Miracle-dz/UniFAR-FTUs huggingface dataset:
Years : A Cluster-Randomized , Controlled Trial Effects of Promoting Longer-Term and Exclusive Breastfeeding on Cardiometabolic Risk

Natalia Sergeichick, Nina Gusina, Ying Foo, Tom Palmer, Jennifer Thompson, Matthew W. Richard M. Martin, Rita Patel, Michael S. Kramer, Konstantin Vilchuck, Natalia Bogdanovich, Factors at Age 11.5 Years: A Cluster-Randomized, Controlled Trial Effects of Promoting Longer-Term and Exclusive Breastfeeding on Cardiometabolic Risk Print ISSN: 0009-7322. Online ISSN: 1524-4539 Copyright © 2013 American Heart Association, Inc. All rights reserved. is published by the American Heart Association, 7272 Greenville Avenue, Dallas, TX 75231 Circulation doi: 10.1161/CIRCULATIONAHA.113. 00516
Modeling and monitoring of a multivariate spatio-temporal network system

Abstract With the development of information technology, various network systems are created to connect physical objects and people by sensor nodes or smart devices, providing unprecedented opportunities to realize automated interconnected systems and revolutionize people’s lives. However, network systems are vulnerable to attacks, due to the integration of physical objects and human behaviors as well as the complex spatio-temporal correlated structures of the network systems. Therefore, how to accurately and effectively model and monitor a network system is critical to ensure information security and support system automation. To address this issue, this article develops a multivariate spatio-temporal modeling and monitoring methodology for a network system by using multiple types of sensor signals collected from the network system. We first propose a Multivariate Spatio-Temporal Autoregressive (MSTA) model by integrating a Gaussian Markov Random Field and a vector autoregressive model structure to characterize the spatio-temporal correlation of the network system. In particular, we develop an iterative model learning algorithm that integrates the Bayesian inference, least squares, and a sum square error-based optimization method to learn the network structure and estimate parameters in the MSTA model. Then, we propose two spatio-temporal control schemes to monitor the network system based on the MSTA model. Numerical experiments and a real case study of an IoT network system are presented to validate the performance of the proposed method.
Multilevel feature fusion and edge optimization network for self-supervised monocular depth estimation

Abstract. Monocular depth estimation is an essential step in scene geometry understanding. However, the depth maps predicted by the existing methods have the problems of loss of small target details and blurred edges. To this end, we propose a monocular depth estimation method based on multilevel feature fusion and edge optimization to obtain depth maps with rich depth details and precise edges. First, we improved the encoder part to make it better adapted for the depth estimation task. In addition, we fill the U-shaped network with a dense feature fusion layer (Dense-FL) to capture global context information and then combine the proposed structure attention module to further enhance the obtained feature information, so the depth map contains more detailed information. Finally, we design an edge optimization module to incorporate edge features into the training process and combine the proposed reweighted loss and image edge detail loss to constrain the network, so as to further improve the learning ability of the model to the edge of the object. The experimental results on the KITTI dataset show that the depth prediction results obtained by our method perform better at small targets and the edges of objects in the scene, and the structure of the objects is more complete. Generalization experiments on the Cityscapes and Sceneflow datasets also further confirmed the effectiveness and superiority of our proposed method.
Corn silk aqueous extracts and intraocular pressure of systemic and non-systemic hypertensive subjects

Background Hypotensive properties have been attributed to the stigma/style of Zea mays L (corn silk). Although the effect of corn silk extract on blood pressure has been documented in animal studies, we are not aware of any study on its effect on human blood pressure and intraocular pressure. Methods A randomised study was carried out on the effect of water only, masked doses of corn silk aqueous extract (60, 130, 192.5 and 260 mg/kg body weight) on intraocular pressure and blood pressure of 20 systemic and 20 non-systemic hypertensive subjects. Intraocular pressure and blood pressure were measured at baseline and every hour for eight hours after administering water or a masked dose of corn silk aqueous extract. Each dose was administered at two-week intervals to each subject in the two study groups. Results The results showed that the last three doses of corn silk aqueous extract gave a statistically significant reduction (p < 0.001) in mean intraocular pressure and blood pressure within eight hours of administration. The peak effect on intraocular pressure was observed after four hours and this was preceded by the peak effect on blood pressure, which occurred after three hours of administration. The hypotensive effect was dose-dependent in the two groups. Conclusion Corn silk aqueous extract has a lowering effect on intraocular pressure in systemic and non-systemic hypertensive subjects. This may have resulted from the fall in blood pressure that is due to potassium-induced natriuresis and diuresis caused by the high potassium content in the high doses of the corn silk extract.
Prognosis of White-Coat and Masked Hypertension: International Database of Home Blood Pressure in Relation to Cardiovascular Outcome

Home blood pressure monitoring is useful in detecting white-coat and masked hypertension and is recommended for patients with suspected or treated hypertension. The prognostic significance of white-coat and masked hypertension detected by home measurement was investigated in 6458 participants from 5 populations enrolled in the International Database of HOme blood pressure in relation to Cardiovascular Outcomes. During a median follow-up of 8.3 years, 714 fatal plus nonfatal cardiovascular events occurred. Among untreated subjects (n=5007), cardiovascular risk was higher in those with white-coat hypertension (adjusted hazard ratio 1.42; 95% CI [1.06–1.91]; P=0.02), masked hypertension (1.55; 95% CI [1.12–2.14]; P<0.01) and sustained hypertension (2.13; 95% CI [1.66–2.73]; P<0.0001) compared with normotensive subjects. Among treated patients (n=1451), the cardiovascular risk did not differ between those with high office and low home blood pressure (white-coat) and treated controlled subjects (low office and home blood pressure; 1.16; 95% CI [0.79–1.72]; P=0.45). However, treated subjects with masked hypertension (low office and high home blood pressure; 1.76; 95% CI [1.23–2.53]; P=0.002) and uncontrolled hypertension (high office and home blood pressure; 1.40; 95% CI [1.02–1.94]; P=0.04) had higher cardiovascular risk than treated controlled patients. In conclusion, white-coat hypertension assessed by home measurements is a cardiovascular risk factor in untreated but not in treated subjects probably because the latter receive effective treatment on the basis of their elevated office blood pressure. In contrast, masked uncontrolled hypertension is associated with increased cardiovascular risk in both untreated and treated patients, who are probably undertreated because of their low office blood pressure.
Social Class-Based Discrimination and Psychological Symptoms Among Socioeconomically Disadvantaged College Students: The Moderated Mediation Role of Stress Mindset and Rumination

Discrimination as a crucial stressor damages the mental health of socioeconomically disadvantaged individuals through increased ruminative thinking. A “stress-is-enhancing” mindset may protect the mental health of socioeconomically disadvantaged individuals under the pressures of perceived discrimination and rumination. This study examined the mediating role of rumination and the moderating role of stress mindset in the relationship between perceived discrimination and psychological symptoms among socioeconomically disadvantaged college students. A total of 919 socioeconomically disadvantaged undergraduate students (48.4% female, ages 17–25) were recruited. The results indicated that perceived discrimination was positively associated with psychological symptoms among socioeconomically disadvantaged undergraduate students through rumination (B = 0.11, boot SE = 0.01, boot 95% CIs = [0.08, 0.13]). Importantly, stress mindset moderated the indirect association between perceived discrimination and psychological distress through rumination (B = −0.18, boot SE = 0.08, boot 95% CIs = [−0.32, −0.03]). Specifically, compared with individuals with low levels of the stress-is-enhancing mindset, the indirect effect of perceived discrimination on psychological distress through rumination was weaker among individuals with high levels of the stress-is-enhancing mindset. The findings provide support for future intervention practice to promote a stress-is-enhancing mindset to protect the mental health of socioeconomically disadvantaged college students under the pressures of perceived discrimination and rumination.
On-line Speech Enhancement by Time-Frequency Masking under Prior Knowledge of Source Location

can extract only target signals with constraints on source localization in on-line systems. The proposed system is a kind of methods for enhancing a target signal and suppressing other interference signals. But, the performance of proposed system is superior to any other methods and the extraction of target source is comparatively complete. The method has a beamforming concept and uses an improved time-frequency (TF) mask-based BSS algorithm to separate a target signal from multiple noise sources. The target sources are assumed to be in front and test data was recorded in a reverberant room. The experimental results of the proposed method was evaluated by the PESQ score of real-recording sentences and showed a noticeable speech enhancement.
Gene Expression and Pharmacodynamic-Induced Changes in 1 , 760 SLE Patients from Two Phase III Trials of B Cell Activating Factor Blockade with Tabalumab

Dr. Hoffman is a full-time employee and stockholder of Eli Lilly and Company. Dr. Merrill has received consulting fees, speaking fees, and/or honoraria from Eli Lilly and Company (less than $10,000 each). Dr. Alarcón-Riquelme has received consulting fees, speaking fees, and/or honoraria from Eli Lilly and Company (less than $10,000 each). Dr. Petri has received consulting fees from Eli Lilly (less than $10,000 each). Dr. Dow is a full-time employee and stockholder of Eli Lilly and Company. Dr. Nisenbaum is a fulltime employee and stockholder of Eli Lilly and Company. Dr. Schroeder is a full-time employee and stockholder of Eli Lilly and Company. Ms. Komocsar is a full-time employee and stockholder of Eli Lilly and Company. Dr. Perumal is a full-time employee and stockholder of Eli Lilly and Company. Dr. Linnik is a full-time employee and stockholder of Eli Lilly and Company. Dr. Airey is a full-time employee of Eli Lilly and Company. Dr. Liu is a full-time employee and stockholder of Eli Lilly and Company. Dr. Rocha is a full-time employee and stockholder of Eli Lilly and Company. Dr. Higgs is a full-time employee and stockholder of Eli Lilly and Company.
Photon Doppler velocimetry surface return study for common surface preparations

Abstract. Photon Doppler velocimetry (PDV) is a ubiquitous diagnostic method in dynamic compression experiments. It typically requires the roughening of the test surface to ensure scattering of the probe light back into the PDV probe at non-normal angles of incidence. Although surface scattering plays an essential role in the quality of PDV data, surface scattering properties for common materials and preparations have not previously been studied with specific relevance to PDV. Here, we present detailed measurements of the bidirectional reflectance distribution function for four different surface preparations (Scotch-Brite hand and drill roughened, ball-rolled, and grit blasted) and four different materials (copper, aluminum, stainless steel, and tantalum). These measurements employ a conventional PDV probe and obtain a diffraction limited angular resolution with 10 pW accuracy and 2.6% repeatability. In addition to scattering data, we employ scattering theory and simulations to accurately emulate the measured data. We also present a straightforward method to derive the average scattering distribution from surface profilometry and observe several qualitative aspects of the scattering data that may help to optimize PDV signals.
Aerosol Distributions and Transport over Southern Morocco from Ground-Based and Satellite Observations (2004–2020)

The present study investigates the optical properties of aerosols on daily and seasonal scales with the use of the aerosol optical depth (AOD) and Angström exponent (AE) data retrieved from AErosol RObotic NETwork (AERONET) and collected at four stations in Southern Morocco—Saada (31.63° N; 8.16° W), Ouarzazate (30.93° N; 6.91° W), Oukaïmeden (31.21° N; 7.86° W) and Ras-El-Aïn (31.67° N; 7.60° W). An evaluation of the aerosol volumetric size distribution (AVSD) is also obtained for Saada and Ouarzazate. An AOD inter-comparison is performed between AERONET data and satellite sensors (MODerate resolution Imaging Spectroradiometer—MODIS), as well as assimilation products (Modern-Era Retrospective analysis for Research and Applications, version 2 (MERRA-2) and Copernicus Atmosphere Monitoring Service (CAMS)), by the means of a linear regression. Regardless of site location and elevation, the results show the prevalence of the annual cycle of AOD, with a maximum in summer and a minimum in winter. In association with this seasonal variation, the variations in AE and AVSD showed an increase in coarse mode over Ouarzazate and Saada during summer (July to August), underlining that Southern Morocco is prone to the regular transport of desert dust on a seasonal basis. The inter-comparison reveals that the MERRA-2 dataset is slightly more appropriate for the study region, since it shows correlation coefficients (r) ranging from 0.758 to 0.844 and intercepts ranging from 0.021 to 0.070, depending on the study site. The statistical analysis of the back-trajectories simulated by the HYbrid Single Particle Lagrangian Integrated Trajectory (HYSPLIT) model were consistent with the observations and confirmed the dominance of desert dust aerosols during the summer over the study region. On the other hand, the winter season reveals a predominance of anthropogenic and oceanic aerosols originating from the north and the west of the study site.
Toward a Participatory Digital Ethnography of Blockchain Governance

Blockchain governance occurs through a combination of social and technical activities, involving smart contracts, deliberation within a group, and voting. These processes are significant as they demonstrate how governance of distributed infrastructures is evolving. While typologies of blockchain governance can be constructed by gathering on-chain interactions and formal rules, other aspects are more difficult to observe, including governance interactions occurring inside discussion forums. In this article, we discuss a participatory digital ethnography technique, whereby participants and researchers use a bespoke bot to identify governance interactions occurring within project forums (on Discord). The technique is designed to be used in conjunction with the analysis of software for the purpose of mapping and understanding the “governance surface” of different protocols. We describe our tools and methods for understanding automated futures through a case study of the SourceCred community, an organization using, developing, and maintaining open source software called SourceCred. The SourceCred codebase is also used by other decentralized communities for various organizational functions, including reputation and compensation.
Medication adherence and patient satisfaction among low socioeconomic hypertensive older people visiting a secondary care public hospital in south India

This study aimed to assess the medication adherence and treatment satisfaction in ageing hypertensive patients. It also evaluated the prevalence and potential predictors of nonadherence. The present observational study was conducted at a public secondary care hospital. Hypertensive patients of either sex aged >60 years visiting outpatient departments and hospital pharmacies were recruited into the study. Adherence and treatment satisfaction in the study population was measured using the Morisky Medication Adherence Scale and the Medication Interview Satisfaction Scale (MISS-21), respectively. A total of 202 aged hypertensive patients participated in the study. Majority of the study population had a low socioeconomic status and were within the age group of 60–65 years. Most of the patients reported good treatment satisfaction in all the domains of MISS-21. The prevalence of nonadherence (low to medium) was found to be 61.38%. Upon execution of bivariate analysis, elevated blood pressure, polypharmacy, and nonvegetarian diet were identified as predictors for nonadherence in the study population ( p < 0.05). Multiple factors, like patient’s mental status, health literacy, comorbidities, and drug-related problems, affect medication adherence behavior. Geriatric patients need to be specially addressed at hospitals regarding the advantages of treatment adherence. In the Indian set-up, there is an increased responsibility on all the healthcare professionals when dealing with ageing patients with chronic diseases.
Regimens Superiority of Combination Antimicrobial KPC-Producing Klebsiella pneumoniae : Treatment Outcome of Bacteremia Due to

Published Ahead of Print 17 January 2012. 10.1128/AAC.06268-11. 2012, 56(4):2108. DOI: Antimicrob. Agents Chemother. Polsky, Jennifer M. Adams-Haduch and Yohei Doi Mary C. Kilayko, Gabriel Sandovsky, Emilia Sordillo, Bruce Zubair A. Qureshi, David L. Paterson, Brian A. Potoski, Regimens Superiority of Combination Antimicrobial KPC-Producing Klebsiella pneumoniae: Treatment Outcome of Bacteremia Due to
Prediction Model for Daily Reference Crop Evapotranspiration Based on Hybrid Algorithm in Semi-Arid Regions of China

The accurate estimation of reference crop evapotranspiration (ETO) plays an important role in guiding regional water resource management and crop water content research. In order to improve the accuracy of ETO prediction in regions with missing data, this study used the partial correlation analysis method to select factors that have a large impact on ETO as input combinations to construct ETO estimation models for typical stations in semi-arid regions of China. A biological heuristic optimization algorithm (Golden Eagle optimization algorithm (GEO) and Sparrow optimization algorithm (SSA)) and Extreme Learning Machine model (ELM) were combined to improve the estimation accuracy. The results showed that Ra was the primary factor affecting the ETO model, with an importance range of 0.187–0.566. Compared with the independent ELM model, the hybrid model has higher accuracy and stability. The estimated value of the SSA-ELM model under five-factor input condition (Ra, RH, Tmax, Tmin, U2) is closest to the standard value calculated by FAO56 PM: RMSE = 0.067–0.085, R2 = 0.998–0.999, MAE = 0.050–0.066 and NSE = 0.998–0.999. In general, the combination of a partial correlation analysis algorithm and a hybrid model can be used to estimate ETO with high accuracy under the condition of reducing input factors. Use of the first five factors extracted from the partial correlation analysis algorithm as input to build an ETO estimation model based on SSA-ELM in China’s semi-arid regions is recommended, which can also provide a reference for ETO estimation in similar regions.
A Sparse Mixture-of-Experts Model With Screening of Genetic Associations to Guide Disease Subtyping

Motivation: Identifying new genetic associations in non-Mendelian complex diseases is an increasingly difficult challenge. These diseases sometimes appear to have a significant component of heritability requiring explanation, and this missing heritability may be due to the existence of subtypes involving different genetic factors. Taking genetic information into account in clinical trials might potentially have a role in guiding the process of subtyping a complex disease. Most methods dealing with multiple sources of information rely on data transformation, and in disease subtyping, the two main strategies used are 1) the clustering of clinical data followed by posterior genetic analysis and 2) the concomitant clustering of clinical and genetic variables. Both of these strategies have limitations that we propose to address. Contribution: This work proposes an original method for disease subtyping on the basis of both longitudinal clinical variables and high-dimensional genetic markers via a sparse mixture-of-regressions model. The added value of our approach lies in its interpretability in relation to two aspects. First, our model links both clinical and genetic data with regard to their initial nature (i.e., without transformation) and does not require post-processing where the original information is accessed a second time to interpret the subtypes. Second, it can address large-scale problems because of a variable selection step that is used to discard genetic variables that may not be relevant for subtyping. Results: The proposed method was validated on simulations. A dataset from a cohort of Parkinson’s disease patients was also analyzed. Several subtypes of the disease and genetic variants that potentially have a role in this typology were identified. Software availability: The R code for the proposed method, named DiSuGen, and a tutorial are available for download (see the references).
Multimodal data fusion for systems improvement: A review

Abstract In recent years, information available from multiple data modalities has become increasingly common for industrial engineering and operations research applications. There have been a number of research works combining these data in unsupervised, supervised, and semi-supervised fashions that have addressed various issues of combining heterogeneous data, as well as several existing open challenges that remain to be addressed. In this review paper, we provide an overview of some methods for the fusion of multimodal data. We provide detailed real-world examples in manufacturing and medicine, introduce early, late, and intermediate fusion, as well as discuss several approaches under decomposition-based and neural network fusion paradigms. We summarize the capabilities and limitations of these methods and conclude the review article by discussing the existing challenges and potential research opportunities.