Results 161 to 170 of about 161,416 (264)

Nutritional Interventions During Pregnancy Improve Maternal and Neonatal Outcomes: A Systematic Review and Meta‐Analysis of Randomized Controlled Trials

Journal of Midwifery &Women's Health, EarlyView.
Introduction This systematic review and meta‐analysis aimed to evaluate the effects of nutritional interventions during pregnancy on maternal and neonatal outcomes. Methods The review followed Preferred Reporting Items for Systematic Reviews and Meta‐Analyses guidelines and was registered in PROSPERO (CRD42024528918).
Güleren Sabuncular, Elif Akbaş, Elif Uca, Rabia Bağlayici, Döndü Kurnaz, Şule Aktaç   +5 more
wiley   +1 more source

Age-Related Time-to-Onset Analysis of Drug-Associated Erythema Multiforme Using the Japanese Adverse Drug Event Report Database. [PDF]

Cureus
Tanaka H, Nakao S, Sugishita K, Yamazaki T, Ito S, Kageyama K, Sato M, Ino Y, Iguchi K, Nakamura M.   +9 more
europepmc   +1 more source

Product Introduction Events and Stock Returns: A Meta‐Analysis

Journal of Product Innovation Management, EarlyView.
ABSTRACT Academic Summary Product introduction events are frequent across industries, countries, and other contexts and part of the innovation strategy of many companies. However, the future performance of new products is uncertain, posing significant risks for firms and investors and leading to heterogeneous stock market responses.
Niklas Grube, Erik Maier, Tammo Bijmolt
wiley   +1 more source

Impact of reduced ¹⁸F-MK6240 PET/MR acquisition duration on image quality and tau pathology assessment in patients with cognitive impairment. [PDF]

EJNMMI Phys
Liao K, Li J, Jiao F, Zhang Z, Xu L, Wang Z, Li W, Pang H.   +7 more
europepmc   +1 more source

On Testing for Independence Between Generalized Error Models of Several Time Series

Journal of Time Series Analysis, EarlyView.
ABSTRACT We define generalized innovations associated with generalized error models having arbitrary distributions, that is, distributions that can be mixtures of continuous and discrete distributions. These models include stochastic volatility models and regime‐switching models with possibly zero‐inflated regimes.
Kilani Ghoudi, Bouchra R. Nasri, Bruno N. Rémillard   +2 more
wiley   +1 more source

Multivariate Birnbaum-Saunders accelerated lifetime regression model: estimation and residual analysis. [PDF]

Sci Rep
Oliveira MI, Cysneiros FJA, Barros M.
europepmc   +1 more source

Moving Aggregate Modified Autoregressive Copula‐Based Time Series Models (MAGMAR‐Copulas)

Journal of Time Series Analysis, EarlyView.
ABSTRACT Copula‐based time series models can model univariate and stationary time series in a flexible way by decomposing the joint distribution of consecutive observations into a copula and the stationary distribution. Implicitly, this approach assumes a finite Markov order. In reality, a time series may not follow the Markov property.
Sven Pappert
wiley   +1 more source

Spatial and temporal description of antimalarial drug resistance markers in Ghana using targeted amplicon deep sequencing. [PDF]

Antimicrob Agents Chemother
Tandoh KZ, Bruku SA, Opoku-Agyeman P, Ameyaw P, Baafi E, Frimpong AF, Kumordjie S, Matrevi SA, Ngoi J, Attram N, Abuaku B, Quashie NB, Hontz RD, Duah-Quashie NO.   +13 more
europepmc   +1 more source

Disentangling Thermal and Hydraulic Drivers of Stomatal Conductance Under Hot Versus Dry Vapour Pressure Deficit Increases Using Water‐Use Efficiency

Plant, Cell &Environment, EarlyView.
ABSTRACT Predicting plant functioning in a warming world requires capturing its divergent responses to hot versus dry vapour pressure deficit (VPD) increases. Stomatal responses to VPD are typically studied without disentangling the effects of RH and temperature, creating uncertainties for future plant functioning.
Aaron Potkay, Brandon Sloan, Aviad Perry, Or Sperling, Uri Hochberg, Rong Li, Xiangtao Xu, Mazen Nakad, Richard L. Peters, Xue Feng   +9 more
wiley   +1 more source

Hierarchical knowledge distillation framework for efficient node influence prediction in large-scale complex networks. [PDF]

Sci Rep
Yu X, Liu J, Tang L, Mi J, Long L.
europepmc   +1 more source