Field-controlled suppression of phonon-induced transitions in coupled quantum dots

We calculate the longitudinal-acoustic phonon scattering rate for a vertical double quantum dot system with weak lateral confinement and show that a strong modulation of the single-electron excited states lifetime can be induced by an external magnetic or electric field. The results are obtained for typical realistic devices using a Fermi golden rule approach and a three-dimensional description of the electronic quantum states.Comment: REVTex4 class, 6 pages, 3 figures, to be published in Applied Physics Letter

Wasteland Ecologies: Undomestication and Multispecies Gains on an Anthropocene Dumping Ground

On the western edge of the former brown coal mines in Søby, an area in central Jutland in Denmark that is now protected as a natural and cultural heritage site, a public eyesore hides behind dirt mounds and fences: the waste disposal and recycling facility known as AFLD Fasterholt. Established in the 1970s, when prevailing perceptions were that the entire mining area was a polluted wasteland, the AFLD Fasterholt waste and recycling plant has since changed in response to new EU waste management regulations, as well as the unexpected proliferation of non-human life in the area. Based on field research at this site—an Anthropocene landscape in the heartland of an EU-configured welfare-state—this article is a contribution to the multispecies ethnography and political ecology of wastelands. We argue that “waste” is a co-species, biopolitical happening—a complex symbolic, political, biological, and technological history. We combine ethnographic fieldwork, social history, wildlife observation, and spatial analysis to follow what we call “undomestication,” the reconfiguration of human projects by more-than-human forms of life into novel assemblies of species, politics, resources, and technologies. Waste landscapes, this article argues, are the result of unheralded multispecies collaboration that can be traced empirically by attending ethnographically to multispecies forms of “gain-making,” the ways in which humans and other species leverage difference to find economic and ecological opportunity

Quantum estimation and remote charge sensing with a hole-spin qubit in silicon

Hole-spin qubits in semiconductors represent a mature platform for quantum technological applications. Here we consider their use as quantum sensors, and specifically for inferring the presence and estimating the distance from the qubit of a remote charge. Different approaches are considered, based on the use of single or double quantum dots, ground and out-of-equilibrium states, Rabi and Ramsey measurements, and comparatively analyzed by means of the discrimination probability, and of the classical and quantum Fisher information. Detailed quantitative aspects result from the multiband character of the hole states, which we account for by means of the Luttinger-Kohn Hamiltonian. Furthermore, general conclusions can be drawn on the relative efficiency of the above options, and analytical expressions are derived for the Fisher information of a generic qubit within the Rabi and Ramsey schemes

Plant scientists' research attention is skewed towards colourful, conspicuous and broadly distributed flowers

Despite the perception that plant science focuses on strictly scientific criteria, this analysis finds that there is an aesthetic bias in regards to which plants, based on certain traits, receive more research attention. Scientists' research interests are often skewed toward charismatic organisms, but quantifying research biases is challenging. By combining bibliometric data with trait-based approaches and using a well-studied alpine flora as a case study, we demonstrate that morphological and colour traits, as well as range size, have significantly more impact on species choice for wild flowering plants than traits related to ecology and rarity. These biases should be taken into account to inform more objective plant conservation efforts.Peer reviewe

Simulations of Optical Emissions for Attacking AES and Masked AES

In this paper we present a novel attack based on photonic emission analysis targeting software implementations of AES. We focus on the particular case in which the attacker can collect the photonic emission of a limited number of sense amplifiers (e.g. only one) of the SRAM storing the S-Box. The attack consists in doing hypothesis on the secret key based on the knowledge of the partial output of the SubBytes operation. We also consider the possibility to attack a masked implementation of AES using the photonic emission analysis. In the case of masking, the attacker needs 2 leakages of the same encryption to overcome the randomization of the masks. For our analysis, we assume the same physical setup described in other previous works. Reported results are based on simulations with some hypothesis on the probability of photonic emission of a single transistor

Characterization of GECPAR, a noncoding RNA that regulates the transcriptional program of diffuse large B cell lymphoma

Enhancers are regulatory regions of DNA, which play a key role in cell-type specific differentiation and development. Most active enhancers are transcribed into enhancer RNAs (eRNAs) that can regulate transcription of target genes by means of in cis as well as in trans action. eRNAs stabilize contacts between distal genomic regions and mediate the interaction of DNA with master transcription factors. Here, we characterised an enhancer RNA, GECPAR (GErminal Center Proliferative Adapter RNA), that is specifically transcribed in normal and neoplastic germinal center B-cells from the super-enhancer of POU2AF1, a key regulatory gene of the germinal center reaction. Using diffuse large B cell lymphoma cell line models, we demonstrated the tumor suppressor activity of GECPAR, which is mediated via its transcriptional regulation of proliferation and differentiation genes, particularly MYC and the Wnt pathway

Marine anticancer agents: An overview with a particular focus on their chemical classes

UID/Multi/04378/2019 IF/00700/2014 grant number 216Z167 grant RTA 2015-00010-C03-02 No. PBA/MB/16/01 PDOC/19/02/01The marine environment is a rich source of biologically active molecules for the treatment of human diseases, especially cancer. The adaptation to unique environmental conditions led marine organisms to evolve different pathways than their terrestrial counterparts, thus producing unique chemicals with a broad diversity and complexity. So far, more than 36,000 compounds have been isolated from marine micro- and macro-organisms including but not limited to fungi, bacteria, microalgae, macroalgae, sponges, corals, mollusks and tunicates, with hundreds of new marine natural products (MNPs) being discovered every year. Marine-based pharmaceuticals have started to impact modern pharmacology and different anti-cancer drugs derived from marine compounds have been approved for clinical use, such as: cytarabine, vidarabine, nelarabine (prodrug of ara-G), fludarabine phosphate (pro-drug of ara-A), trabectedin, eribulin mesylate, brentuximab vedotin, polatuzumab vedotin, enfortumab vedotin, belantamab mafodotin, plitidepsin, and lurbinectedin. This review focuses on the bioactive molecules derived from the marine environment with anticancer activity, discussing their families, origin, structural features and therapeutic use.publishersversionpublishe

Speeding up the Consensus Clustering methodology for microarray data analysis

<p>Abstract</p> <p>Background</p> <p>The inference of the number of clusters in a dataset, a fundamental problem in Statistics, Data Analysis and Classification, is usually addressed via internal validation measures. The stated problem is quite difficult, in particular for microarrays, since the inferred prediction must be sensible enough to capture the inherent biological structure in a dataset, e.g., functionally related genes. Despite the rich literature present in that area, the identification of an internal validation measure that is both fast and precise has proved to be elusive. In order to partially fill this gap, we propose a speed-up of <monospace>Consensus</monospace> (Consensus Clustering), a methodology whose purpose is the provision of a prediction of the number of clusters in a dataset, together with a dissimilarity matrix (the consensus matrix) that can be used by clustering algorithms. As detailed in the remainder of the paper, <monospace>Consensus</monospace> is a natural candidate for a speed-up.</p> <p>Results</p> <p>Since the time-precision performance of <monospace>Consensus</monospace> depends on two parameters, our first task is to show that a simple adjustment of the parameters is not enough to obtain a good precision-time trade-off. Our second task is to provide a fast approximation algorithm for <monospace>Consensus</monospace>. That is, the closely related algorithm <monospace>FC</monospace> (Fast Consensus) that would have the same precision as <monospace>Consensus</monospace> with a substantially better time performance. The performance of <monospace>FC</monospace> has been assessed via extensive experiments on twelve benchmark datasets that summarize key features of microarray applications, such as cancer studies, gene expression with up and down patterns, and a full spectrum of dimensionality up to over a thousand. Based on their outcome, compared with previous benchmarking results available in the literature, <monospace>FC</monospace> turns out to be among the fastest internal validation methods, while retaining the same outstanding precision of <monospace>Consensus</monospace>. Moreover, it also provides a consensus matrix that can be used as a dissimilarity matrix, guaranteeing the same performance as the corresponding matrix produced by <monospace>Consensus</monospace>. We have also experimented with the use of <monospace>Consensus</monospace> and <monospace>FC</monospace> in conjunction with <monospace>NMF</monospace> (Nonnegative Matrix Factorization), in order to identify the correct number of clusters in a dataset. Although <monospace>NMF</monospace> is an increasingly popular technique for biological data mining, our results are somewhat disappointing and complement quite well the state of the art about <monospace>NMF</monospace>, shedding further light on its merits and limitations.</p> <p>Conclusions</p> <p>In summary, <monospace>FC</monospace> with a parameter setting that makes it robust with respect to small and medium-sized datasets, i.e, number of items to cluster in the hundreds and number of conditions up to a thousand, seems to be the internal validation measure of choice. Moreover, the technique we have developed here can be used in other contexts, in particular for the speed-up of stability-based validation measures.</p