithms is that it is not known whether they provide simultaneous rational approximations for generic vectors. Following ideas of Dani, Lagarias and Kleinbock-Margulis we describe a simple algorithm based on the dynamics of flows on the homogeneous space SL(2,Z)\SL(2,R) (the space of lattices of covolume one) that indeed yields best possible approximations to any irrational vector. The algorithm is ideally suited for a number of dynamical applications that involve small divisor problems. We explicitely construct renormalization schemes for (a) the linearization of vector fields on tori of arbitrary dimension and (b) the construction of invariant tori for Hamiltonian systems.Comment: 51 page

An artificial immune system for fuzzy-rule induction in data mining

This work proposes a classification-rule discovery algorithm integrating artificial immune systems and fuzzy systems. The algorithm consists of two parts: a sequential covering procedure and a rule evolution procedure. Each antibody (candidate solution) corresponds to a classification rule. The classification of new examples (antigens) considers not only the fitness of a fuzzy rule based on the entire training set, but also the affinity between the rule and the new example. This affinity must be greater than a threshold in order for the fuzzy rule to be activated, and it is proposed an adaptive procedure for computing this threshold for each rule. This paper reports results for the proposed algorithm in several data sets. Results are analyzed with respect to both predictive accuracy and rule set simplicity, and are compared with C4.5rules, a very popular data mining algorithm

The effect of bone marrow-derived stem cells associated with platelet-rich plasma on the osseointegration of immediately placed implants

Stem cells associated with growth factors have been shown to improve bone healing and the osseointegration of dental implants. A Brazilian miniature pig model was used to evaluate the effect of autologous bone marrow-derived mesenchymal stem cells (BM-MS

Bayesian Nonparametric Inverse Reinforcement Learning

Inverse reinforcement learning (IRL) is the task of learning the reward function of a Markov Decision Process (MDP) given the transition function and a set of observed demonstrations in the form of state-action pairs. Current IRL algorithms attempt to find a single reward function which explains the entire observation set. In practice, this leads to a computationally-costly search over a large (typically infinite) space of complex reward functions. This paper proposes the notion that if the observations can be partitioned into smaller groups, a class of much simpler reward functions can be used to explain each group. The proposed method uses a Bayesian nonparametric mixture model to automatically partition the data and find a set of simple reward functions corresponding to each partition. The simple rewards are interpreted intuitively as subgoals, which can be used to predict actions or analyze which states are important to the demonstrator. Experimental results are given for simple examples showing comparable performance to other IRL algorithms in nominal situations. Moreover, the proposed method handles cyclic tasks (where the agent begins and ends in the same state) that would break existing algorithms without modification. Finally, the new algorithm has a fundamentally different structure than previous methods, making it more computationally efficient in a real-world learning scenario where the state space is large but the demonstration set is small

Twistors and Black Holes

Motivated by black hole physics in N=2, D=4 supergravity, we study the geometry of quaternionic-Kahler manifolds M obtained by the c-map construction from projective special Kahler manifolds M_s. Improving on earlier treatments, we compute the Kahler potentials on the twistor space Z and Swann space S in the complex coordinates adapted to the Heisenberg symmetries. The results bear a simple relation to the Hesse potential \Sigma of the special Kahler manifold M_s, and hence to the Bekenstein-Hawking entropy for BPS black holes. We explicitly construct the ``covariant c-map'' and the ``twistor map'', which relate real coordinates on M x CP^1 (resp. M x R^4/Z_2) to complex coordinates on Z (resp. S). As applications, we solve for the general BPS geodesic motion on M, and provide explicit integral formulae for the quaternionic Penrose transform relating elements of H^1(Z,O(-k)) to massless fields on M annihilated by first or second order differential operators. Finally, we compute the exact radial wave function (in the supergravity approximation) for BPS black holes with fixed electric and magnetic charges.Comment: 47 pages, v2: typos corrected, reference added, v3: minor change

Will ultrathin CIGS solar cells overtake the champion thin-film cells? Updated SCAPS baseline models reveal main differences between ultrathin and standard CIGS

Cu(In,Ga)Se2 (CIGS) solar cells are amongst the best performing thin-film technologies, with the latest performance gains being mainly due to recent years improvements obtained with post-deposition treatments (PDT). Moreover, thinning of the absorber layer down to sub-micrometre values (ultrathin absorbers) is of extreme importance for CIGS to be even more cost-effective and sustainable. However, electrical and optical limitations, such as rear interface recombination and insufficient light absorption, prevent the widespread implementation of ultrathin CIGS devices. The recent electrical CIGS simulation baseline models have failed to keep up with the experimental developments. Here an updated and experimentally based baseline model for electrical simulations in the Solar Cell Capacitor Simulator (SCAPS) software is presented and discussed with the incorporation of the PDT effects and increased optical accuracy with the support from Finite-Difference Time-Domain (FDTD) simulation results. Furthermore, a champion solar cell with an equivalent architecture validates the developed thin-film model. The baseline model is also applied to ultrathin CIGS solar cell devices, validated with the ultrathin champion cell. Ultimately, these ultrathin models pave the way for an ultrathin baseline model. Simulations results reveal that addressing these absorbers' inherent limitations makes it possible to achieve an ultrathin solar cell with at least 21.0% power conversion efficiency, with open-circuit voltage values even higher than the recent thin-film champion cells.This work was supported by the Fundação para a Ciência e Tecno-logia (FCT) grant numbers DFA/BD/7073/2020, DFA/BD/4564/2020, SFRH/BD/146776/2019, IF/00133/2015, UIDB/50025/2020, UIDP/50025/2020, UIDB/04730/2020, and UIDP/04730/2020. The authors want to acknowledge the funding from the project NovaCell (PTDC/ CTM-CTM/28075/2017). The authors also acknowledge the financial support of the project Baterias 2030, with the reference POCI-01-0247-FEDER-046109, co-funded by Operational Programme for Competitiveness and Internationalization (COMPETE 2020), under the Portugal 2020 Partnership Agreement, through the European Regional Development Fund (ERDFinfo:eu-repo/semantics/submittedVersio