Loss of MyoD Promotes Fate Transdifferentiation of Myoblasts Into Brown Adipocytes

Brown adipose tissue (BAT) represents a promising agent to ameliorate obesity and other metabolic disorders. How- ever, the abundance of BAT decreases with age and BAT paucity is a common feature of obese subjects. As brown adipocytes and myoblasts share a common Myf5 lineage origin, elucidating the molecular mechanisms underlying the fate choices of brown adipocytes versus myoblasts may lead to novel approaches to expand BAT mass. Here we identify MyoD as a key negative regulator of brown adipocyte development. CRISPR/CAS9-mediated deletion of MyoD in C2C12 myoblasts facilitates their adipogenic transdifferentiation. MyoD knockout downregulates miR- 133 and upregulates the miR-133 target Igf1r, leading to amplification of PI3K–Akt signaling. Accordingly, inhibition of PI3K or Akt abolishes the adipogenic gene expression of MyoD null myoblasts. Strikingly, loss of MyoD converts satellite cell-derived primary myoblasts to brown adipocytes through upregulation of Prdm16, a target of miR-133 and key determinant of brown adipocyte fate. Conversely, forced expression of MyoD in brown preadipocytes blocks brown adipogenesis and upregulates the expression of myogenic genes. Importantly, miR-133a knockout signifi- cantly blunts the inhibitory effect of MyoD on brown adipogenesis. Our results establish MyoD as a negative regu- lator of brown adipocyte development by upregulating miR-133 to suppress Akt signaling and Prdm16

DeepSeek LLM: Scaling Open-Source Language Models with Longtermism

The rapid development of open-source large language models (LLMs) has been truly remarkable. However, the scaling law described in previous literature presents varying conclusions, which casts a dark cloud over scaling LLMs. We delve into the study of scaling laws and present our distinctive findings that facilitate scaling of large scale models in two commonly used open-source configurations, 7B and 67B. Guided by the scaling laws, we introduce DeepSeek LLM, a project dedicated to advancing open-source language models with a long-term perspective. To support the pre-training phase, we have developed a dataset that currently consists of 2 trillion tokens and is continuously expanding. We further conduct supervised fine-tuning (SFT) and Direct Preference Optimization (DPO) on DeepSeek LLM Base models, resulting in the creation of DeepSeek Chat models. Our evaluation results demonstrate that DeepSeek LLM 67B surpasses LLaMA-2 70B on various benchmarks, particularly in the domains of code, mathematics, and reasoning. Furthermore, open-ended evaluations reveal that DeepSeek LLM 67B Chat exhibits superior performance compared to GPT-3.5

Clodronate liposomes improve metabolic profile and reduce visceral adipose macrophage content in diet-induced obese mice

BACKGROUND: Obesity-related adipose inflammation has been thought to be a causal factor for the development of insulin resistance and type 2 diabetes. Infiltrated macrophages in adipose tissue of obese animals and humans are an important source for inflammatory cytokines. Clodronate liposomes can ablate macrophages by inducing apoptosis. In this study, we aim to determine whether peritoneal injection of clodronate liposomes has any beneficial effect on systemic glucose homeostasis/insulin sensitivity and whether macrophage content in visceral adipose tissue will be reduced in diet-induced obese (DIO) mice. METHODOLOGY/PRINCIPAL FINDINGS: Clodronate liposomes were used to deplete macrophages in lean and DIO mice. Macrophage content in visceral adipose tissue, metabolic parameters, glucose and insulin tolerance, adipose and liver histology, adipokine and cytokine production were examined. Hyperinsulinemic-euglycemic clamp study was also performed to assess systemic insulin sensitivity. Peritoneal injection of clodronate liposomes significantly reduced blood glucose and insulin levels in DIO mice. Systemic glucose tolerance and insulin sensitivity were mildly improved in both lean and DIO mice treated with clodronate liposomes by intraperitoneal (i.p.) injection. Hepatosteatosis was dramatically alleviated and suppression of hepatic glucose output was markedly increased in DIO mice treated with clodronate liposomes. Macrophage content in visceral adipose tissue of DIO mice was effectively decreased without affecting subcutaneous adipose tissue. Interestingly, levels of insulin sensitizing hormone adiponectin, including the high molecular weight form, were significantly elevated in circulation. CONCLUSIONS/SIGNIFICANCE: Intraperitoneal injection of clodronate liposomes reduces visceral adipose tissue macrophages, improves systemic glucose homeostasis and insulin sensitivity in DIO mice, which can be partially attributable to increased adiponectin levels

A Thermal Infrared Land Surface Temperature Retrieval Algorithm for Thin Cirrus Skies Using Cirrus Optical Properties

To acquire daytime land surface temperature (LST) in thin cirrus cloudy skies, we have developed a three-channel LST retrieval algorithm based on a widely used two-channel LST retrieval algorithm for the clear-sky conditions. In this algorithm, the LST is expressed as a multiple linear function of MODIS channels 29, 31 and 32 with the coefficients of the linear function dependent on the cirrus optical depth (COD) and cirrus effective radius (R). The influences from land surface emissivities (LSEs) are also considered in this algorithm. The simulated dataset shows that the LST could be estimated using the proposed algorithm with the root mean squire error (RMSE) less than 2.2 K in thin cirrus cloudy skies (COD less than 0.7) when viewing zenith angle (VZA) equivalent to 0°. As VZA is equivalent to 60°, the maximum RMSE are 2.7 K. The widely used generalized split-window (GSW) algorithm proposed for clear-sky conditions are used in cirrus cloudy skies, and the RMSEs of GSW algorithm estimated LST are 16.89 K and 22.32 K for VZA =0° and VZA =60° respectively when COD is 0.7. It indicates that the proposed three-channel algorithm can significantly improve the LST retrieval accuracy using thermal infrared data in cirrus cloudy skies. To estimate the LST errors caused by the uncertainties of COD, R, LSE and instrument noise, a sensitivity analysis was performed. It shows that the accuracy of cirrus COD is more important for the retrieval of LST compared with other parameters. The maximum total LST errors, taking into account all the input parameters’ uncertainty and algorithm error itself, are 3.8 K and 4.3 K when VZA =0° and VZA =60° respectively

Impaired exercise tolerance, mitochondrial biogenesis, and muscle fiber maintenance in miR-133a–deficient mice

Streamline Modern style "ocean liner" building, detail of the "prow"; Crossroads of the World has been called America's first outdoor shopping mall. Located on Sunset Boulevard and Las Palmas in Los Angeles, the mall features a central building designed to resemble an ocean liner surrounded by a small village of cottage-style bungalows, in different styles. It was designed by Robert V. Derrah and built in 1936. Once a busy shopping center, the Crossroads now hosts private offices, primarily for the entertainment industry. It has been used for location shooting in many films, television shows and commercials. A reproduction of Crossroads' iconic tower and spinning globe can be seen at Walt Disney World in Florida. It is on the US National Register of Historic Places (added 1980). Between the nine buildings the styles represented are Streamline Modern, Spanish style, California Mediterranean, Italian, French, Moorish, Cape Cod, and a "European village". Source: Wikipedia; http://en.wikipedia.org/wiki/Main_Page (accessed 7/18/2012

Building Extraction and Floor Area Estimation at the Village Level in Rural China Via a Comprehensive Method Integrating UAV Photogrammetry and the Novel EDSANet

Dynamic monitoring of building environments is essential for observing rural land changes and socio-economic development, especially in agricultural countries, such as China. Rapid and accurate building extraction and floor area estimation at the village level are vital for the overall planning of rural development and intensive land use and the “beautiful countryside” construction policy in China. Traditional in situ field surveys are an effective way to collect building information but are time-consuming and labor-intensive. Moreover, rural buildings are usually covered by vegetation and trees, leading to incomplete boundaries. This paper proposes a comprehensive method to perform village-level homestead area estimation by combining unmanned aerial vehicle (UAV) photogrammetry and deep learning technology. First, to tackle the problem of complex surface feature scenes in remote sensing images, we proposed a novel Efficient Deep-wise Spatial Attention Network (EDSANet), which uses dual attention extraction and attention feature refinement to aggregate multi-level semantics and enhance the accuracy of building extraction, especially for high-spatial-resolution imagery. Qualitative and quantitative experiments were conducted with the newly built dataset (named the rural Weinan building dataset) with different deep learning networks to examine the performance of the EDSANet model in the task of rural building extraction. Then, the number of floors of each building was estimated using the normalized digital surface model (nDSM) generated from UAV oblique photogrammetry. The floor area of the entire village was rapidly calculated by multiplying the area of each building in the village by the number of floors. The case study was conducted in Helan village, Shannxi province, China. The results show that the overall accuracy of the building extraction from UAV images with the EDSANet model was 0.939 and that the precision reached 0.949. The buildings in Helan village primarily have two stories, and their total floor area is 3.1 × 105 m2. The field survey results verified that the accuracy of the nDSM model was 0.94; the RMSE was 0.243. The proposed workflow and experimental results highlight the potential of UAV oblique photogrammetry and deep learning for rapid and efficient village-level building extraction and floor area estimation in China, as well as worldwide

Building Extraction and Floor Area Estimation at the Village Level in Rural China Via a Comprehensive Method Integrating UAV Photogrammetry and the Novel EDSANet

Dynamic monitoring of building environments is essential for observing rural land changes and socio-economic development, especially in agricultural countries, such as China. Rapid and accurate building extraction and floor area estimation at the village level are vital for the overall planning of rural development and intensive land use and the “beautiful countryside” construction policy in China. Traditional in situ field surveys are an effective way to collect building information but are time-consuming and labor-intensive. Moreover, rural buildings are usually covered by vegetation and trees, leading to incomplete boundaries. This paper proposes a comprehensive method to perform village-level homestead area estimation by combining unmanned aerial vehicle (UAV) photogrammetry and deep learning technology. First, to tackle the problem of complex surface feature scenes in remote sensing images, we proposed a novel Efficient Deep-wise Spatial Attention Network (EDSANet), which uses dual attention extraction and attention feature refinement to aggregate multi-level semantics and enhance the accuracy of building extraction, especially for high-spatial-resolution imagery. Qualitative and quantitative experiments were conducted with the newly built dataset (named the rural Weinan building dataset) with different deep learning networks to examine the performance of the EDSANet model in the task of rural building extraction. Then, the number of floors of each building was estimated using the normalized digital surface model (nDSM) generated from UAV oblique photogrammetry. The floor area of the entire village was rapidly calculated by multiplying the area of each building in the village by the number of floors. The case study was conducted in Helan village, Shannxi province, China. The results show that the overall accuracy of the building extraction from UAV images with the EDSANet model was 0.939 and that the precision reached 0.949. The buildings in Helan village primarily have two stories, and their total floor area is 3.1 × 105 m2. The field survey results verified that the accuracy of the nDSM model was 0.94; the RMSE was 0.243. The proposed workflow and experimental results highlight the potential of UAV oblique photogrammetry and deep learning for rapid and efficient village-level building extraction and floor area estimation in China, as well as worldwide