Joint Modeling of Radial Velocities and Photometry with a Gaussian Process Framework

Developments in the stability of modern spectrographs have led to extremely precise instrumental radial velocity (RV) measurements. For most stars, the detection limit of planetary companions with these instruments is expected to be dominated by astrophysical noise sources such as starspots. Correlated signals caused by rotationally-modulated starspots can obscure or mimic the Doppler shifts induced by even the closest, most massive planets. This is especially true for young, magnetically active stars where stellar activity can cause fluctuation amplitudes of $\gtrsim$0.1 mag in brightness and $\gtrsim$100 m s$^{-1}$ in RV semi-amplitudes. Techniques that can mitigate these effects and increase our sensitivity to young planets are critical to improving our understanding of the evolution of planetary systems. Gaussian processes (GPs) have been successfully employed to model and constrain activity signals in individual cases. However, a principled approach of this technique, specifically for the joint modeling of photometry and RVs, has not yet been developed. In this work, we present a GP framework to simultaneously model stellar activity signals in photometry and RVs that can be used to investigate the relationship between both time series. Our method, inspired by the $\textit{FF}^\prime$ framework of (Aigrain et al. 2012), models spot-driven activity signals as the linear combinations of two independent latent GPs and their time derivatives. We also simulate time series affected by starspots by extending the $\texttt{starry}$ software (Luger et al. 2019) to incorporate time evolution of stellar features. Using these synthetic datasets, we show that our method can predict spot-driven RV variations with greater accuracy than other GP approaches.Comment: 19 pages, 10 figure

sFuzz: An efficient adaptive fuzzer for solidity smart contracts

Ministry of Education, Singapore under its Academic Research Funding Tier

Study of the Correlation Between Bronchial Hyperresponsiveness and Exhaled Nitric Oxide in Subjects with Suspected Symptoms of Asthma

Background: Bronchial hyperresponsiveness (BHR) is one of main features of asthma within chronic inflammation and reversible bronchoconstriction. Actually, methacholine challenge is useful method to detect BHR in subjects with suspected asthma symptoms. However, this method has some limitations due to its safety and side effects. The measure of exhaled nitric oxide (NO) demonstrates currently as the alternative method for methacholine challenge. Methods: Ninety-five subjects had at least one of the following symptoms were included in this study: wheezing or chest tightness during exercise, chronic cough, or nocturnal coughing. They were divided into two groups depending on the positivity or negativity of BHR. Lung function test, exhaled NO measurement, and methacholine challenge were done for each study subject. Results: There were no significant differences between two groups for age and male/female ratio (41 ± 22 vs 38 ± 23 years old and 0.9 vs 1.1; P > 0.05 and P > 0.05; respectively). The percentage of wheezing and nocturnal coughing in subjects with positive BHR (BHR+) was significantly higher than that in subjects with negative BHR (BHR-: 70.9% and 64.5% vs 31.2% and 45.1%; P<0.001 and P<0.01; respectively). FENO measured at 50 mL/s in subjects with BHR+ was significantly higher subjects with BHR- (36 ± 10 ppb vs 11 ± 9 ppb; P<0.001). There was a significant correlation between FENO-50 mL/s and methacholine dose in subjects with BHR+ (R= -0.695; P<0.001). FENO-50 mL/s at 35 ppb had 86.7% of sensibility and 82.9% of specificity for diagnosis of BHR. Conclusion: FENO is a useful biomarker for diagnosis of asthma in subjects with suspected symptoms of asthma. FENO level has a high sensitivity and specificity for screening out subjects with BHR. The measurement of exhaled NO may be an alternative method for detecting BHR in diagnosis of asthma in clinical practice

Signs of Similar Stellar Obliquity Distributions for Hot and Warm Jupiters Orbiting Cool Stars

Transiting giant planets provide a natural opportunity to examine stellar obliquities, which offer clues about the origin and dynamical histories of close-in planets. Hot Jupiters orbiting Sun-like stars show a tendency for obliquity alignment, which suggests that obliquities are rarely excited or that tidal realignment is common. However, the stellar obliquity distribution is less clear for giant planets at wider separations where realignment mechanisms are not expected to operate. In this work, we uniformly derive line-of-sight inclinations for 47 cool stars ($T_\mathrm{eff}$ $<$ 6200 K) harboring transiting hot and warm giant planets by combining rotation periods, stellar radii, and $v \sin i$ measurements. Among the systems that show signs of spin-orbit misalignment in our sample, three are identified as being misaligned here for the first time. Of particular interest are Kepler-1654, one of the longest-period (1047 d; 2.0 AU) giant planets in a misaligned system, and Kepler-30, a multi-planet misaligned system. By comparing the reconstructed underlying inclination distributions, we find that the inferred minimum misalignment distributions of hot Jupiters spanning $a/R_{*}$ = 3-20 ($\approx$ 0.01-0.1 AU) and warm Jupiters spanning $a/R_{*}$ = 20-400 ($\approx$ 0.1-1.9 AU) are in good agreement. With 90$\%$ confidence, at least 24$^{+9}_{-7}\%$ of warm Jupiters and 14$^{+7}_{-5}\%$ of hot Jupiters around cool stars are misaligned by at least 10$^\circ$. Most stars harboring warm Jupiters are therefore consistent with spin-orbit alignment. The similarity of hot and warm Jupiter misalignment rates suggests that either the occasional misalignments are primordial and originate in misaligned disks, or the same underlying processes that create misaligned hot Jupiters also lead to misaligned warm Jupiters.Comment: AJ, accepte

Out of Plane Distortions of the Heme b of Escherichia coli Succinate Dehydrogenase

The role of the heme b in Escherichia coli succinate dehydrogenase is highly ambiguous and its role in catalysis is questionable. To examine whether heme reduction is an essential step of the catalytic mechanism, we generated a series of site-directed mutations around the heme binding pocket, creating a library of variants with a stepwise decrease in the midpoint potential of the heme from the wild-type value of +20 mV down to −80 mV. This difference in midpoint potential is enough to alter the reactivity of the heme towards succinate and thus its redox state under turnover conditions. Our results show both the steady state succinate oxidase and fumarate reductase catalytic activity of the enzyme are not a function of the redox potential of the heme. As well, lower heme potential did not cause an increase in the rate of superoxide production both in vitro and in vivo. The electron paramagnetic resonance (EPR) spectrum of the heme in the wild-type enzyme is a combination of two distinct signals. We link EPR spectra to structure, showing that one of the signals likely arises from an out-of-plane distortion of the heme, a saddled conformation, while the second signal originates from a more planar orientation of the porphyrin ring

CW Interference Effects on High Data Rate Transmission Through the ACTS Wideband Channel

Satellite communications channels are susceptible to various sources of interference. Wideband channels have a proportionally greater probability of receiving interference than narrowband channels. NASA's Advanced Communications Technology Satellite (ACTS) includes a 900 MHz bandwidth hardlimiting transponder which has provided an opportunity for the study of interference effects of wideband channels. A series of interference tests using two independent ACTS ground terminals measured the effects of continuous-wave (CW) uplink interference on the bit-error rate of a 220 Mbps digitally modulated carrier. These results indicate the susceptibility of high data rate transmissions to CW interference and are compared to results obtained with a laboratory hardware-based system simulation and a computer simulation

Refining Long Short-Term Memory Neural Network Input Parameters for Enhanced Solar Power Forecasting

This article presents a research approach to enhancing the quality of short-term power output forecasting models for photovoltaic plants using a Long Short-Term Memory (LSTM) recurrent neural network. Typically, time-related indicators are used as inputs for forecasting models of PV generators. However, this study proposes replacing the time-related inputs with clear sky solar irradiance at the specific location of the power plant. This feature represents the maximum potential solar radiation that can be received at that particular location on Earth. The Ineichen/Perez model is then employed to calculate the solar irradiance. To evaluate the effectiveness of this approach, the forecasting model incorporating this new input was trained and the results were compared with those obtained from previously published models. The results show a reduction in the Mean Absolute Percentage Error (MAPE) from 3.491% to 2.766%, indicating a 24% improvement. Additionally, the Root Mean Square Error (RMSE) decreased by approximately 0.991 MW, resulting in a 45% improvement. These results demonstrate that this approach is an effective solution for enhancing the accuracy of solar power output forecasting while reducing the number of input variables

Isolation and characterization of novel microsatellite markers and their application for diversity assessment in cultivated groundnut (Arachis hypogaea)

<p>Abstract</p> <p>Background</p> <p>Cultivated peanut or groundnut (<it>Arachis hypogaea </it>L.) is the fourth most important oilseed crop in the world, grown mainly in tropical, subtropical and warm temperate climates. Due to its origin through a single and recent polyploidization event, followed by successive selection during breeding efforts, cultivated groundnut has a limited genetic background. In such species, microsatellite or simple sequence repeat (SSR) markers are very informative and useful for breeding applications. The low level of polymorphism in cultivated germplasm, however, warrants a need of larger number of polymorphic microsatellite markers for cultivated groundnut.</p> <p>Results</p> <p>A microsatellite-enriched library was constructed from the genotype TMV2. Sequencing of 720 putative SSR-positive clones from a total of 3,072 provided 490 SSRs. 71.2% of these SSRs were perfect type, 13.1% were imperfect and 15.7% were compound. Among these SSRs, the GT/CA repeat motifs were the most common (37.6%) followed by GA/CT repeat motifs (25.9%). The primer pairs could be designed for a total of 170 SSRs and were optimized initially on two genotypes. 104 (61.2%) primer pairs yielded scorable amplicon and 46 (44.2%) primers showed polymorphism among 32 cultivated groundnut genotypes. The polymorphic SSR markers detected 2 to 5 alleles with an average of 2.44 per locus. The polymorphic information content (PIC) value for these markers varied from 0.12 to 0.75 with an average of 0.46. Based on 112 alleles obtained by 46 markers, a phenogram was constructed to understand the relationships among the 32 genotypes. Majority of the genotypes representing subspecies <it>hypogaea </it>were grouped together in one cluster, while the genotypes belonging to subspecies <it>fastigiata </it>were grouped mainly under two clusters.</p> <p>Conclusion</p> <p>Newly developed set of 104 markers extends the repertoire of SSR markers for cultivated groundnut. These markers showed a good level of PIC value in cultivated germplasm and therefore would be very useful for germplasm analysis, linkage mapping, diversity studies and phylogenetic relationships in cultivated groundnut as well as related <it>Arachis </it>species.</p