Patterned tungsten disulfide/graphene heterostructures for efficient multifunctional optoelectronic devices

A patterned-growth, scalable fabrication strategy allows photodetectors with good electrical properties that show fast response with red light and persistent photocurrent with blue light

Role of Chemosensory TRP Channels in Lung Cancer

Transient receptor potential (TRP) channels represent a large family of cation channels and many members of the TRP family have been shown to act as polymodal receptor molecules for irritative or potentially harmful substances. These chemosensory TRP channels have been extensively characterized in primary sensory and neuronal cells. However, in recent years the functional expression of these proteins in non-neuronal cells, e.g., in the epithelial lining of the respiratory tract has been confirmed. Notably, these proteins have also been described in a number of cancer types. As sensor molecules for noxious compounds, chemosensory TRP channels are involved in cell defense mechanisms and influence cell survival following exposure to toxic substances via the modulation of apoptotic signaling. Of note, a number of cytostatic drugs or drug metabolites can activate these TRP channels, which could affect the therapeutic efficacy of these cytostatics. Moreover, toxic inhalational substances with potential involvement in lung carcinogenesis are well established TRP activators. In this review, we present a synopsis of data on the expression of chemosensory TRP channels in lung cancer cells and describe TRP agonists and TRP-dependent signaling pathways with potential relevance to tumor biology. Furthermore, we discuss a possible role of TRP channels in the non-genomic, tumor-promoting effects of inhalational carcinogens such as cigarette smoke

An addressable quantum dot qubit with fault-tolerant control fidelity

Exciting progress towards spin-based quantum computing has recently been made with qubits realized using nitrogen-vacancy (N-V) centers in diamond and phosphorus atoms in silicon, including the demonstration of long coherence times made possible by the presence of spin-free isotopes of carbon and silicon. However, despite promising single-atom nanotechnologies, there remain substantial challenges in coupling such qubits and addressing them individually. Conversely, lithographically defined quantum dots have an exchange coupling that can be precisely engineered, but strong coupling to noise has severely limited their dephasing times and control fidelities. Here we combine the best aspects of both spin qubit schemes and demonstrate a gate-addressable quantum dot qubit in isotopically engineered silicon with a control fidelity of 99.6%, obtained via Clifford based randomized benchmarking and consistent with that required for fault-tolerant quantum computing. This qubit has orders of magnitude improved coherence times compared with other quantum dot qubits, with T_2* = 120 mus and T_2 = 28 ms. By gate-voltage tuning of the electron g*-factor, we can Stark shift the electron spin resonance (ESR) frequency by more than 3000 times the 2.4 kHz ESR linewidth, providing a direct path to large-scale arrays of addressable high-fidelity qubits that are compatible with existing manufacturing technologies

Comparison of Classifier Fusion Methods for Predicting Response to Anti HIV-1 Therapy

BACKGROUND: Analysis of the viral genome for drug resistance mutations is state-of-the-art for guiding treatment selection for human immunodeficiency virus type 1 (HIV-1)-infected patients. These mutations alter the structure of viral target proteins and reduce or in the worst case completely inhibit the effect of antiretroviral compounds while maintaining the ability for effective replication. Modern anti-HIV-1 regimens comprise multiple drugs in order to prevent or at least delay the development of resistance mutations. However, commonly used HIV-1 genotype interpretation systems provide only classifications for single drugs. The EuResist initiative has collected data from about 18,500 patients to train three classifiers for predicting response to combination antiretroviral therapy, given the viral genotype and further information. In this work we compare different classifier fusion methods for combining the individual classifiers. PRINCIPAL FINDINGS: The individual classifiers yielded similar performance, and all the combination approaches considered performed equally well. The gain in performance due to combining methods did not reach statistical significance compared to the single best individual classifier on the complete training set. However, on smaller training set sizes (200 to 1,600 instances compared to 2,700) the combination significantly outperformed the individual classifiers (p<0.01; paired one-sided Wilcoxon test). Together with a consistent reduction of the standard deviation compared to the individual prediction engines this shows a more robust behavior of the combined system. Moreover, using the combined system we were able to identify a class of therapy courses that led to a consistent underestimation (about 0.05 AUC) of the system performance. Discovery of these therapy courses is a further hint for the robustness of the combined system. CONCLUSION: The combined EuResist prediction engine is freely available at http://engine.euresist.org

Nachweis und Bestimmung von Phenacetin, N-Acetyl-p-aminophenol sowie ihren Hauptumwandlungsprodukten in Harn und Serum

Peer Reviewe

Bestimmung von Phenacetin und N-Acetyl-p-aminophenol über Stoffwechselprodukte im Harn

Peer Reviewe

Quantum dot spectroscopy using a single phosphorus donor

Using a deterministic single P donor placed with atomic precision accuracy next to a nanoscale silicon quantum dot, we present a way to analyze the energy spectrum of small quantum dots in silicon by tunnel-coupled transport measurements. The energy-level structure of the quantum dot is observed as resonance features within the transport bias triangles when the donor chemical potential is aligned with states within the quantum dot as confirmed by a numeric rate equation solver SIMON. This technique allows us to independently extract the quantum dot level structure irrespective of the density of states in the leads. Such a method is useful for the investigation of silicon quantum dots in the few-electron regime where the level structure is governed by an intricate interplay between the spin- and the valley-orbit degrees of freedom

Charge sensing of a few-donor double quantum dot in silicon

We demonstrate the charge sensing of a few-donor double quantum dot precision placed with atomic resolution scanning tunnelling microscope lithography. We show that a tunnel-coupled single electron transistor (SET) can be used to detect electron transitions on both dots as well as inter-dot transitions. We demonstrate that we can control the tunnel times of the second dot to the SET island by ∼4 orders of magnitude by detuning its energy with respect to the first dot

High-Fidelity Rapid Initialization and Read-Out of an Electron Spin via the Single Donor D- Charge State

We demonstrate high-fidelity electron spin read-out of a precision placed single donor in silicon via spin selective tunneling to either the D+ or D- charge state of the donor. By performing read-out at the stable two electron D0虠D- charge transition we can increase the tunnel rates to a nearby single electron transistor charge sensor by nearly 2 orders of magnitude, allowing faster qubit read-out (1 ms) with minimum loss in read-out fidelity (98.4%) compared to read-out at the D+虠D0 transition (99.6%). Furthermore, we show that read-out via the D- charge state can be used to rapidly initialize the electron spin qubit in its ground state with a fidelity of FI=99.8%