Miracle-dz/UniFAR-FTUs huggingface dataset:
Nanotransfection-based vasculogenic cell reprogramming drives functional recovery in a mouse model of ischemic stroke

Fibroblasts nonvirally programmed to convert into induced endothelial cells show therapeutic potential for ischemic stroke. Ischemic stroke causes vascular and neuronal tissue deficiencies that could lead to substantial functional impairment and/or death. Although progenitor-based vasculogenic cell therapies have shown promise as a potential rescue strategy following ischemic stroke, current approaches face major hurdles. Here, we used fibroblasts nanotransfected with Etv2, Foxc2, and Fli1 (EFF) to drive reprogramming-based vasculogenesis, intracranially, as a potential therapy for ischemic stroke. Perfusion analyses suggest that intracranial delivery of EFF-nanotransfected fibroblasts led to a dose-dependent increase in perfusion 14 days after injection. MRI and behavioral tests revealed ~70% infarct resolution and up to ~90% motor recovery for mice treated with EFF-nanotransfected fibroblasts. Immunohistological analysis confirmed increases in vascularity and neuronal cellularity, as well as reduced glial scar formation in response to treatment with EFF-nanotransfected fibroblasts. Together, our results suggest that vasculogenic cell therapies based on nanotransfection-driven (i.e., nonviral) cellular reprogramming represent a promising strategy for the treatment of ischemic stroke.
Pregnancies in Diabetes and Obesity: The Capacity-Load Model of Placental Adaptation

Excess nutritional supply to the growing fetus, resulting from maternal diabetes and obesity, is associated with increased risks of fetal maldevelopment and adverse metabolic conditions in postnatal life. The placenta, interposed between mother and fetus, serves as the gateway between the two circulations and is usually considered to mediate maternal exposures to the fetus through a direct supply line. In this Perspective, however, we argue that the placenta is not an innocent bystander and mounts responses to fetal “signals of distress” to sustain its own adequate function and protect the fetus. We describe several types of protection that the placenta can offer the fetus against maternal metabolic perturbations and offer a theoretical model of how the placenta responds to the intrauterine environment in maternal diabetes and obesity to stabilize the fetal environment. Our approach supports growing calls for early screening and control of pregnancy metabolism to minimize harmful fetal outcomes.
Prospective materials and applications for Li secondary batteries

Li-ion batteries have been employed successfully in various small electronic devices for the last two decades, and the types of applications are currently expanding to include electric vehicles (EVs), power tools, and large electric power storage units. In order to be implemented in these emerging markets, novel materials for negative and positive electrodes as well as electrolytes need to be developed to achieve high energy density, high power, and safe lithium rechargeable batteries. Here, the trends of the market and development of materials for each application are introduced, and some of next generation Li-ion batteries are discussed.
A flexible, transparent and ultrathin single-layer graphene earphone

Graphene is flexible and transparent with one-atom layer thickness, and is a novel building block with potential applications in future portable devices. Herein a flexible, transparent and ultrathin earphone based on single-layer graphene (SLG) is reported. The SLG earphone operates in the frequency range of 20 Hz to 200 kHz and has a highest sound pressure level (SPL) of 70 dB at a 1 cm distance. The SPLs emitted from one to six layers of stacked SLG are compared. It is observed that the SPL decreases with an increasing number of stacked layers. The SLG earphone, which is packaged with a commercial earphone casing, can play music clearly. Compared with a conventional earphone, the SLG earphone has a broader frequency response and a lower fluctuation. Testing results in both time and frequency domains show a frequency doubling effect, which indicates that the working principle is based on the electro-thermoacoustic (ETA) effect. As the SLG earphone operates in both the audible and ultrasonic frequency range, it can be used for a wide variety of applications.
Simple non-iterative clustering and CNNs for coarse segmentation of breast cancer whole-slide images

Breast cancer is the dominant cancer among women as it accounts for about one-quarter of all cancer cases in females. The digitized images of Hematoxylin and Eosin (H&E) stained slides of breast cancer specimens carry valuable diagnostic information. However, inspecting these slides manually is a non-trivial task prone to subjective interpretation. Digital pathology (DP) and artificial intelligence (AI) open an opportunity for objective interpretation of the image data. It is challenging to automate the segmentation process in the whole slide images due to the visual complexity of tissue appearance without the need for tedious and time-consuming fine annotations. Many algorithms classify the tissue regions into different types instead of segmenting them, as the classification algorithms require coarse annotations that are easier to acquire. In this paper, we propose a new segmentation framework that combines the simple non-iterative clustering algorithm with a standard convolutional neural network (CNN) classifier to segment whole slide images into different tissue types. In addition, a graph-based post-processing step is applied to improve the framework segmentation performance further. The results show promising improvement to the CNN classifier based coarse segmentation, which would give better feasibility to quantify and study tissues’ mutual relationships.
Super-solution technology for low light level imaging in the field of remote sensing

We have designed the super-solution technology for low light level imaging in the field of remote sensing. Low light level image super-resolution is realized with a method of super-resolution processing based on worked example learning and the information of high-resolution visible image. Multi-frame image and single-frame image of the super-resolution algorithms are combined to apply in the low light level imaging in the field of remote sensing. With the ground targets as the reference, the results illustrate that the higher resolution images are obtained and a number of image data are satisfactory. This technology has impressive potential for improving the efficiency of low light level remote sensing.
A consistent definition of probe size and spatial resolution in the analytical electron microscope

A technique for measuring the extremely small (<2 nm) probe sizes generated in a field emission gun analytical electron microscope is described. The technique involves scanning the fine probe across the edge of small oriented MgO cubes. A mathematical description of the intensity profiles is developed and it is shown how this relates to various definitions of the probe size. The experimentally measured probe sizes are then combined in a consistent manner with both beam broadening calculations and experimental measurements. This approach results in the first self‐consistent definition of the spatial resolution of X‐ray microanalysis in thin foils.
Fundamental study of polymer dynamic behavior in resist processing

Although Extreme Ultra Violet (EUV) at 13.5 nm wavelength already moved into commercialization state, serious technical issues remain as important challenges. Local variation, such as intra-filed CD uniformity or LER, is typically identified from calculated mean CD utilizing top-down view observation. In this study, cross sectional SEM was applied efficiently and resist fundamental behaviors were visualized and quantified. Furthermore, I would mention about minimum structural unit constructing resist pattern related to resolution limit and LER.
Infrared image recognition method for pedestrian and vehicles based on improved YOLO

Aiming at the problems of low real-time and low accuracy in target recognition of pedestrians and vehicles in infrared images, as well as missing long-distance detection, a YOLO-based deep learning method for pedestrian and vehicle recognition was proposed. First, image preprocessing and data enhancement are applied to improve the generalization ability of the model; then the network structure were changed, as well as the multi-scale feature detection map and the PANet structure were applied to improve the recognition ability of different scale targets; finally the attention mechanism was applied to improve the detection accuracy. In the simulation, the actual collected infrared pedestrian and vehicle data sets were used for training, and got the training model. Then the proposed method was transplanted to the embedded GPU platform to verify the performance of the algorithm. The result shows that the recognition accuracy rate reaches 84.35%, 12.44% improved compared with the original YOLO method, and the speed has reached 88 frames per second, which can meet the actual engineering needs.
Application of fuzzy active disturbance rejection control in stable imaging

In order to further improve the imaging stability of photoelectric devices such as lidar, this paper studies the influence of the introduction of fuzzy control into the linear active disturbance rejection controller on the stability and anti-disturbance performance of lidar imaging. When the system model is unknown, the fuzzy control that also does not depend on model information is introduced into the linear active disturbance rejection control(LADRC), and the fuzzy control is used as the active disturbance rejection controller, and the LADRC is nonlinearized through the fuzzy control. Taking the photoelectric stabilization platform as the research object, the fuzzy active disturbance rejection (FADRC) is simulated and compared with the traditional design method. The simulation results show that the fuzzy active disturbance rejection proposed in this paper can effectively improve the response speed and anti-disturbance ability of the photoelectric stabilized platform. The response speed is increased by 50%, the overshoot is reduced by 67.3%, and the disturbance isolation ability is increased by 20.3%. It can greatly improve the imaging stability of lidar.
Investigating the quality changes of raw and hot smoked garfish (Belone belone euxini, Günther, 1866) at ambient and refrigerated temperatures.

The objective of this study is to determine quality changes and shelf life of hot-smoked garfish (Belone belone euxini, Gunther, 1866) in terms of chemical and sensory analyses at refrigerated and ambient conditions. For this purpose, total volatile basic nitrogen (TVB-N), thiobarbituric acid (TBA), trimethylamine (TMA) and sensory analyses were carried out daily during storage period. Proximate composition of raw and smoked garfish and the percentage of yield for smoking process were also determined. Un-smoked fish samples were used as control groups. According to TVB-N results, the samples of control groups, spoilt on the 3rd and 6th days at ambient and refrigerated temperatures, respectively. Whereas the smoked samples stored ambient and refrigerated temperatures, spoiled on 9th and the 25th days, respectively. TMA and TBA results did not support these results and values were still found within acceptable range on the mentioned days. According to sensory analyses, the smoked samples had shelf-life for one week at ambient temperatures and three weeks for refrigerated storage conditions.
Infrared and small target detection based on convolution neural network under the ground-air background

In this paper, a detection method of infrared dim target under ground-sky background is proposed. In view of the situation that there are more edges under the ground-sky background, the top-hat and median filtering algorithm are used for preliminary processing, and a large number of boundaries are estimated by combining Laplacian operator and Canny operator, and then the difference is made to eliminate the influence of boundary on detection, leaving some suspected target points. Finally, a lightweight convolution neural network is used to determine the suspected target points, and the regression problem of the target position is simplified to a binary classification problem. The experimental results show that the proposed algorithm has higher detection probability and lower false alarm rate than the traditional infrared small target detection algorithm under the ground-sky background, and has good detection effect
Realization of target detection in near-infrared remote sensing image

In order to realize the detection of targets in near-infrared remote sensing images, a target detection system based on traditional algorithms and random rough surface theory (Dot-curve system) is established. The traditional algorithm and Dot-curve system algorithm used in the system are studied. First, according to the target's morphological characteristics, gray-scale characteristics, feature invariance, signal scale invariance and other principle characteristics, the traditional target detection algorithm is introduced. Then, the structure diagram of the algorithm in this paper is given, the definition and principle of Dot-curve system algorithm are introduced in detail, and the nonlinear fitting algorithm used in this paper is introduced and explained. After this, based on the analysis and comparison of the target skeleton's main axis gray average, minor axis gray average, main axis gray variance, minor axis gray variance, and angle of the minor axis gray slope, we verified Dot-curve system algorithm by simulation. Finally, the output shows the Dot point of the target and all the main and minor axes, and the gray curve of the main and minor axes after fitting is compared and analyzed. The experimental results show that the simulation fitting effect is good, and the ship target and wake can be detected intuitively. Because of it completely relies on the theory of graphic feature analysis, this method has the advantages of high stability and strong anti-interference ability.
Microbial Precipitation of Arsenic Sulfides in Andean Salt Flats

An abiotic origin has traditionally been assumed for the arsenic minerals realgar and orpiment associated with thermal springs. Microbial precipitation of arsenic, however, has been studied in pure cultures and the isotopic composition of arsenic sulfides associated with some borate deposits suggests a biotic origin for those minerals. The aim of the present study is to demonstrate the role of bacterial arsenic precipitation in the biogeochemical cycle of arsenic in such borate deposits. For this purpose both enrichment and pure cultures were obtained from the natural arsenic minerals and the composition and isotopic signatures of the arsenic sulfide minerals precipitated by the cultures and those associated with boron deposits from an Andean salt flat in northern Chile were compared. Based on the microbiological and chemical evidence gathered, it is concluded that bacteria contributed to the formation of the arsenic minerals. This interpretation is based on the consistent association of a variety of features that strongly indicate microbial involvement in the precipitation process. These include: (1) enrichment and isolation of cultures with arsenic precipitation capacity from arsenic mineral samples, (2) high numbers of arsenic-precipitating bacteria in the Andean minerals and brines, (3) chemical and mineralogical properties of precipitates experimentally formed under biotic and abiotic conditions, (4) similarities in stoichiometry between natural and laboratory obtained minerals, and (5) the consistent depletion in δ34S values for natural versus laboratory obtained sulfides. Thus, microbial precipitation of arsenic sulfides is a geochemically relevant metabolism.
Research on improving imaging field of view of laser imaging through scattering media

Scattering medium is always a problem to be overcome in traditional optical imaging technology. In recent years, a speckle intensity correlation technique has been proposed to achieve high quality reconstruction of targets hidden in the scattering medium. Unfortunately, the imaging field of view (FOV) of this technology is seriously limited by the optical memory effect (OME) range. Here, we propose an iterative point-spread-function deconvolution (IPSFD) method to reconstruct multiple targets. According to the reversibility of the deconvolution algorithm, the IPSFD method starts from a small known target, and estimates the adjacent targets and point-spread-functions continuously by the iterative deconvolution algorithm, and finally reconstructs all targets within around three times the optical memory effect range. Theoretical and experimental results confirm that our method can achieve multi-target reconstruction of the single-shot large FOV speckle pattern. This method may have potential in practical applications biomedical imaging, astronomical imaging, and atmospheric turbulence imaging.
Adhesives made from isocyanates and pyrolysis oils for wood composites

Abstract Wood adhesives derived from softwood bark pyrolysis oils (PO) and isocyanates (pMDI) have been developed for particleboards with a non-conventional blending system. The PO produced by the vacuum pyrolysis Pyrocycling™ process was used to replace between 30 and 40% by weight of the pMDI in a 4% adhesive-containing particleboard. Results showed that mechanical properties (IB, MOE and MOR) and thickness swelling of homogeneous boards exceeded the minimum requirements set by ANSI A208.1-1993 and ASTM 1037-96a standards. The study showed that pyrolysis oils can be mixed at ratios as high as 40% with pMDI and give acceptable interior grade particleboard properties. It was also shown that PO reduces panel to metal adhesion by 86%, in comparison to the control.
Hyperspectral monitoring of wheat stripe rust based on PSO-KELM model

Stripe rust is one of the epidemic diseases that cause massive yield reduction of wheat. The rapid monitoring of wheat stripe rust is of great significance for its control. Hyperspectral remote sensing has been increasingly used in the identification and prediction of crop diseases. In order to determine the appropriate method for monitoring wheat stripe rust by hyperspectral remote sensing, this paper calculates multiple hyperspectral features and solar-induced chlorophyll fluorescence (SIF) characteristics based on canopy measured hyperspectral and radiance. Two feature selection algorithms, variable importance in projection (VIP) and k-neighbor mutual information(k-MI), were used to screen the hyperspectral characteristics and SIF features which are sensitive to wheat stripe rust. The selected feature variables were used as input variables of partial least squares (PLS), extreme learning machine optimized by particle swarm optimization (PSO-ELM) kernel extreme learning machine optimized by particle swarm optimization (PSO-KELM) to construct a wheat stripe rust prediction model. The study shows that the number of features selected by k-MI is less than that of VIP method. Between the models constructed by the optimal features of the two methods, the prediction accuracy of the PSO-KELM model is the best, and the PSO-KELM model constructed by the k-MI method is the most accurate in which the training set R2 is 0.951, the verification set R2 is 0.952, and their corresponding root mean square errors (RMSE) are respectively 0.070 and 0.066. Conclusively, compared with the PLS model and the PSO-ELM model, the PSO-KELM model improves the stability and generalization ability of the model. It can be used as a new method for identifying the incidence of stripe rust and provides new ideas for further realization of large-scale high-precision remote sensing monitoring of crop health status. Keywords: wheat; stripe rust; Hyperspectral; solar-induced chlorophyll fluorescence; feature selection; kernel extreme learning machine; particle swarm optimization;
Interaction support for virtual studio by vibration feedback

Virtual studios, that synthesize video-camera images with CG objects of studio sets and props, are commonly used in professional program production. Since the actors cannot directly see the CG objects in the studio, they must estimate the positions of the CG objects by watching synthesized video displayed on a monitor placed within their field of view. However, it is difficult for actors to look at a monitor while acting because their acting becomes unnatural if gaze goes toward the monitor. Vibration feedback can be used as a way of indicating the positions of CG objects without the actor having to look at the synthesized video and without affecting the shooting or sound pickup. This method attaches a small vibration device to the actor’s body and communicates the distance to the CG objects by varying the intensity and pattern of vibration. A system to verify this method was developed. Subjects evaluated whether the surface of a CG object can be recognized and whether the distance from the surface of the CG object can be estimated on a four-level scale for four cases in an experiment that simulates interactions with a studio set and props. The experimenter observed whether the subject's hand penetrated the CG object in the synthesized video. As a result, it was found that vibration feedback is very useful for interactions with studio sets except for close interactions like handling props.
Detection method of tubular target leakage based on deep learning

Aiming at the problem that the previous tubular target leak detection method is not sensitive to small leakage and slow leakage and false alarm caused by external interference is not strong, tubular target leakage detection method based on deep learning is proposed. Firstly, the target detection network YOLO3 model is used to detect the tubular target in optical images. By analyzing the test results, the YOLO3 based tubular target leakage detection network is optimized and improved from three aspects: data set expansion, detection mode and network structure. Including: 1) using data transformation using rotation transformation, color dithering, zoom transformation, shift transformation and flip transformation on the data set; 2) according to the characteristics of the tubular target images, the detection method of polygon frame selection is used; 3) simplifying the network structure of the detection and output part. Finally, the improved network is trained and verified. The experimental results show that compared with the YOLO3 network model, the recognition accuracy and recall rate of the tubular target and the leaked area are greatly improved, and the average detection time is also reduced.