The Prospects for Immigration Amendments

Obg proteins are a family of P-loop GTPases, conserved from bacteria to human. The Obg protein in Escherichia coli (ObgE) has been implicated in many diverse cellular functions, with proposed molecular roles in two global processes, ribosome assembly and stringent response. Here, using pre-steady state fast kinetics we demonstrate that ObgE is an anti-association factor, which prevents ribosomal subunit association and downstream steps in translation by binding to the 50S subunit. ObgE is a ribosome dependent GTPase; however, upon binding to guanosine tetraphosphate (ppGpp), the global regulator of stringent response, ObgE exhibits an enhanced interaction with the 50S subunit, resulting in increased equilibrium dissociation of the 70S ribosome into subunits. Furthermore, our cryo-electron microscopy (cryo-EM) structure of the 50S? ObgE? GMPPNP complex indicates that the evolutionarily conserved N-terminal domain (NTD) of ObgE is a tRNA structural mimic, with specific interactions with peptidyl-transferase center, displaying a marked resemblance to Class I release factors. These structural data might define ObgE as a specialized translation factor related to stress responses, and provide a framework towards future elucidation of functional interplay between ObgE and ribosome-associated (p) ppGpp regulators. Together with published data, our results suggest that ObgE might act as a checkpoint in final stages of the 50S subunit assembly under normal growth conditions. And more importantly, ObgE, as a (p) ppGpp effector, might also have a regulatory role in the production of the 50S subunit and its participation in translation under certain stressed conditions. Thus, our findings might have uncovered an under-recognized mechanism of translation control by environmental cues

Privacy Protection for Youth Risk Behavior Using Bayesian Data Synthesis: A Case Study to the YRBS

The large number of publicly available survey datasets of wide variety, albeit useful, raise respondent-level privacy concerns. The synthetic data approach to data privacy and confidentiality has been shown useful in terms of privacy protection and utility preservation. This paper aims at illustrating how synthetic data can facilitate the dissemination of highly sensitive information about youth risk behavior by presenting a case study of synthetic data for a sample of the Youth Risk Behavior Survey (YRBS). Given the categorical nature of almost all variables in YRBS, the Dirichlet Process mixture of products of multinomials (DPMPM) synthesizer is adopted to partially synthesize the YRBS sample. Detailed evaluations of utility and disclosure risks demonstrate that the generated synthetic data are able to significantly reduce the disclosure risks compared to the confidential YRSB sample while maintaining a high level of utility

Shared Cycling Demand Prediction during COVID-19 Combined with Urban Computing and Spatiotemporal Residual Network

The regularity and demand predictions of shared cycling are very necessary and challenging for the management and development of urban pedestrian and bicycle traffic. The bicycle-sharing system has the problem of spatial and temporal demand fluctuations and presents a very complex nonlinear regularity. The demand for shared bicycles is affected by many factors, including time, space, weather and the situation of COVID-19. This study proposes a new bicycle-sharing demand forecasting model (USTARN) based on the impact of COVID-19, which combines urban computing and spatiotemporal attention residual network. USTARN consists of two parts. In the first part, a spatiotemporal attention residual network model is established to learn the temporal correlation and spatial correlation of shared bicycle demand. The temporal characteristic branches of each spatial small region are trained, respectively, to predict the shared bicycle demand in batches in different regions and periods according to the historical data. In order to improve the prediction accuracy of the model, the second part of the model adjusts and redistributes the prediction results of the first part by learning other information of the city, such as the severity of COVID-19, weather, temperature, wind speed and holidays. It can predict the demand for shared bicycles in different urban areas in different periods and different severities of COVID-19. This study uses the order data of shared bicycles during the period of COVID-19 in 2020 obtained from the open data platform of Shenzhen municipal government as verification, analyzes the spatiotemporal regularity of the system demand and discusses the impact of the number of newly diagnosed patients and the daily minimum temperature on the demand for shared bicycles. The results show that USTARN can fully reflect time, space, the epidemic situation, weather and temperature, and the prediction results of the impact of wind speed and other factors on the demand for shared bicycles are better than the classical methods

Asymptotic distributions of impulse response functions in short panel vector autoregressions

This paper establishes the asymptotic distributions of the impulse response functions in panel vector autoregressions with a fixed time dimension. It also proves the asymptotic validity of a bootstrap approximation to their sampling distributions. The autoregressive parameters are estimated using the GMM estimators based on the first differenced equations and the error variance is estimated using an extended analysis-of-variance type estimator. Contrary to the time series setting, we find that the GMM estimator of the autoregressive coefficients is not asymptotically independent of the error variance estimator. The asymptotic dependence calls for variance correction for the orthogonalized impulse response functions. Simulation results show that the variance correction improves the coverage accuracy of both the asymptotic confidence band and the studentized bootstrap confidence band for the orthogonalized impulse response functions.Asymptotic distribution Bootstrap Nonorthogonalized impulse response function Orthogonalized impulse response function Panel data Vector autoregressions

Associations of medical conditions, lifestyle and unintentional weight loss in early old age: The 1946 British Birth Cohort.

BackgroundUnintentional weight loss in older people has been linked to increased risk of mortality. We aimed to investigate common medical conditions and lifestyle factors, including body fat distribution, as potential determinants of recent and prospective unintentional weight loss in early old age.MethodsFrom the Medical Research Council (MRC) National Survey of Health and Development (NSHD), we included a total of 2234 study members aged 60-64 with information on unintentional weight loss in 2006-2010. Of these, 2136 also had information on unintentional weight loss recorded in 2015. Logistic regression was conducted to examine the associations between medical conditions, lifestyle, and body fat distribution at age 60-64 and unintentional weight loss at age 60-64 and 69.ResultsA total of 109 of 2234 study members had unintentional weight loss at ages 60-64, and 166 of 2136 at age 69. Never smoking was associated with lower risk of unintentional weight loss at age 60-64 (OR = 0.29, 95%CI = 0.12-0.68 compared to current smokers), and this association remained when adjusted for other determinants. Greater waist-hip ratio (OR = 0.95, 95%CI = 0.91-0.99) and body fat-lean mass ratio (OR = 0.96, 95%CI = 0.94-0.99) were associated with less likelihood of unintentional weight loss at age 60-64. Never smoking and greater hip circumference at age 60-64 were associated with lower odds of unintentional weight loss at age 69.ConclusionsSmoking status and body fat distribution may help identify those at risk of unintentional weight loss in early old age. Their benefit in interventions to prevent age-associated weight loss needs to be further investigated

Comprehensive Analysis of the GRAS Gene Family in <i>Paulownia fortunei</i> and the Response of DELLA Proteins to Paulownia Witches’ Broom

The GRAS (GAI\RGA\SCL) gene family encodes plant-specific transcription factors that play crucial roles in plant growth and development, stress tolerance, and hormone network regulation. Plant dwarfing symptom is mainly regulated by DELLA proteins of the GRAS gene subfamily. In this study, the association between the GRAS gene family and Paulownia witches’ broom (PaWB) was investigated. A total of 79 PfGRAS genes were identified using bioinformatics methods and categorized into 11 groups based on amino acid sequences. Tandem duplication and fragment duplication were found to be the main modes of amplification of the PfGRAS gene family. Gene structure analysis showed that more than 72.1% of the PfGRASs had no introns. The genes PfGRAS12/18/58 also contained unique DELLA structural domains; only PfGRAS12, which showed significant response to PaWB phytoplasma infection in stems, showed significant tissue specificity and responded to gibberellin (GA3) in PaWB-infected plants. We found that the internodes were significantly elongated under 100 µmol·L−1 GA3 treatment for 30 days. The subcellular localization analysis indicated that PfGRAS12 is located in the nucleus and cell membrane. Yeast two-hybrid (Y2H) and bimolecular fluorescence complementation (BiFC) assays confirmed that PfGRAS12 interacted with PfJAZ3 in the nucleus. Our results will lay a foundation for further research on the functions of the PfGRAS gene family and for genetic improvement and breeding of PaWB-resistant trees

DreamDiffusion: Generating High-Quality Images from Brain EEG Signals

This paper introduces DreamDiffusion, a novel method for generating high-quality images directly from brain electroencephalogram (EEG) signals, without the need to translate thoughts into text. DreamDiffusion leverages pre-trained text-to-image models and employs temporal masked signal modeling to pre-train the EEG encoder for effective and robust EEG representations. Additionally, the method further leverages the CLIP image encoder to provide extra supervision to better align EEG, text, and image embeddings with limited EEG-image pairs. Overall, the proposed method overcomes the challenges of using EEG signals for image generation, such as noise, limited information, and individual differences, and achieves promising results. Quantitative and qualitative results demonstrate the effectiveness of the proposed method as a significant step towards portable and low-cost ``thoughts-to-image'', with potential applications in neuroscience and computer vision.Comment: 8 pages, 7 figure

An acetal-based polymeric crosslinker with controlled pH-sensitivity

.An acetal based polymeric cross-linker with controlled pH-sensitivity was used for the synthesis of collagen hydrogels and sponges. The novel cross-linker was synthesized using DE-ATRP and was more biocompatible compared to the commercial 4-star PEG.</p

Evaluating the Influence of Different Layouts of Residential Buildings on the Urban Thermal Environment

Urban residential building layouts have an impact on air temperature and thermal comfort. Research has shown that poorly designed building layouts can lead to thermal discomfort. Thus, it is crucial to analyze the relationship between residential building layouts and air temperature. We used the ENVI-met 3D microclimate model to simulate six typical residential building layouts and explore the diurnal and seasonal variations in air temperature. In addition, we used the physiological equivalent temperature (PET) as the evaluation index for the thermal comfort of different building layouts. The diurnal results showed that the air temperature of the parallel layout rose faster and fell faster, and these changes were more significant in summer. The results of the air temperature classifications indicated that the frequency of low-air-temperature areas in the parallel layout is approximately 12% smaller than that of the enclosed and semi-enclosed layouts, and the high-air-temperature area frequency is 11% higher than that of the enclosed and semi-enclosed layouts in summer. In winter, the frequency of low-air-temperature areas in the parallel layout is approximately 7% smaller than that of the enclosed and semi-enclosed layouts, and the high-air-temperature area frequency is 5% higher than that of the enclosed and semi-enclosed layouts. In combination with the PET results, we found that the enclosed layout is the optimal configuration. Moreover, in some cases, increased building height and vegetation lead to a reduction in air temperature

Long-Term-Stable Near-Infrared Polymer Dots with Ultrasmall Size and Narrow-Band Emission for Imaging Tumor Vasculature <i>in Vivo</i>

Fluorescent nanoprobes have become one of the most promising classes of materials for cancer imaging. However, there remain many unresolved issues with respect to the understanding of their long-term colloidal stability and photostability in both biological systems and the environment. In this study, we report long-term-stable near-infrared (NIR) polymer dots for <i>in vivo</i> tumor vasculature imaging. NIR-emitting polymer dots were prepared by encapsulating an NIR dye, silicon 2,3-naphthalo­cyanine bis­(trihexyl­silyloxide) (NIR775), into a matrix of polymer dots, poly­[2-methoxy-5-(2-ethyl­hexyloxy)-1,4-phenylene­vinylene] (MEH-PPV), using a nanoscale precipitation method. The prepared NIR polymer dots were sub-5 nm in diameter, exhibited narrow-band NIR emission at 778 nm with a full width at half-maximum of 20 nm, and displayed a large Stokes shift (>300 nm) between the excitation and emission maxima. In addition, no significant uptake of the prepared NIR polymer dots by either human glioblastoma U87MG cells or human non-small cell lung carcinoma H1299 cells was detected. Moreover, these NIR polymer dots showed long-term colloidal stability and photostability in water at 4 °C for at least 9 months, and were able to image vasculature of xeno­grafted U87MG tumors in living mice after intra­venous injection. These results thus open new opportunities for the development of whole-body imaging of mice based on NIR polymer dots as fluorescent nanoprobes