TESLA-X: An effective method to search for sub-threshold lensed gravitational waves with a targeted population model

Strong gravitational lensing can produce copies of gravitational-wave signals from the same source with the same waveform morphologies but different amplitudes and arrival times. Some of these strongly-lensed gravitational-wave signals can be demagnified and become sub-threshold. We present TESLA-X, an enhanced approach to the original GstLAL-based TargetEd Subthreshold Lensing seArch (TESLA) method, for improving the detection efficiency of these potential sub-threshold lensed signals. TESLA-X utilizes lensed injections to generate a targeted population model and a targeted template bank. We compare the performance of a full template bank search, TESLA, and TESLA-X methods via a simulation campaign, and demonstrate the performance of TESLA-X in recovering lensed injections, particularly targeting a mock event. Our results show that the TESLA-X method achieves a maximum of $\sim 20\%$ higher search sensitivity compared to the TESLA method within the sub-threshold regime, presenting a step towards detecting the first lensed gravitational wave. TESLA-X will be employed for the LIGO-Virgo-KAGRA's collaboration-wide analysis to search for lensing signatures in the fourth observing run

Discovery of a ROCK inhibitor, FPND, which prevents cerebral hemorrhage through maintaining vascular integrity by interference with VE-cadherin

published_or_final_versio

GroundNLQ @ Ego4D Natural Language Queries Challenge 2023

In this report, we present our champion solution for Ego4D Natural Language Queries (NLQ) Challenge in CVPR 2023. Essentially, to accurately ground in a video, an effective egocentric feature extractor and a powerful grounding model are required. Motivated by this, we leverage a two-stage pre-training strategy to train egocentric feature extractors and the grounding model on video narrations, and further fine-tune the model on annotated data. In addition, we introduce a novel grounding model GroundNLQ, which employs a multi-modal multi-scale grounding module for effective video and text fusion and various temporal intervals, especially for long videos. On the blind test set, GroundNLQ achieves 25.67 and 18.18 for R1@IoU=0.3 and R1@IoU=0.5, respectively, and surpasses all other teams by a noticeable margin. Our code will be released at\url{https://github.com/houzhijian/GroundNLQ}.Comment: 5 pages, 2 figures, 4 tables, the champion solution for Ego4D Natural Language Queries Challenge in CVPR 202

Machine Learning-Derived Prenatal Predictive Risk Model to Guide Intervention and Prevent the Progression of Gestational Diabetes Mellitus to Type 2 Diabetes : Prediction Model Development Study

Publisher Copyright: © Mukkesh Kumar, Li Ting Ang, Cindy Ho, Shu E Soh, Kok Hian Tan, Jerry Kok Yen Chan, Keith M Godfrey, Shiao-Yng Chan, Yap Seng Chong, Johan G Eriksson, Mengling Feng, Neerja KarnaniBackground: The increasing prevalence of gestational diabetes mellitus (GDM) is concerning as women with GDM are at high risk of type 2 diabetes (T2D) later in life. The magnitude of this risk highlights the importance of early intervention to prevent the progression of GDM to T2D. Rates of postpartum screening are suboptimal, often as low as 13% in Asian countries. The lack of preventive care through structured postpartum screening in several health care systems and low public awareness are key barriers to postpartum diabetes screening. Objective: In this study, we developed a machine learning model for early prediction of postpartum T2D following routine antenatal GDM screening. The early prediction of postpartum T2D during prenatal care would enable the implementation of effective strategies for diabetes prevention interventions. To our best knowledge, this is the first study that uses machine learning for postpartum T2D risk assessment in antenatal populations of Asian origin. Methods: Prospective multiethnic data (Chinese, Malay, and Indian ethnicities) from 561 pregnancies in Singapore's most deeply phenotyped mother-offspring cohort study-Growing Up in Singapore Towards healthy Outcomes-were used for predictive modeling. The feature variables included were demographics, medical or obstetric history, physical measures, lifestyle information, and GDM diagnosis. Shapley values were combined with CatBoost tree ensembles to perform feature selection. Our game theoretical approach for predictive analytics enables population subtyping and pattern discovery for data-driven precision care. The predictive models were trained using 4 machine learning algorithms: logistic regression, support vector machine, CatBoost gradient boosting, and artificial neural network. We used 5-fold stratified cross-validation to preserve the same proportion of T2D cases in each fold. Grid search pipelines were built to evaluate the best performing hyperparameters. Results: A high performance prediction model for postpartum T2D comprising of 2 midgestation features-midpregnancy BMI after gestational weight gain and diagnosis of GDM-was developed (BMI_GDM CatBoost model: AUC=0.86, 95% CI 0.72-0.99). Prepregnancy BMI alone was inadequate in predicting postpartum T2D risk (ppBMI CatBoost model: AUC=0.62, 95% CI 0.39-0.86). A 2-hour postprandial glucose test (BMI_2hour CatBoost model: AUC=0.86, 95% CI 0.76-0.96) showed a stronger postpartum T2D risk prediction effect compared to fasting glucose test (BMI_Fasting CatBoost model: AUC=0.76, 95% CI 0.61-0.91). The BMI_GDM model was also robust when using a modified 2-point International Association of the Diabetes and Pregnancy Study Groups (IADPSG) 2018 criteria for GDM diagnosis (BMI_GDM2 CatBoost model: AUC=0.84, 95% CI 0.72-0.97). Total gestational weight gain was inversely associated with postpartum T2D outcome, independent of prepregnancy BMI and diagnosis of GDM (P = .02; OR 0.88, 95% CI 0.79-0.98). Conclusions: Midgestation weight gain effects, combined with the metabolic derangements underlying GDM during pregnancy, signal future T2D risk in Singaporean women. Further studies will be required to examine the influence of metabolic adaptations in pregnancy on postpartum maternal metabolic health outcomes. The state-of-the-art machine learning model can be leveraged as a rapid risk stratification tool during prenatal care.Peer reviewe

Improving Highway Work Zone Safety

Highway work zones disrupt normal traffic flow and can create severe safety problems. Due to the rising needs in highway maintenance and construction in the United States, the number of work zones is increasing nationwide. With a total of 1,010 fatalities and more than 40,000 injuries occurring in 2006, improvements in work zone safety are necessary. The three primary objectives of this research project included: 1) to determine the effectiveness of a Portable Changeable Message Sign (PCMS) in reducing vehicle speeds on two-lane, rural highway work zones; 2) to determine the effectiveness of a Temporary Traffic Sign (TTS), (W20-1, “Road Work Ahead”); and 3) to determine motorists’ responses to the signage. To accomplish these objectives, field experiments were conducted at US-36 and US-73 in Seneca and Hiawatha, Kansas, respectively. During the field experiments, an evaluation of the effectiveness of the PCMS was conducted under three different conditions: 1) PCMS on; 2) PCMS off, but still visible; and 3) PCMS removed from the road and out of sight. The researchers also divided the vehicles into three classes (passenger car, truck, and semitrailer) and compared the mean speed change of these classes based on three different sign setups: PCMS on, PCMS off, and the use of the TTS (W20-1, “Road Work Ahead”). A survey was also conducted at the experimental work zones to obtain a general understanding of the motorists’ attitudes as they traveled through the construction areas. Based on the data analysis results, researchers concluded that the presence of the PCMS effectively reduced vehicle speeds on two-lane highway work zones. A slow speed is more likely to reduce the probability of a crash or the severity of a crash. In addition, researchers performed a univariate analysis of the variance test to determine if a significant interaction existed between motorists’ responses and the sign conditions. The results showed a significant interaction between the signs and passenger car vehicles

Evaluation of the Osteogenic Potential of Growth Factorâ Rich Demineralized Bone Matrix In Vivo

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/141502/1/jper0036.pd

Exploring preconception signatures of metabolites in mothers with gestational diabetes mellitus using a non-targeted approach

BackgroundMetabolomic changes during pregnancy have been suggested to underlie the etiology of gestational diabetes mellitus (GDM). However, research on metabolites during preconception is lacking. Therefore, this study aimed to investigate distinctive metabolites during the preconception phase between GDM and non-GDM controls in a nested case-control study in Singapore.MethodsWithin a Singapore preconception cohort, we included 33 Chinese pregnant women diagnosed with GDM according to the IADPSG criteria between 24 and 28 weeks of gestation. We then matched them with 33 non-GDM Chinese women by age and pre-pregnancy body mass index (ppBMI) within the same cohort. We performed a non-targeted metabolomics approach using fasting serum samples collected within 12 months prior to conception. We used generalized linear mixed model to identify metabolites associated with GDM at preconception after adjusting for maternal age and ppBMI. After annotation and multiple testing, we explored the additional predictive value of novel signatures of preconception metabolites in terms of GDM diagnosis.ResultsA total of 57 metabolites were significantly associated with GDM, and eight phosphatidylethanolamines were annotated using HMDB. After multiple testing corrections and sensitivity analysis, phosphatidylethanolamines 36:4 (mean difference beta: 0.07; 95% CI: 0.02, 0.11) and 38:6 (beta: 0.06; 0.004, 0.11) remained significantly higher in GDM subjects, compared with non-GDM controls. With all preconception signals of phosphatidylethanolamines in addition to traditional risk factors (e.g., maternal age and ppBMI), the predictive value measured by area under the curve (AUC) increased from 0.620 to 0.843.ConclusionsOur data identified distinctive signatures of GDM-associated preconception phosphatidylethanolamines, which is of potential value to understand the etiology of GDM as early as in the preconception phase. Future studies with larger sample sizes among alternative populations are warranted to validate the associations of these signatures of metabolites and their predictive value in GDM.Peer reviewe