Role of Hydrogen Bonding in Green Fluorescent Protein-like Chromophore Emission.

The fluorescence emission from green fluorescent protein (GFP) is known to be heavily influenced by hydrogen bonding between the core fluorophore and the surrounding side chains or water molecules. Yet how to utilize this feature for modulating the fluorescence of GFP chromophore or GFP-like fluorophore still remains elusive. Here we present theoretical calculations to predict how hydrogen bonding could influence the excited states of the GFP-like fluorophores. These studies provide both a new perspective for understanding the photophysical properties of GFP as well as a solid basis for the rational design of GFP-based fluorophores

Identifying Mis-Configured Author Profiles on Google Scholar Using Deep Learning

Google Scholar has been a widely used platform for academic performance evaluation and citation analysis. The issue about the mis-configuration of author profiles may seriously damage the reliability of the data, and thus affect the accuracy of analysis. Therefore, it is important to detect the mis-configured author profiles. Dealing with this issue is challenging because the scale of the dataset is large and manual annotation is time-consuming and relatively subjective. In this paper, we first collect a dataset of Google Scholar's author profiles in the field of computer science and compare the mis-configured author profiles with the reliable ones. Then, we propose an integrated model that utilizes machine learning and node embedding to automatically detect mis-configured author profiles. Additionally, we conduct two application case studies based on the data of Google Scholar, i.e., outstanding scholar searching and university ranking, to demonstrate how the improved dataset after filtering out the mis-configured author profiles will change the results. The two case studies validate the importance and meaningfulness of the detection of mis-configured author profiles.Peer reviewe

Using Multiple Statistical Methods to Derive Dietary Patterns Associated with Cardiovascular Disease in Patients with Type 2 Diabetes: Results from a Multiethnic Population-Based Study

Background. There are few reports on the relationship between dietary patterns and cardiovascular disease (CVD) risk in patients with type 2 diabetes (T2D). This study aimed to explore relationships between dietary patterns and CVD risk in the T2D population using multiple statistical analysis methods. Methods. A total of 2,984 patients with T2D from the Xinjiang Multi-Ethnic Cohort, 555 of whom were suffering from CVD, were enrolled in this study. Participants’ dietary intake was measured by the semiquantitative food frequency questionnaire (FFQ). Three statistical methods were used to construct dietary patterns, including principal component analysis (PCA) method, reduced-rank regressions (RRR) method, and partial least-squares regression (PLS) method. Then, the association between dietary patterns and CVD risk in T2D patients was analyzed by logistic regression. After excluding participants with CVD, the associations between dietary patterns and 10-year CVD risk scores were subsequently evaluated to reduce reverse causality. Results. In this study, four dietary patterns were identified by three methods. Adjustment for confounding factors, subjects with the highest scores on the “high-protein and high-carbohydrate” patterns derived from PCA, RRR, and PLS had higher odds of CVD than those with the lowest scores (OR: 2.89, 95% CI: 2.11–3.96, P t r e n d < 0.001 ; OR: 2.96, 95% CI: 2.17–4.03, P t r e n d < 0.001 ; OR: 2.01, 95% CI: 1.50–2.70, P t r e n d < 0.001 , respectively). However, the dietary pattern of PCA-prudent was not significantly related to the odds of having CVD in T2D patients (adjusted ORQ4vsQ1: 0.93, 95% CI: 0.70–1.24, P t r e n d = 0.474 ). Interestingly, we also found significant associations between “high-protein and high-carbohydrate” patterns and the elevated predicted 10-year CVD risk in T2D patients (all P t r e n d < 0.05 ). Conclusion. The positive correlation between “high-protein and high-carbohydrate” patterns and CVD risk in T2D patients was robust across all three data-driven approaches. These findings may have public health significance, encouraging an emphasis on food choices in the usual diet and promoting nutritional interventions for patients with T2D to prevent CVD

Enhanced photovoltage in perovskite-type artificial superlattices on Si substrates

Abstract We have fabricated a three-component perovskite-type superlattice (SL) consisting of La 0.9 Sr 0.1 MnO 3 , SrTiO 3 and LaAlO 3 with atomic scale control by laser molecular beam epitaxy on Si substrates. When a He-Ne laser irradiated the superlattice by side illumination, a stable photovoltage was produced and the responsivity reached 46.7 mV mW −1 which is six times higher than that of a similarly grown La 0.9 Sr 0.1 MnO 3 single layer on Si substrates. This work demonstrates the potential of the present SL in photo-detectors operating at room temperature

Low divergence single-mode surface-emitting concentric-circular-grating terahertz quantum cascade lasers

We report the design, fabrication and experimental characterization of surface-emitting terahertz (THz) frequency quantum cascade lasers (QCLs) with distributed feedback concentric-circular-gratings. Single-mode operation is achieved at 3.73 THz with a side-mode suppression ratio as high as ~30 dB. The device emits ~5 times the power of a ridge laser of similar dimensions, with little degradation in the maximum operation temperature. Two lobes are observed in the far-field emission pattern, each of which has a divergence angle as narrow as ~13.5° × 7°. We demonstrate that deformation of the device boundary, caused by anisotropic wet chemical etching is the cause of this double-lobed profile, rather than the expected ring-shaped pattern

Adaptive in vivo device for theranostics of inflammation: Real-time monitoring of interferon-γ and aspirin

Cytokines mediate and control immune and inflammatory responses. Complex interactions exist among cytokines, inflammation, and the innate and adaptive immune responses in maintaining homeostasis, health, and well-being. On-demand, local delivery of anti-inflammatory drugs to target tissues provides an approach for more effective drug dosing while reducing the adverse effects of systemic drug delivery. This work demonstrates a proof-of-concept theranostic approach for inflammation based on analyte-kissing induced signaling, whereby a drug (in this report, aspirin) can be released upon the detection of a target level of a proinflammatory cytokine (i.e., interferon-γ (IFN-γ)) in real time. The structure-switching aptamer-based biosensor described here is capable of quantitatively and dynamically detecting IFN-γ both in vitro and in vivo with a sensitivity of 10 pg mL−1. Moreover, the released aspirin triggered by the immunoregulatory cytokine IFN-γ is able to inhibit inflammation in a rat model, and the release of aspirin can be quantitatively controlled. The data reported here provide a new and promising strategy for the in vivo detection of proinflammatory cytokines and the subsequent therapeutic delivery of anti-inflammatory molecules. This universal theranostic platform is expected to have great potential for patient-specific personalized medicine. Statement of Significance: We developed an adaptive in vivo sensing device whereby a drug, aspirin, can be released upon the detection of a proinflammatory cytokine, interferon-γ (IFN-γ), in real time with a sensitivity of 10 pg mL−1. Moreover, the aspirin triggered by IFN-γ depressed inflammation in the rat model and was delivered indirectly through blood and cerebrospinal fluid or directly to the inflammation tissue or organ without adverse gastrointestinal effects observed in the liver and kidney. We envision that, for the first time, patients with chronic inflammatory disease can receive the right intervention and treatment at the right time. Additionally, this technology may empower patients to monitor their personalized health and disease management program, allowing real-time diagnostics, disease monitoring, and precise and effective treatments