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Studies have reported opposing effects of high-fat (HF) diet and mechanical stimulation on lineage commitment of the bone marrow stem cells. Yet, how bone marrow modulates its gene expression in response to the combined effects of mechanical loading and a HF diet has not been addressed. We investigated whether early-life (before onset of sexual maturity at 6 weeks of age) voluntary physical activity can modulate the effects of a HF diet on male Sprague Dawley rats. In the bone marrow, early-life HF diet resulted in adipocyte hypertrophy and a pro-inflammatory and pro-adipogenic gene expression profile. The bone marrow of the rats that undertook wheel exercise while on a HF diet retained a memory of the early-life exercise. This memory lasted at least 60 days after the cessation of the voluntary exercise. Our results are consistent with the marrow adipose tissue having a unique response to HF feeding in the presence or absence of exercise.

The present work was carried out to develop and optimize the functional lipid Conjugated Linoleic Acid (CLA) as beverage emulsion stabilized with whey protein isolate using response surface methodology. The effect of three independent variables Conjugated Linoleic Acid (CLA; 5–15% (v/v)), whey protein isolate (WPI; 4–6% (w/v)) and pressure cycle (1–3) on dependent variables creaming index (CI%) and emulsion stability (ES%) were studied. Box Behnken Design (BBD) generated 16 factorial runs with 4 center points. Stress tests such as freeze-thaw test, mechanical stress test, and particle size characterization were analyzed. Under the optimized conditions, CLA beverage emulsion with 10% (v/v) CLA, 6% (w/v) WPI with 3 pressure cycle (T8) have 98% CI and ES 30th day of refrigerated storage was considered as an optimized emulsion for incorporation into dairy products. In conclusion, it is possible to utilize whey protein isolate as a sole emulsifier in formulation of CLA beverage emulsion using Response Surface Methodology-Box Behnken Design (RSM-BBD) method with reproducible results.

The multispectral satellite sensor images have multibands, which have some typical noise. There is difficult to detect this tipical noise with low resolution image. The satellite local or gloval pixel information and quantificationare degraded due to this noise. Many standard transformations and filtering operations are developed for detection and removing of non-gaussion noise, which are not given sophisticated results with existing methods. These statistical characteristics are applied to those samples to identify and quantify present tipical noise. The higher-order statistical based machine learning algorithm is developing to remove the speckle noise from satellite image. In this proposed algorithm, implemented the higher order statistical approache such as skewness, kurtosis based adaptive curvelet methods are implemented for the detection and suppression of speckle noise with retrieve of spectral and spectral values. The proposed algorithm preserves smooth and sharp details and maintains the tradeoff level in multispectral bands is suitable for advanced high speed 5G communication with the effective rate of transmission. The proposed results are verified with suitable statistical parameters such as PSNR, Entropy and ERGAS values. [ABSTRACT FROM AUTHOR]

The analysis of Parkinson's disease (PD) is an inspiring task that necessitates the analysis of numerous motor and non-motor indications. During analysis, some abnormalities are considered as important symptoms to analyze the disease. Hence, this research introduced the proposed chronological smart sunflower optimization Algorithm (CSSFOA) for classifying the PD from voice data and voice signal samples. For voice signal, the input signals are pre-processed by the Gaussian filter, and then the significant features are extracted from it. The selection of optimal features is done by the chronological smart flower optimization Algorithm (CSFOA). The proposed CSFOA-based feature selection method considered the features by the Bray–Curtis distance. The PD classification is done by the ZF-Net which is trained by proposed CSSFOA to increase the performance of classification. The experimental result reveals that the proposed CSSFOA_ZF-Net algorithm got a better testing accuracy of 0.945, a sensitivity of 0.919, and a specificity of 0.957. [ABSTRACT FROM AUTHOR]

In the present situation, applications related to multimedia are discovered to be comfortable with the use of video. The number of end consumers who use video continues to rise every day. People are presently searching for videos with better quality among the ones that are currently available there. This results in the launch and dissemination of HD (high definition) videos. Ultra high-definition (UHD) videos are becoming more and more popular as a result of this advancement and need. However, as video communication keeps expanding, there is an upsurge in network traffic because of the limited bandwidth, especially among smart cities. Different advancement codecs have been suggested to deal with the data stream to overcome this hazardous circumstance. However, the fact that modern UHD videos have huge amounts of data makes the available codecs even more complicated. UHD videos can be processed with the latest improvement codec, H.265/High-efficiency video coding (HEVC). Nevertheless, it is impacted by increased power consumption and intricate calculations. Limitations in the codec's functionality confine its use to specific applications, preventing its application in wireless, mobile, or portable settings. Hence, this research concentrates on implementing frame-level quality enhancement through a deep learning network known as FQE-Net. The deep learning convolutional neural network (DLCNN) is specifically crafted to manage films with resolutions up to 16K. Its primary objectives include reducing complexity, minimizing artifacts, enhancing the efficiency of the HEVC codec, and compacting energy consumption. To achieve superior efficiency, it is imperative to replace the DWT transforms within the HEVC codec with a DLCNN model. Additionally, incorporating the Content Block Search Algorithm for Motion Estimation and Compensation, alongside filtering techniques like Sample Adaptive Filter and Deblocking Filter, becomes essential. The simulation results showed that the suggested FQE-Net performed better than the conventional techniques. [ABSTRACT FROM AUTHOR]

Around the world, population growth and urbanization are rapidly increasing, resulting in high waste generation. When waste is not properly managed, it creates serious health issues, and methane (CH4) generation and other toxic pollutants will lead to global climatic changes. The technologies that involve converting the waste into electricity, biofuel, etc... are very expensive and amount to 20-50% of municipal budgets. An automatic waste segregating and composting system, which helps to segregate the waste into metal and biodegradable materials by using an Arduino and various sensors, is proposed. After being crushed in the composting process, biodegradable waste is used to make fertilizers and achieve the required level of compost by maintaining and controlling its parameters. By using an ultrasonic sensor, the system determines whether the bin is empty or full. It also gives data about the bin status by using "THINGVIEWFREE" app. [ABSTRACT FROM AUTHOR]