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Objective To study the changes of heart-brain coupling indexes after cerebral ischemia in a rat model of middle cerebral artery occlusion (MCAO) and the relationship with prognosis in the early stage. Methods Twenty male SD rats(7~8 weeks old, body weight 290±25 g) were randomly divided into 2 groups (n=10): the cerebral ischemia group (MCAO group) and the control group (Sham group). Lead Ⅱ electrocardiography (ECG) and dual-channel electroencephalogram (EEG) were recorded at the baseline and 0~4 h after the surgery. Neurofunctional recovery was evaluated by neurological deficit score(NDS) via a series of behavior tests every 24 h and survival time was recorded. The heart rate variability (HRV) indicators were extracted by the collected ECG signals, including RR interval (RRI), low frequency (LF), high frequency (HF) and LF/HF ratio. The power spectrum of δ, θ, α and β waves were calculated from EEG signal. The heart-brain network was constructed based on the indicators mentioned, and the transfer entropy algorithm was used to quantify the coupling strength applied to the prognosis for survival which was analyzed and compared by the area under curve (AUC) among nodes of different networks. Results There was a bidirectional interaction between the brain and the heart, and the strength of two-way coupling increased after ischemia. There was obviously increased coupling between HRV LF and EEG δ wave. Transfer entropy (AUC=0.717, P=0.010) was superior to the HRV (AUC=0.571, P=0.404) and EEG power spectrum (AUC=0.583, P=0.329) in prognostication. Conclusion The low-frequency coupling between heart and brain enhanced after cerebral ischemia, and the coupling index of heart and brain can improve the prognostic performance.

In this study, wheat straw char was prepared at 500°C and used as a catalyst (at 500–700°C) to reform the wheat straw volatiles. The char samples before and after reforming were characterized in terms of N2 adsorption (BET area), Raman spectroscopy, Fourier transform infrared spectroscopy, proximate analysis, ultimate analysis, and inductively coupled plasma measurements. The surface morphology of the used char was analyzed by a scanning electron microscope. The results have revealed that following the reforming process, the carbon content increased from 69.2% to 71.7% (at 500°C), 73.9% (at 600°C), and 74.3% (at 700°C) and the fixed carbon content increased from 64.6% to 68.7% (at 500°C), 71.4% (at 600°C), and 72.4% (at 700°C). In contrast, the higher heating value of the used char remained unchanged. We observed a decrease in specific surface area (from 112.67 to 7.93 m2·g−1), but an increase in char defects and functional groups following the reforming process (at 600°C) served to maintain catalytic activity, as shown in a second reforming test. Our results suggest that surface defects and functional groups are the main factors contributing to char reactivity.

Thyroid hormone has been shown to have both tumor-promoting and tumor-suppressing actions, which has led to significant debate over its involvement in the development of cancer. Proliferation, apoptosis, invasiveness, and angiogenesis are all aspects of cancer that are affected by the thyroid hormones T3 and T4, according to research conducted in animal models and in vitro experiments. The effects of thyroid hormones on cancer cells are mediated by many non-genomic mechanisms, one of which involves the activation of the plasma membrane receptor integrin αvβ3. Typically, abnormal amounts of thyroid hormones are linked to a higher occurrence of cancer. Both benign and malignant thyroid disorders were found to be associated with an increased risk of extra-thyroidal malignancies, specifically colon, breast, prostate, melanoma, and lung cancers. The purpose of this review was to shed light on this link to define which types of cancer are sensitive to thyroid hormones and, as a result, are anticipated to respond favorably to treatment of the thyroid hormone axis. [ABSTRACT FROM AUTHOR]

Crack detection methods of high-rise building walls based on traditional computer vision (CV) heavily rely on manual selection and extraction of design features. Convolutional neural network (CNN)-based CV can actively learn the features of cracks and adapt to complex backgrounds, solving the limitations of traditional crack detection methods. This paper explores faster region-CNN, single shot multibox detector (SSD), You Only Look Once for crack detection, and Mask R-CNN for crack segmentation and proposes a novel automatic crack geometric quantification method by combining CNN-based object detection and segmentation. The contents include (1) crack detection and bounding box extraction, exploring a variety of models, selecting the best model to detect the image taken by an unmanned aerial vehicle (UAV), and extracting the crack region; (2) crack segmentation, using the detection results of the first part as input for more accurate detection and segmentation of cracks; and (3) a novel pixel-level geometric quantization method of crack based on Hough straight-line detection, mainly including crack length and width. Then, the pixel level is transformed into the actual geometric quantization to simply determine the crack severity. The three models generated in these three parts can be used for managing exterior wall cracks in high-rise buildings for different inspection purposes. [ABSTRACT FROM AUTHOR]