Miracle-dz/UniFAR-FTUs huggingface dataset:
Small but Mighty—Exosomes, Novel Intercellular Messengers in Neurodegeneration

Simple Summary Exosomes are biological nanoparticles recently recognized as intercellular messengers. They contain a cargo of lipids, proteins, and RNA. They can transfer their content to not only cells in the vicinity but also to cells at a distance. This unique ability empowers them to modulate the physiology of recipient cells. In brain, exosomes play a role in neurodegenerative diseases such as Alzheimer’s disease and Parkinson’s disease and amyotrophic lateral sclerosis. Abstract Exosomes of endosomal origin are one class of extracellular vesicles that are important in intercellular communication. Exosomes are released by all cells in our body and their cargo consisting of lipids, proteins and nucleic acids has a footprint reflective of their parental origin. The exosomal cargo has the power to modulate the physiology of recipient cells in the vicinity of the releasing cells or cells at a distance. Harnessing the potential of exosomes relies upon the purity of exosome preparation. Hence, many methods for isolation have been developed and we provide a succinct summary of several methods. In spite of the seclusion imposed by the blood–brain barrier, cells in the CNS are not immune from exosomal intrusive influences. Both neurons and glia release exosomes, often in an activity-dependent manner. A brief description of exosomes released by different cells in the brain and their role in maintaining CNS homeostasis is provided. The hallmark of several neurodegenerative diseases is the accumulation of protein aggregates. Recent studies implicate exosomes’ intercellular communicator role in the spread of misfolded proteins aiding the propagation of pathology. In this review, we discuss the potential contributions made by exosomes in progression of Alzheimer’s disease, Parkinson’s disease, and amyotrophic lateral sclerosis. Understanding contributions made by exosomes in pathogenesis of neurodegeneration opens the field for employing exosomes as therapeutic agents for drug delivery to brain since exosomes do cross the blood–brain barrier.
Identification and Classification of Mechanical Damage During Continuous Harvesting of Root Crops Using Computer Vision Methods

Detecting sugar beetroot crops with mechanical damage using machine learning methods is necessary for fine-tuning beet harvester units. The Agrifac HEXX TRAXX harvester with an installed computer vision system was investigated. A video camera (24 fps) was installed above the turbine, which receives the dug-out beets after the digger and is connected to a single-board computer. At the preprocessing stage, static and insignificant image details were revealed. Canny edge detector and excess green minus excess red (ExGR) method were used. The identified areas were excluded from the image. The remaining areas were glued with similar areas of another image. As a result, the number of images entering the second stage of preprocessing was reduced by half. Then Otsu’s binarization was used. The main stage of image processing is divided into two sub-stages: detection and classification. The improved YOLOv4-tiny method was chosen for root crop detection using a single-board computer (SBC). This method allows processing up to 14 images of 416 $\times416$ pixels with 86% precision and 91% recall. To classify root crop damage, we considered two algorithms as candidates: 1. bag of visual words (BoVW) with a support vector machine (SVM) classifier using histogram of oriented gradients (HOG) and scale-invariant feature transform (SIFT) descriptors; 2. convolutional neural networks (CNN). Under normal lighting conditions, CNN showed the best accuracy, which was 99%. The implemented methods were used to detect and classify blurred images of sugar beetroots, which were previously rejected. For improved YOLOv4-tiny precision was 74% and recall was 70%. CNN classification accuracy was 92.6%.
Wireless passive flexible accelerometer fabricated using micro-electro-mechanical system technology for bending structure surfaces

We propose an inductor-capacitor (LC) wireless passive flexible accelerometer, which eliminates the difficulty in measuring the acceleration on the surface of a bending structure. The accelerometer is composed of a flexible polyimide (PI) substrate and a planar spiral inductance coil (thickness 300 nm), made using micro-electro-mechanical system (MEMS) technology. It can be bent or folded at will, and can be attached firmly to the surface of objects with a bending structure. The principle of radio frequency wireless transmission is used to measure the acceleration signal by changing the distance between the accelerometer and the antenna. Compared with other accelerometers with a lead wire, the accelerometer can prevent the lead from falling off in the course of vibration, thereby prolonging its service life. Through establishment of an experimental platform, when the distance between the antenna and accelerometer was 5 mm, the characterization of the surface of bending structures demonstrated the sensing capabilities of the accelerometer at accelerations of 20–100 m/s2. The results indicate that the acceleration and peak-to-peak output voltage were nearly linear, with accelerometer sensitivity reaching 0.27 mV/(m·s−2). Moreover, the maximum error of the accelerometer was less than 0.037%.
Combined Activity of the Redox-Modulating Compound Setanaxib (GKT137831) with Cytotoxic Agents in the Killing of Acute Myeloid Leukemia Cells

Acute myeloid leukemia (AML) cells harbor elevated levels of reactive oxygen species (ROS), which promote cell proliferation and cause oxidative stress. Therefore, the inhibition of ROS formation or elevation beyond a toxic level have been considered as therapeutic strategies. ROS elevation has recently been linked to enhanced NADPH oxidase 4 (NOX4) activity. Therefore, the compound Setanaxib (GKT137831), a clinically advanced ROS-modulating substance, which has initially been identified as a NOX1/4 inhibitor, was tested for its inhibitory activity on AML cells. Setanaxib showed antiproliferative activity as single compound, and strongly enhanced the cytotoxic action of anthracyclines such as daunorubicin in vitro. Setanaxib attenuated disease in a mouse model of FLT3-ITD driven myeloproliferation in vivo. Setanaxib did not significantly inhibit FLT3-ITD signaling, including FLT3 autophosphorylation, activation of STAT5, AKT, or extracellular signal regulated kinase 1 and 2 (ERK1/2). Surprisingly, the effects of Setanaxib on cell proliferation appeared to be independent of the presence of NOX4 and were not associated with ROS quenching. Instead, Setanaxib caused elevation of ROS levels in the AML cells and importantly, enhanced anthracycline-induced ROS formation, which may contribute to the combined effects. Further assessment of Setanaxib as potential enhancer of cytotoxic AML therapy appears warranted.
Modeling of Buildings Based on ArcObjects

In recent years,the research about 3DGIS is popular,in particular the realization of 3D Scene is pop.Since the models based on professional software are not suitable to 3DGIS and some developing tool boxes are too difficult for us to use,the method to extend the existing GIS′s function of modeling by using the components becomes a good choose for users.In this paper,the function of modeling the buildings was programmed by Visual Basic and ArcObjects.As the result,the buildings could be modeled in ArcScene.
Indonesia Tourism Enterprises' Stock Returns Research: Looking Back and Moving Forward

The objective of this research is to compare movement of stock return literature on companies in the tourism sector and other businesses. This study also analyzed comprehensively the relationship between contexts built on the literature. The mix method approach is adopted by combining text mining methods, which are analyzed using network and content analysis methods. The scientific literature was collected using the web scraping method on Litmaps and selected 200 articles and only used 195 articles (between 2000-2021) related to the topic of stock returns and 23 (between 2013-2020) related to the tourism company sector. In the process of data analysis and visualization, this study uses AntConc, RStudio, and Gephi software. This review discovered that in terms of topic and context, stock return studies discuss issues related to the influence of fundamental factors on stock returns, and vice versa, while the approach used is mostly quantitative based on the financial performance reports. Regarding the frequency of appearance, this result revealed that the effects of fundamental factors on stock returns in tourism companies can be translated into a number of strategic issues, including Current Ratio, TATO, ROE, FATO, and Systematic Risk, which provide a significant and positive effect on Direct Share Prices. Furthermore, the Current Ratio, TATO, ROE, and FATO are thought to have a significant and positive effect on stock returns via Systematic Risk. The scope of this research is limited to the national journal (Indonesia) on the Litmaps search engine. In theory, the results of this research can be used as a comparison and considerations related to previous studies, as well as what investigation requires to be done immediately, in the context of tourism companies' stock returns. The literature study on the tourism companies’ stock returns issue is perhaps the first systematic literature review in Indonesia.
Study of potential capacity for Cr(VI) reduction by Staphylococcus gallinarum strain VIII1 immobilized, resting and permeabilized

ABSTRACT Bioremediation uses microorganisms to detoxify or remove pollution. This paper reports a study to evaluate the efficacy of removing hexavalent chromium (Cr(VI)) using a metal-resistant bacterium. Molecular identification reveals it to be Staphylococcus gallinarum strain VIII1. This can reduce 100 mg/L of Cr(VI) after 72 h at pH 7 and 30°C. Abatement rate is increased in the presence of glucose, glycerol and copper ions. Immobilised cells in sodium alginate beads showed a high reduction efficiency compared to free cells. Bioreduction by resting and permeabilized cells confirms that this strain removes chromate ions by an enzymatic process.
Machine intelligence for chemical reaction space

Discovering new reactions, optimizing their performance, and extending the synthetically accessible chemical space are critical drivers for major technological advances and more sustainable processes. The current wave of machine intelligence is revolutionizing all data‐rich disciplines. Machine intelligence has emerged as a potential game‐changer for chemical reaction space exploration and the synthesis of novel molecules and materials. Herein, we will address the recent development of data‐driven technologies for chemical reaction tasks, including forward reaction prediction, retrosynthesis, reaction optimization, catalysts design, inference of experimental procedures, and reaction classification. Accurate predictions of chemical reactivity are changing the R&D processes and, at the same time, promoting an accelerated discovery scheme both in academia and across chemical and pharmaceutical industries. This work will help to clarify the key contributions in the fields and the open challenges that remain to be addressed.
Comparative ambient and indoor particulate matter analysis of operation theatres of government and private (trust) Hospitals of Lahore, Pakistan

© 2015, Pakistan Agricultural Scientists Forum. All rights reserved. The link between infection and indoor air quality (IAQ) in operating theatres is well established. The level of airborne particulate matter (PM) in operating theatres in Pakistan has not yet been studied comprehensively. Monitoring of both indoor (operating theatre) and outdoor concentrations of PM in both activity and non-activity time periods was done using a DUSTTRAK Aerosol Monitor (TSI Model 8520) and DRX Aerosol Monitor (TSI Model 8533) for 24 hours. Two hospitals in Lahore were selected: Services Hospital (government – site 1) and Shalamar Hospital (private – site 2). The highest concentration of PM was observed in the orthopaedic operating theatre at site 1 during working hours with an average concentration of 757(±540), 809(±58), 824(±585), 875(±586) and 970(±581) μg/m3 of PM 1 ,PM 2.5 , PM 4 , PM 10 and PMTotal respectively while the average PM 2.5 outdoor concentration was 294 μg/m3. The minimum average PM concentration was found in the orthopaedic operating theatre at site 2 during working hours: 18(±8), 19(±8), 20(±9), 26(±9) and 39(±9) μg/m3 for PM 1 , PM 2.5 , PM 4 , PM 10 and PMTotal respectively. The use of vertical laminar air flow ventilation strategy was found to be an effective measure in reducing PM levels and it might be possible to predict the air quality of operating theatres by determining PM dust load. Factors such as ventilation system, door opening /closing rates, building age, possible sources of infiltration, number of people present in the operating area all play a role in influencing PM concentrations in operating theatres.
Perceived Risk of Job Instability and Unethical Organizational Behaviour Amid the COVID-19 Pandemic: The Role of Family Financial Pressure and Distributive Injustice in the Tourism Industry

The economic disaster precipitated by the pandemic of COVID-19 changed people’s perceptions of ordinary job stability and elevated it to an ultimate high level. To avoid being laid off, employees who are concerned about job stability may engage in unethical activities in the name of their employer. In this study, the influence of job instability on unethical organizational behaviour (UOB) was investigated through the mediating role of family financial pressure and distributive injustice. Perceptions of 830 employees working in hotels (5-star and 4-star) and travel agencies (Category A) were explored and further analyzed using structural equation modelling. The results asserted that family financial pressure and distributive injustice partially mediated the effects of job insecurity on UOB. Important insights on theoretical and practical implications were further deliberated towards the end of this study.
Recent progress in the applications of gold-based nanoparticles towards tumor-targeted imaging and therapy

Cancer death rate remains high all over the world, scientists are paying increasing attention to meet the requirements for precise diagnosis and therapy. Therefore, early diagnosis and active treatment can effectively improve the five-year survival rate of patients. In recent years, gold-based nanomaterials have received increasing attention in medical fields due to their excellent biocompatibility, low toxicity and unique properties. In addition, because of the inherent nature of gold nanomaterials including for computed tomography (CT), fluorescence/optical imaging (FI/OI), surface enhanced Raman spectroscopy imaging (SERS), photoacoustic imaging (PAI) and photothermal therapy (PTT), various gold nanomaterials were developed as theranostic nanoplatforms. In this review, we summarized the latest developments of nanomaterials in imaging and combined therapy, and the prospects for the future application of gold-based theranostic nanoplatforms were also proposed.
Study on the Operation Performance of Pipeline Air-lift System

In order to analyze influencing factors for operation performance of the pipeline air-lift system,experiments were made in a simulation system with a lifting-pipe 30 m in height and 100 mm in diameter.After a group of tests by adjusting submergence ratio,supplied air volume,concentration of the transported slurry,the relation curves among flow rate of transported slurry,lifting rate and lifting efficiency of nodules and supplied air volume were obtained,the operating law of the system has also been summarized as parameters varied.Moreover,the effectiveness of the calculation model under critical condition for particle incipient motion is verified.
LTASIM: A Desktop Nonlinear Airship Simulation

A comprehensive nonlinear 6 DOF simulation model for modern airships has been developed (LTASIM) using the TUTSIM-6 program for use on PCcompatible computers. The model includes nonlinear aerodynamics and buoyancy effects, various thrustor types, important virtual mass and inertia tensor terms, non-constant wind effects, stability and trim controlsystem, airship-to-target relative motions. On a fast 25MHz 486-type computer, it will run much faster than the simulated time interval, thereby offering possibilities for use in real-time simulation devices.
Distribution and Attribution of Gross Primary Productivity Increase Over the Mongolian Plateau, 2001-2018

Gross primary productivity (GPP) over the Mongolian Plateau (MP) is a vital component of the global terrestrial carbon cycle. Using the latest MODIS GPP estimates at the best achievable spatial resolution along with several ancillary datasets, we investigated GPP variations in the MP region during 2001–2018 and attributed these changes to land-surface temperature (<inline-formula> <tex-math notation="LaTeX">$T_{\mathrm {s}}$ </tex-math></inline-formula>), total precipitation (<inline-formula> <tex-math notation="LaTeX">$P_{\mathrm {t}}$ </tex-math></inline-formula>), land-cover change (LCC), and atmospheric carbon dioxide (CO<sub>2</sub>) concentrations. The 18-year-averaged annual cumulative GPP in the MP region was 357.02 ± 24.76 gC <inline-formula> <tex-math notation="LaTeX">$\text{m}^{-2}$ </tex-math></inline-formula> yr<sup>−1</sup> during the study period, ranging from 60.51 ± 6.10 gC m<sub>−2</sub> yr<sup>−1</sup> in deserts to 596.41 ± 35.49 gC m<sub>−2</sub> yr<sup>−1</sup> in forests. A linear regression analysis indicated a significant overall increase in GPP, at a rate of 3.91 gC m<sub>−2</sub> yr<sup>−1</sup> (<inline-formula> <tex-math notation="LaTeX">$p < 0.01$ </tex-math></inline-formula>). In comparison, GPP increased at a rate of 0.79 gC m<sub>−2</sub> yr<sup>−1</sup> in deserts (<inline-formula> <tex-math notation="LaTeX">$p < 0.01$ </tex-math></inline-formula>), 4.79 gC m<sub>−2</sub> yr<sup>−1</sup> in forests (<inline-formula> <tex-math notation="LaTeX">$p < 0.01$ </tex-math></inline-formula>), and 5.76 gC m<sub>−2</sub> yr<sup>−1</sup> in grasslands (<inline-formula> <tex-math notation="LaTeX">$p < 0.01$ </tex-math></inline-formula>). Our detailed attribution analysis indicates that GPP is positively sensitive to surface air temperature (0.15 gC °C<sub>−1</sub>) and total precipitation (0.25 gC mm<sup>−1</sup>) but negatively sensitive to atmospheric CO<sub>2</sub> concentrations (−0.20 gC mol<sup>−1</sup>) and LCC (−0.93 gC class<sup>−1</sup>). Furthermore, we reported large differences in the spatial patterns and magnitudes among individual variables in the GPP attribution analysis, with LCC proving to be the dominant factor followed by CO<sub>2</sub> fertilization effects; climatic factors had comparatively little influence on GPP variations during the study period. Although MODIS GPP does not take CO<sub>2</sub> fertilization effect into account, the close relationship between MODIS GPP and atmospheric CO<sub>2</sub> concentrations still pose referencing value in attributing the GPP increase in this period. Overall, the findings of this study contribute to our understanding of the responses of sensitive ecosystems to the competing effects of climate change and human disturbance at regional scales.
Study on TCM Tongue Image Segmentation Model Based on Convolutional Neural Network Fused with Superpixel

Tongue image segmentation is a base work of TCM tongue processing. Nowadays, deep learning methods are widely used on tongue segmentation, which has better performance than conventional methods. However, when the tongue color is close to the color of the adjoining area, the contour of tongue segmentation by deep learning may be coarse which could influence the subsequent analysis. Here a novel tongue image segmentation model based on a convolutional neural network fused with superpixel was proposed to solve the problem. Methods. On the basis of a convolutional neural network fused with superpixel, the novel tongue image segmentation model SpurNet was proposed in this study. The residual structure of ResNet18 was introduced as the feature extraction layer on the encoding path, to construct the first stage processing module UrNet of SpurNet. The superpixel segmentation was fused with UrNet to form the second stage process of SpurNet. To verify the effect of SpurNet. The models before and after fusion with superpixel, classical image segmentation models FCN and DeepLab were compared with SpurNet on the dataset of 367 manually labeled tongue images. Results. The SpurNet model performance test with 10-fold cross-validation showed PA of 0.9145 ± 0.0043, MPA of 0.9168 ± 0.0048, MIoU of 0.8417 ± 0.0072 and FWIoU of 0.8454 ± 0.0072. Relative to FCN, DeepLab and their superpixel fused models, the SpurNet model was superior in tongue image segmentation and could increase PA by 1.91%–3.17%, MPA by 1.38%–2.61%, MIoU by 3.09%–5.07%, and FWIoU by 3.11%–5.08%. Compared to UrNet, the first stage processing module, the SpurNet model also increased the PA, MPA, MIoU and FWIoU by 0.15%, 0.09%, 0.24% and 0.24%, respectively. Conclusion. The SpurNet model, after fusing with superpixel image segmentation, can better accomplish the task of tongue image segmentation, more accurately process the margins of tongue and resolve the over-segmentation and under-segmentation. The thought of this study is a new exploration in the field of tongue image segmentation, which could provide a reference for the modern research on TCM tongue images.
STABLE DEEP EUTECTIC SOLVENT DOPED WITH Metal nanoparticles

In this work, we prepared MeNPs doped DES systems. Gold nanoparticles were prepared on aluminum oxide microparticles surface. DES was prepared from choline chloride and urea, and embedded with the nano/micro particles. Created system was characterized using UV-Vis and FTIR spectroscopy, as well as TEM technique. As the result, we demonstrate that stable deep eutectic solvents that can be used for a range of applications.
Modeling and management of diagnosis and maintenance knowledge for R & D of complex product

In order to model and manage the diagnosis and maintenance knowledge created for RD of complex product, a method of knowledge modeling was proposed aimed to utilize the knowledge effectively. Firstly the concept of knowledge unit was put forward, and the concept of knowledge scenario and knowledge modeling were defined based on knowledge unit. Knowledge model was used to describe different structural forms of knowledge units under different knowledge scenarios. Then the diagnosis maintenance knowledge unit model and pattern of knowledge organization based on product structure tree were set up, and the methods of knowledge reuse and knowledge discovery were provided. Finally the application system model was established and an example was adopted to verify the proposed method.