Characterization of Cre recombinase activity for in vivo targeting of adipocyte precursor cells.

The increased incidence of obesity and metabolic disease underscores the importance of elucidating the biology of adipose tissue development. The recent discovery of cell surface markers for prospective identification of adipose precursor cells (APCs) in vivo will greatly facilitate these studies, yet tools for specifically targeting these cells in vivo have not been identified. Here, we survey three transgenic mouse lines, Fabp4-Cre, PdgfRα-Cre, and Prx1-Cre, precisely assessing Cre-mediated recombination in adipose stromal populations and mature tissues. Our data provide key insights into the utility of these tools to modulate gene expression in adipose tissues. In particular, Fabp4-Cre is not effective to target APCs, nor is its activity restricted to these cells. PdgfRα-Cre directs recombination in the vast majority of APCs, but also targets other populations. In contrast, adipose expression of Prx1-Cre is chiefly limited to subcutaneous inguinal APCs, which will be valuable for dissection of APC functions among adipose depots

Intelligent modeling with physics-informed machine learning for petroleum engineering problems

The advancement in big data and artificial intelligence has enabled a novel exploration mode for the study of petroleum engineering. Unlike theory-based solution methods, the data-driven intelligent approaches demonstrate superior flexibility, computational efficiency and accuracy for dealing with complex multi-scale, and multi-physics problems. However, these intelligent models often disregard physical laws in pursuit of error minimization, which leads to certain uncertainties. Therefore, physics-informed machine learning approaches have been developed based on data, guided by physics, and supported by machine learning models. This study summarizes four embedding mechanisms for introducing physical information into machine learning models, including input databased embedding, model architecture-based embedding, loss function-based embedding, and model optimization-based embedding mechanism. These “data + physics” dualdriven intelligent models not only exhibit higher prediction accuracy while adhering to physic laws, but also accelerate the convergence to improve computational efficiency. This paradigm will facilitate the guide developments in solving petroleum engineering problems toward a more comprehensive and efficient direction.Cited as: Xie, C., Du, S., Wang, J., Lao, J., Song, H. Intelligent modeling with physics-informed machine learning for petroleum engineering problems. Advances in Geo-Energy Research, 2023, 8(2): 71-75. https://doi.org/10.46690/ager.2023.05.0

Macrophage-released ADAMTS1 promotes muscle stem cell activation.

Coordinated activation of muscle stem cells (known as satellite cells) is critical for postnatal muscle growth and regeneration. The muscle stem cell niche is central for regulating the activation state of satellite cells, but the specific extracellular signals that coordinate this regulation are poorly understood. Here we show that macrophages at sites of muscle injury induce activation of satellite cells via expression of Adamts1. Overexpression of Adamts1 in macrophages in vivo is sufficient to increase satellite cell activation and improve muscle regeneration in young mice. We demonstrate that NOTCH1 is a target of ADAMTS1 metalloproteinase activity, which reduces Notch signaling, leading to increased satellite cell activation. These results identify Adamts1 as a potent extracellular regulator of satellite cell activation and have significant implications for understanding the regulation of satellite cell activity and regeneration after muscle injury.Satellite cells are crucial for growth and regeneration of skeletal muscle. Here the authors show that in response to muscle injury, macrophages secrete Adamts1, which induces satellite cell activation by modulating Notch1 signaling

Transient enhancement of magnetization damping in CoFeB film via pulsed laser excitation

Laser-induced spin dynamics of in-plane magnetized CoFeB films has been studied by using time-resolved magneto-optical Kerr effect measurements. While the effective demagnetization field shows little dependence on the pump laser fluence, the intrinsic damping constant has been found to be increased from 0.008 to 0.076 with the increase in the pump fluence from 2 mJ/cm2 to 20 mJ/cm2. This sharp enhancement has been shown to be transient and ascribed to the heating effect induced by the pump laser excitation, as the damping constant is almost unchanged when the pump-probe measurements are performed at a fixed pump fluence of 5 mJ/cm2 after irradiation by high power pump pulses

Transient enhancement of magnetization damping in CoFeB film via pulsed laser excitation

Laser-induced spin dynamics of in-plane magnetized CoFeB films has been studied by using time-resolved magneto-optical Kerr effect measurements. While the effective demagnetization field shows little dependence on the pump laser fluence, the intrinsic damping constant has been found to be increased from 0.008 to 0.076 with the increase in the pump fluence from 2 mJ/cm2 to 20 mJ/cm2. This sharp enhancement has been shown to be transient and ascribed to the heating effect induced by the pump laser excitation, as the damping constant is almost unchanged when the pump-probe measurements are performed at a fixed pump fluence of 5 mJ/cm2 after irradiation by high power pump pulses

Interface magnetic and electrical properties of CoFeB /InAs heterostructures

Amorphous magnetic CoFeB ultrathin films have been synthesized on the narrow band gap semiconductor InAs(100) surface, and the nature of the interface magnetic anisotropy and electrical contact has been studied. Angle-dependent hysteresis loops reveal that the films have an in-plane uniaxial magnetic anisotropy (UMA) with the easy axis along the InAs [0-11] crystal direction. The UMA was found to be dependent on the annealing temperatures of the substrates, which indicates the significant role of the Fe, Co-As bonding at the interface related to the surface condition of the InAs(100). I-V measurements show an ohmic contact interface between the CoFeB films and the InAs substrates, which is not affected by the surface condition of the InAs (100)

Element specific spin and orbital moments of nanoscale CoFeB amorphous thin films on GaAs(100)

CoFeB amorphous films have been synthesized on GaAs(100) and studied with X-ray magnetic circular dichroism (XMCD) and transmission electron microscopy (TEM). We have found that the ratios of the orbital to spin magnetic moments of both the Co and Fe in the ultrathin amorphous film have been enhanced by more than 300% compared with those of the bulk crystalline Co and Fe, and specifically a large orbital moment of 0.56 μB from the Co atoms has been observed and at the same time the spin moment of the Co atoms remains comparable to that of the bulk hcp Co. The results indicate that the large uniaxial magnetic anisotropy (UMA) observed in the ultrathin CoFeB film on GaAs(100) is related to the enhanced spin-orbital coupling of the Co atoms in the CoFeB. This work offers experimental evidences of the correlation between the UMA and the element specific spin and orbital moments in the CoFeB amorphous film on the GaAs(100) substrate, which is of significance for spintronics applications

Muscleblind-Like 1 Knockout Mice Reveal Novel Splicing Defects in the Myotonic Dystrophy Brain

Myotonic dystrophy type 1 (DM1) is a multi-systemic disorder caused by a CTG trinucleotide repeat expansion (CTGexp) in the DMPK gene. In skeletal muscle, nuclear sequestration of the alternative splicing factor muscleblind-like 1 (MBNL1) explains the majority of the alternative splicing defects observed in the HSALR transgenic mouse model which expresses a pathogenic range CTGexp. In the present study, we addressed the possibility that MBNL1 sequestration by CUGexp RNA also contributes to splicing defects in the mammalian brain. We examined RNA from the brains of homozygous Mbnl1ΔE3/ΔE3 knockout mice using splicing-sensitive microarrays. We used RT-PCR to validate a subset of alternative cassette exons identified by microarray analysis with brain tissues from Mbnl1ΔE3/ΔE3 knockout mice and post-mortem DM1 patients. Surprisingly, splicing-sensitive microarray analysis of Mbnl1ΔE3/ΔE3 brains yielded only 14 candidates for mis-spliced exons. While we confirmed that several of these splicing events are perturbed in both Mbnl1 knockout and DM1 brains, the extent of splicing mis-regulation in the mouse model was significantly less than observed in DM1. Additionally, several alternative exons, including Grin1 exon 4, App exon 7 and Mapt exons 3 and 9, which have previously been reported to be aberrantly spliced in human DM1 brain, were spliced normally in the Mbnl1 knockout brain. The sequestration of MBNL1 by CUGexp RNA results in some of the aberrant splicing events in the DM1 brain. However, we conclude that other factors, possibly other MBNL proteins, likely contribute to splicing mis-regulation in the DM1 brain

Dongting Lake Water Level Forecast and Its Relationship with the Three Gorges Dam Based on a Long Short-Term Memory Network

To study the Dongting Lake water level variation and its relationship with the upstream Three Gorges Dam (TGD), a deep learning method based on a Long Short-Term Memory (LSTM) network is used to establish a model that predicts the daily water levels of Dongting Lake. Seven factors are used as the input for the LSTM model and eight years of daily data (from 2003 to 2012) are used to train the model. Then, the model is applied to the test dataset (from 2011 to 2013) for forecasting and is evaluated using the root mean squared error (RMSE) and the coefficient of determination (R2). The test shows the LSTM model has better accuracy compared to the support vector machine (SVM) model. Furthermore, the model is adjusted to simulate the situation where the TGD does not exist to explore the dam’s impact. The experiment shows that the water level of Dongting Lake drops conspicuously every year from September to November during the TGD impounding period, and the water level increases mildly during dry seasons due to TGD replenishment. Additionally, the impact of the TGD results in a water level decline in Dongting Lake during flood peaks and a subsequent lagged rise. This research provides a tool for flood forecasting and offers a reference for TGD water regulation