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The conventional view of gene regulation in biology has centered around protein-coding genes via the central dogma of DNA→mRNA→protein. The discovery of thousands of long non-coding RNAs (lncRNAs) has certainly changed our view of the complexity of mammalian genomes and transcriptomes, as well as many other aspects of biology including transcriptional and posttranscriptional regulation of gene expression. Accumulating reports of misregulated lncRNA expression across numerous cancer types suggest that aberrant lncRNA expression may be a major contributor to tumorigenesis. Here, we summarize recent data about the biological characteristics of lncRNAs in cancer pathways. These include examples with a wide range of molecular mechanisms involved in gene regulation. We also consider the medical implications, and discuss how lncRNAs can be used for cancer diagnosis and prognosis, and serve as potential therapeutic targets. As more examples of regulation by lncRNA are uncovered, one might predict that the large transcripts will eventually rival small RNAs and proteins in their versatility as regulators of genetic information.

The treatment of bone defects remains a significant challenge to be solved clinically. Immunomodulatory properties of orthopedic biomaterials have significance in regulating osteoimmune microenvironment for osteogenesis. A lactic acid‐co‐glycolic acid (PLGA) scaffold incorporates black phosphorus (BP) fabricated by 3D printing technology to investigate the effect of BP on osteoimmunomodulation and osteogenesis in site. The PLGA/BP scaffold exhibits suitable biocompatibility, biodegradability, and mechanical properties as an excellent microenvironment to support new bone formation. The studies' result also demonstrate that the PLGA/BP scaffolds are able to recruit and stimulate macrophages M2 polarization, inhibit inflammation, and promote human bone marrow mesenchymal stem cells (hBMSCs) proliferation and differentiation, which in turn promotes bone regeneration in the distal femoral defect region of steroid‐associated osteonecrosis (SAON) rat model. Moreover, it is screened and demonstrated that PLGA/BP scaffolds can promote osteogenic differentiation by transcriptomic analysis, and PLGA/BP scaffolds promote osteogenic differentiation and mineralization by activating PI3K‐AKT signaling pathway in hBMSC cells. In this study, it is shown that the innovative PLGA/BP scaffolds are extremely effective in stimulating bone regeneration by regulating macrophage M2 polarization and a new strategy for the development of biomaterials that can be used to repair bone defects is offered.

Although widely used in many applications, accurate and efficient human action recognition remains a challenging area of research in the field of computer vision. Most recent surveys have focused on narrow problems such as human action recognition methods using depth data, 3D-skeleton data, still image data, spatiotemporal interest point-based methods, and human walking motion recognition. However, there has been no systematic survey of human action recognition. To this end, we present a thorough review of human action recognition methods and provide a comprehensive overview of recent approaches in human action recognition research, including progress in hand-designed action features in RGB and depth data, current deep learning-based action feature representation methods, advances in human–object interaction recognition methods, and the current prominent research topic of action detection methods. Finally, we present several analysis recommendations for researchers. This survey paper provides an essential reference for those interested in further research on human action recognition.

Perimenopause is an important stage of female senescence. Epidemiological investigation has shown that the incidence of cardiovascular disease in premenopausal women is lower than that in men, and the incidence of cardiovascular disease in postmenopausal women is significantly higher than that in men. This phenomenon reveals that estrogen has a definite protective effect on the cardiovascular system. In the cardiovascular system, oxidative stress is considered important in the pathogenesis of atherosclerosis, myocardial dysfunction, cardiac hypertrophy, heart failure, and myocardial ischemia. From the perspective of oxidative stress, estrogen plays a regulatory role in the cardiovascular system through the estrogen receptor, providing strategies for the treatment of menopausal women with cardiovascular diseases.

Optoelectronic memory plays a vital role in modern semiconductor industry. The fast emerging requirements for device miniaturization and structural flexibility have diverted research interest to two-dimensional thin layered materials. Here, we report a multibit nonvolatile optoelectronic memory based on a heterostructure of monolayer tungsten diselenide and few-layer hexagonal boron nitride. The tungsten diselenide/boron nitride memory exhibits a memory switching ratio approximately 1.1 × 10 6 , which ensures over 128 (7 bit) distinct storage states. The memory demonstrates robustness with retention time over 4.5 × 10 4  s. Moreover, the ability of broadband spectrum distinction enables its application in filter-free color image sensor. This concept is further validated through the realization of integrated tungsten diselenide/boron nitride pixel matrix which captured a specific image recording the three primary colors (red, green, and blue). The heterostructure architecture is also applicable to other two-dimensional materials, which is confirmed by the realization of black phosphorus/boron nitride optoelectronic memory. Continued device miniaturization and feasibility of integrating two-dimensional materials into circuits have enabled flexible and transparent optoelectronic memories. Here, the authors show a WSe 2 –hBN-based heterostructure memory with switching ratio of ~1.1 × 10 6 , ensuring over 128 distinct storage states and retention time of ~4.5 × 10 4  s.

The fields of human activity analysis have recently begun to diversify. Many researchers have taken much interest in developing action recognition or action prediction methods. The research on human action evaluation differs by aiming to design computation models and evaluation approaches for automatically assessing the quality of human actions. This line of study has become popular because of its explosively emerging real-world applications, such as physical rehabilitation, assistive living for elderly people, skill training on self-learning platforms, and sports activity scoring. This paper presents a comprehensive survey of approaches and techniques in action evaluation research, including motion detection and preprocessing using skeleton data, handcrafted feature representation methods, and deep learning-based feature representation methods. The benchmark datasets from this research field and some evaluation criteria employed to validate the algorithms’ performance are introduced. Finally, the authors present several promising future directions for further studies.

We report on widefield microwave vector field imaging with sub-\\(100\\;\\mu {\\rm{m}}\\) resolution using a microfabricated alkali vapor cell. The setup can additionally image dc magnetic fields, and can be configured to image microwave electric fields. Our camera-based widefield imaging system records 2D images with a 6 × 6 mm2 field of view at a rate of 10 Hz. It provides up to \\(50\\;\\mu {\\rm{m}}\\) spatial resolution, and allows imaging of fields as close as \\(150\\;\\mu {\\rm{m}}\\) above structures, through the use of thin external cell walls. This is crucial in allowing us to take practical advantage of the high spatial resolution, as feature sizes in near-fields are on the order of the distance from their source, and represent an order of magnitude improvement in surface-feature resolution compared to previous vapor cell experiments. We present microwave and dc magnetic field images above a selection of devices, demonstrating a microwave sensitivity of \\(1.4\\;\\mu {\\rm{T}}\\;{\\mathrm{Hz}}^{-1/2}\\) per \\(50\\times 50\\times 140\\;\\mu {{\\rm{m}}}^{3}\\) voxel, at present limited by the speed of our camera system. Since we image 120 × 120 voxels in parallel, a single scanned sensor would require a sensitivity of at least \\(12\\;\\mathrm{nT}\\;{\\mathrm{Hz}}^{-1/2}\\) to produce images with the same sensitivity. Our technique could prove transformative in the design, characterization, and debugging of microwave devices, as there are currently no satisfactory established microwave imaging techniques. Moreover, it could find applications in medical imaging.

Aerosol acidity plays a key role in secondary aerosol formation. The high-temporal-resolution PM2.5 pH and size-resolved aerosol pH in Beijing were calculated with ISORROPIA II. In 2016–2017, the mean PM2.5 pH (at relative humidity (RH) > 30 %) over four seasons was 4.5±0.7 (winter) > 4.4±1.2 (spring) > 4.3±0.8 (autumn) > 3.8±1.2 (summer), showing moderate acidity. In coarse-mode aerosols, Ca2+ played an important role in aerosol pH. Under heavily polluted conditions, more secondary ions accumulated in the coarse mode, leading to the acidity of the coarse-mode aerosols shifting from neutral to weakly acidic. Sensitivity tests also demonstrated the significant contribution of crustal ions to PM2.5 pH. In the North China Plain (NCP), the common driving factors affecting PM2.5 pH variation in all four seasons were SO42-, TNH3 (total ammonium (gas + aerosol)), and temperature, while unique factors were Ca2+ in spring and RH in summer. The decreasing SO42- and increasing NO3- mass fractions in PM2.5 as well as excessive NH3 in the atmosphere in the NCP in recent years are the reasons why aerosol acidity in China is lower than that in Europe and the United States. The nonlinear relationship between PM2.5 pH and TNH3 indicated that although NH3 in the NCP was abundant, the PM2.5 pH was still acidic because of the thermodynamic equilibrium between NH4+ and NH3. To reduce nitrate by controlling ammonia, the amount of ammonia must be greatly reduced below excessive quantities.

Vibration dampers and insulators are important components of transmission lines, and it is therefore important for the normal operation of transmission lines to detect defects in these components in a timely manner. In this paper, we provide an automatic detection method for component defects through patrolling inspection by an unmanned aerial vehicle (UAV). We constructed a dataset of vibration dampers and insulators (DVDI) on transmission lines in images obtained by the UAV. It is difficult to detect defects in vibration dampers and insulators from UAV images, as these components and their defective parts are very small parts of the images, and the components vary greatly in terms of their shape and color and are easily confused with the background. In view of this, we use the end-to-end coordinate attention and bidirectional feature pyramid network “you only look once” (BC-YOLO) to detect component defects. To make the network focus on the features of vibration dampers and insulators rather than the complex backgrounds, we added the coordinate attention (CA) module to YOLOv5. CA encodes each channel separately along the vertical and horizontal directions, which allows the attention module to simultaneously capture remote spatial interactions with precise location information and helps the network locate targets of interest more accurately. In the multiscale feature fusion stage, different input features have different resolutions, and their contributions to the fused output features are usually unequal. However, PANet treats each input feature equally and simply sums them up without distinction. In this paper, we replace the original PANet feature fusion framework in YOLOv5 with a bidirectional feature pyramid network (BiFPN). BiFPN introduces learnable weights to learn the importance of different features, which can make the network focus more on the feature mapping that contributes more to the output features. To verify the effectiveness of our method, we conducted a test in DVDI, and its mAP@0.5 reached 89.1%, a value 2.7% higher than for YOLOv5.