The Taiwan ECDFS Near-Infrared Survey: Ultra-deep J and Ks Imaging in the Extended Chandra Deep Field-South

We present ultra-deep J and Ks imaging observations covering a 30' * 30' area of the Extended Chandra Deep Field-South (ECDFS) carried out by our Taiwan ECDFS Near-Infrared Survey (TENIS). The median 5-sigma limiting magnitudes for all detected objects in the ECDFS reach 24.5 and 23.9 mag (AB) for J and Ks, respectively. In the inner 400 arcmin^2 region where the sensitivity is more uniform, objects as faint as 25.6 and 25.0 mag are detected at 5-sigma. So this is by far the deepest J and Ks datasets available for the ECDFS. To combine the TENIS with the Spitzer IRAC data for obtaining better spectral energy distributions of high-redshift objects, we developed a novel deconvolution technique (IRACLEAN) to accurately estimate the IRAC fluxes. IRACLEAN can minimize the effect of blending in the IRAC images caused by the large point-spread functions and reduce the confusion noise. We applied IRACLEAN to the images from the Spitzer IRAC/MUSYC Public Legacy in the ECDFS survey (SIMPLE) and generated a J+Ks selected multi-wavelength catalog including the photometry of both the TENIS near-infrared and the SIMPLE IRAC data. We publicly release the data products derived from this work, including the J and Ks images and the J+Ks selected multiwavelength catalog.Comment: 25 pages, 25 figures, ApJS in pres

Experimental Bayesian Quantum Phase Estimation on a Silicon Photonic Chip

Quantum phase estimation is a fundamental subroutine in many quantum algorithms, including Shor's factorization algorithm and quantum simulation. However, so far results have cast doubt on its practicability for near-term, non-fault tolerant, quantum devices. Here we report experimental results demonstrating that this intuition need not be true. We implement a recently proposed adaptive Bayesian approach to quantum phase estimation and use it to simulate molecular energies on a Silicon quantum photonic device. The approach is verified to be well suited for pre-threshold quantum processors by investigating its superior robustness to noise and decoherence compared to the iterative phase estimation algorithm. This shows a promising route to unlock the power of quantum phase estimation much sooner than previously believed

Turning bubbles on and off during boiling using charged surfactants

Boiling—a process that has powered industries since the steam age—is governed by bubble formation. State-of-the-art boiling surfaces often increase bubble nucleation via roughness and/or wettability modification to increase performance. However, without active in situ control of bubbles, temperature or steam generation cannot be adjusted for a given heat input. Here we report the ability to turn bubbles ‘on and off’ independent of heat input during boiling both temporally and spatially via molecular manipulation of the boiling surface. As a result, we can rapidly and reversibly alter heat transfer performance up to an order of magnitude. Our experiments show that this active control is achieved by electrostatically adsorbing and desorbing charged surfactants to alter the wettability of the surface, thereby affecting nucleation. This approach can improve performance and flexibility in existing boiling technologies as well as enable emerging or unprecedented energy applications.Singapore-MIT Alliance for Research and TechnologyNational Science Foundation (U.S.). Materials Research Science and Engineering Centers (Program) (Award DMR-0819762

Engineering proteinase K using machine learning and synthetic genes

BACKGROUND: Altering a protein's function by changing its sequence allows natural proteins to be converted into useful molecular tools. Current protein engineering methods are limited by a lack of high throughput physical or computational tests that can accurately predict protein activity under conditions relevant to its final application. Here we describe a new synthetic biology approach to protein engineering that avoids these limitations by combining high throughput gene synthesis with machine learning-based design algorithms. RESULTS: We selected 24 amino acid substitutions to make in proteinase K from alignments of homologous sequences. We then designed and synthesized 59 specific proteinase K variants containing different combinations of the selected substitutions. The 59 variants were tested for their ability to hydrolyze a tetrapeptide substrate after the enzyme was first heated to 68°C for 5 minutes. Sequence and activity data was analyzed using machine learning algorithms. This analysis was used to design a new set of variants predicted to have increased activity over the training set, that were then synthesized and tested. By performing two cycles of machine learning analysis and variant design we obtained 20-fold improved proteinase K variants while only testing a total of 95 variant enzymes. CONCLUSION: The number of protein variants that must be tested to obtain significant functional improvements determines the type of tests that can be performed. Protein engineers wishing to modify the property of a protein to shrink tumours or catalyze chemical reactions under industrial conditions have until now been forced to accept high throughput surrogate screens to measure protein properties that they hope will correlate with the functionalities that they intend to modify. By reducing the number of variants that must be tested to fewer than 100, machine learning algorithms make it possible to use more complex and expensive tests so that only protein properties that are directly relevant to the desired application need to be measured. Protein design algorithms that only require the testing of a small number of variants represent a significant step towards a generic, resource-optimized protein engineering process

Radiative transfer modeling of phytoplankton fluorescence quenching processes

We report the first radiative transfer model that is able to simulate phytoplankton fluorescence with both photochemical and non-photochemical quenching included. The fluorescence source term in the inelastic radiative transfer equation is proportional to both the quantum yield and scalar irradiance at excitation wavelengths. The photochemical and nonphotochemical quenching processes change the quantum yield based on the photosynthetic active radiation. A sensitivity study was performed to demonstrate the dependence of the fluorescence signal on chlorophyll a concentration, aerosol optical depths and solar zenith angles. This work enables us to better model the phytoplankton fluorescence, which can be used in the design of new space-based sensors that can provide sufficient sensitivity to detect the phytoplankton fluorescence signal. It could also lead to more accurate remote sensing algorithms for the study of phytoplankton physiology

Radiative Transfer Modeling of Phytoplankton Fluorescence Quenching Processes

We report the first radiative transfer model that is able to simulate phytoplankton fluorescence with both photo chemical and non-photo chemical quenching included. The fluorescence source term in the inelastic radiative transfer equation is proportional to both the quantum yield and scalar irradiance at excitation wavelengths. The photo chemical and non photo chemical quenching processes change the quantum yield based on the photosynthetic active radiation. A sensitivity study was performed to demonstrate the dependence of the fluorescence signal on chlorophyll a concentration, aerosol optical depths and solar zenith angles. This work enables us to better model the phytoplankton fluorescence, which can be used in the design of new space-based sensors that can provide sufficient sensitivity to detect the phytoplankton fluorescence signal. It could also lead to more accurate remote sensing algorithms for the study of phytoplankton physiology

ab initio modeling of open systems: charge transfer, electron conduction, and molecular switching of a C_{60} device

We present an {\it ab initio} analysis of electron conduction through a $C_{60}$ molecular device. Charge transfer from the device electrodes to the molecular region is found to play a crucial role in aligning the lowest unoccupied molecular orbital (LUMO) of the $C_{60}$ to the Fermi level of the electrodes. This alignment induces a substantial device conductance of $\sim 2.2 \times (2e^2/h)$. A gate potential can inhibit charge transfer and introduce a conductance gap near $E_F$, changing the current-voltage characteristics from metallic to semi-conducting, thereby producing a field effect molecular current switch

Synthetic, non-intoxicating 8,9-dihydrocannabidiol for the mitigation of seizures

There can be a fine line between therapeutic intervention and substance abuse, and this point is clearly exemplified in herbal cannabis and its products. Therapies involving cannabis have been the treatment of last resort for some cases of refractory epilepsy, and this has been among the strongest medical justifications for legalization of marijuana. In order to circumvent the narcotic effects of 9-tetrahydrocannabinol (THC), many studies have concentrated on its less intoxicating isomer cannabidiol (CBD). However, CBD, like all natural cannabinoids, is a controlled substance in most countries, and its conversion into THC can be easily performed using common chemicals. We describe here the anticonvulsant properties of 8,9-dihydrocannibidiol (H2CBD), a fully synthetic analogue of CBD that is prepared from inexpensive, non-cannabis derived precursors. H2CBD was found to have effectiveness comparable to CBD both for decreasing the number and reducing the severity of pentylenetetrazole-induced seizures in rats. Finally, H2CBD cannot be converted by any reasonable synthetic route into THC, and thus has the potential to act as a safe, noncontroversial drug for seizure mitigation