Enormous studies have corroborated that long non-coding RNAs (lncRNAs) extensively participate in crucial physiological processes such as metabolism and immunity, and are closely related to the ...occurrence and development of tumors, cardiovascular diseases, nervous system disorders, nephropathy, and other diseases. The application of lncRNAs as biomarkers or intervention targets can provide new insights into the diagnosis and treatment of diseases. This paper has focused on the emerging research into lncRNAs as pharmacological targets and has reviewed the transition of lncRNAs from the role of disease coding to acting as drug candidates, including the current status and progress in preclinical research. Cutting-edge strategies for lncRNA modulation have been summarized, including the sources of lncRNA-related drugs, such as genetic technology and small-molecule compounds, and related delivery methods. The current progress of clinical trials of lncRNA-targeting drugs is also discussed. This information will form a latest updated reference for research and development of lncRNA-based drugs.
This review summarizes the current knowledge on pre- and clinical transformation of lncRNAs-based drugs, covering latest strategies to target pathogenic lncRNAs, indispensable delivery systems, arising clinical trials, future directions and challenges. Display omitted
In infrared spectroscopy analysis area, quantitative modeling is often an essential and inevitable procedure to obtain the relationship between collected spectral information and target components. ...Over the past decades, there are many linear or nonlinear methods have been proposed, such as multi-regression, partial least squares, artificial neural network, support vector machine etc. However, these traditional methods commonly need some preprocessing steps including denoising, baseline correction, wavelength selection and so on. Hence, it requires the users to master so many skilled knowledges before they can establish a good performance quantitative model. Additionally, the stabilities of the above-mentioned methods are often not well enough because there are many random parameters which will result in the model's output are always changing every time. To solve these problems, this paper proposed an end-to-end modeling method based on convolutional neural network and ensemble learning. The experimental results on three infrared spectral datasets (corn, gasoline and mixed gases) showed that the generalized performance of proposed ECNN method outperforms traditional methods like PLS, BP neural network and single CNN method with whole spectral range raw data instead of selected wavelengths. Hence, the proposed method can obviously reduce the modeling knowledge for users and easier to use.
•A novel end-to-end quantitative analysis modeling method for infrared spectroscopy was proposed based on convolutional neural network (CNN), which can directly take the whole range of collected raw spectral information as input without wavelength selectionpreprocessing.•An ensemble framework wasbuilt to improve the stability(also called “robustness”)of quantitative analysis model by introducing ensemble learning ideas.•The performance of proposed method is investigated through two public and one experimentaldatasets in the area ofinfrared spectroscopy.
A significant proportion of migrant children in China are not able to attend public schools for the lack of local household registration (HuKou), and turn to privately-operated migrant schools. This ...paper examines the consequences of such a partially involuntary school choice, using survey data and standardized test scores from field work conducted in Shanghai. We find that migrant students who are unable to enroll in public schools perform significantly worse than their more fortunate counterparts in both Chinese and Mathematics. We also use parental satisfaction and parental assessment of school quality as alternative measures of the educational outcome and find similar results. Our study suggests that access to public schools is the key factor determining the quality of education that migrant children receive.
•We use self-collected survey data and administered standardized tests to study the educational consequences associated with attending migrant schools.•We examine test scores, parental overall satisfaction and parental assessed school quality.•We find that school type is the most important determinant of educational outcomes and overwhelms many important student and family characteristics.
From July 2009 through September 2010, PM10 and PM2.5 were collected at two different functional areas in Shanghai (Baoshan district, an industrial area, and Putuo district, a mixed-use area of ...residential, commercial, and educational compounds). In our analysis, 15 elements were determined using a 710-ES Inductively Coupled Plasma-Emission Spectrometer (ICP-AES). The contents of PM2.5, PM10, and metal elements at the two different sites were comparatively analyzed. The results show that the mean annual concentrations of PM10 and PM2.5 (149.22 μg m−3 and 103.07 μg m−3, respectively) in Baoshan district were significantly higher than those in Putuo district (97.44 μg m−3 and 62.25 μg m−3 respectively). The concentrations of PM10 and PM2.5 were both greatest in winter and lowest in summer, with the two different sites exhibiting the same seasonal variation. It was found that the proportions of 15 metal elements in PM10 and PM2.5 in Baoshan district were 20.49% and 20.56%, respectively, while the proportions in Putuo district were higher (25.98% and 25.93%, respectively). In addition, the proportions of eight heavy metals in PM10 and PM2.5 were 5.50% and 3.07%, respectively, for Baoshan district, while these proportions in Putuo district were 3.18% and 2.77%, respectively, indicating that heavy metal pollution is more pronounced in Baoshan district. Compared with cities in developed countries, the total levels of PM10, PM2.5 and heavy metals in Shanghai were slightly higher. Scanning electron microscopy (SEM) and principal component analysis (PCA) suggested that the possible sources of PM10 in Baoshan district were ground level fugitive dust, traffic sources, and industrial activities, whereas PM2.5 mainly originated from industrial activities, coal combustion, and traffic sources. The sources are same for PM10 and PM2.5 in Putuo region, which originate from traffic sources and ground level fugitive dust.
► Analyzed the contents of PM2.5, PM10, metal elements at different functional areas. ► Compared the levels of PM10, PM2.5, heavy metals with cities in other countries. ► The total levels of PM and heavy metals were slightly higher than developed countries. ► Identify the possible sources of PM10 and PM2.5 with SEM and PCA. ► Preliminary source apportionment results suggested that PM mainly from local sources.
Refined fundus image segmentation results of diabetic retinopathy can better assist doctors in diagnosis.The appearance of large scale and high resolution segmentation data sets provides favorable ...conditions for more refined segmentation.The mainstream segmentation network based on U-Net, using convolution operation based on local operation, cannot fully excavate global information when making pixel prediction.The network model adopts single-input single-output structure, which makes it difficult to obtain multi-scale feature information.In order to maximize the use of existing large-scale high-resolution fundus image focal segmentation data sets and achieve more refined segmentation, better segmentation methods need to be designed.In this paper, U-Net is transformed based on the self-attention mechanism and multi-scale input/output structure, and a new segmentation network, SAM-Net, is proposed.The self-attention module is used to replace the traditional convolutional module, and the ability of the network t
"Functional cure" is being pursued as the ultimate endpoint of antiviral treatment in chronic hepatitis B (CHB), which is characterized by loss of HBsAg whether or not anti-HBs antibodies are ...present. "Functional cure" can be achieved in <10% of CHB patients with currently available therapeutic agents. The dysfunction of specific immune responses to hepatitis B virus (HBV) is considered the major cause of persistent HBV infection. Thus, modulating the host immune system to strengthen specific cellular immune reactions might help eliminate HBV. Strategies are needed to restore/enhance innate immunity and induce HBV-specific adaptive immune responses in a coordinated way. Immune and resident cells express pattern recognition receptors like TLRs and RIG I/MDA5, which play important roles in the induction of innate immunity through sensing of pathogen-associated molecular patterns (PAMPs) and bridging to adaptive immunity for pathogen-specific immune control. TLR/RIG I agonists activate innate immune responses and suppress HBV replication
and
, and are being investigated in clinical trials. On the other hand, HBV-specific immune responses could be induced by therapeutic vaccines, including protein (HBsAg/preS and HBcAg), DNA, and viral vector-based vaccines. More than 50 clinical trials have been performed to assess therapeutic vaccines in CHB treatment, some of which display potential effects. Most recently, using genetic editing technology to generate CAR-T or TCR-T, HBV-specific T cells have been produced to efficiently clear HBV. This review summarizes the progress in basic and clinical research investigating immunomodulatory strategies for curing chronic HBV infection, and critically discusses the rather disappointing results of current clinical trials and future strategies.
Traffic data imputation is critical for both research and applications of intelligent transportation systems. To develop traffic data imputation models with high accuracy, traffic data must be large ...and diverse, which is costly. An alternative is to use synthetic traffic data, which is cheap and easy-access. In this paper, we propose a novel approach using parallel data and generative adversarial networks (GANs) to enhance traffic data imputation. Parallel data is a recently proposed method of using synthetic and real data for data mining and data-driven process, in which we apply GANs to generate synthetic traffic data. As it is difficult for the standard GAN algorithm to generate time-dependent traffic flow data, we made twofold modifications: 1) using the real data or the corrupted ones instead of random vectors as latent codes to generator within GANs and 2) introducing a representation loss to measure discrepancy between the synthetic data and the real data. The experimental results on a real traffic dataset demonstrate that our method can significantly improve the performance of traffic data imputation.
Empirical analysis of bitcoin price Chen, Yuanyuan (Catherine)
Journal of economics and finance,
10/2021, Letnik:
45, Številka:
4
Journal Article
Recenzirano
This paper, merging the stories of monetary theory, management analysis, computer science, and finance, comprehensively studies different forces that affect the bitcoin price. After conducting ...various stationarity tests and cointegration test, I choose the VEC model as the baseline to estimate the bitcoin price empirically. I also include competing methodologies, which have been used in previous studies, on my data set. These methodologies are VAR and ADRL models. With the daily data of 2009–2019, my baseline model shows that in the short run the bitcoin price is mainly affected by the medium of exchange and financial expectation forces, while blockchain technology factors only show a small impact on the bitcoin price. Moreover, using different econometric models yields different results in the short run. To investigate whether some conflicts among previous research can be explained by different specifications of the data, I also conduct two kinds of robustness checks. One is to estimate the same variables between bear and bull states. The states are found using Markov switching model. The other check is to focus on supply and demand forces, particularly during high volatility periods. Both checks find that in different states the effects can be different both in the size or in the significance. Lastly, I also find that effects are different between the short run and the long run.
Synthetic hydrogels or water-containing polymeric materials are much inferior to biological tissues and solid plastics in many aspects of mechanical properties; it is a great challenge to develop ...hydrogels with mechanical properties comparable with or even superior to those of biological tissues and plastics. Here, we report a type of super-strong and tough hydrogen-bonded poly(vinyl alcohol)/poly(acrylic acid) (PVA/PAA) hydrogel by immersing as-prepared PVA hydrogels in aqueous PAA solutions and then cold-drawing the hydrogels to different strains. The immersing process introduces PAA chains into the PVA hydrogels, which increases the cross-linking density by hydrogen bonding and hence, much improved mechanical properties and low water contents (35.9-40.2 wt%) are observed. The cold-drawing orients the polymer chains, which enables the formation of more and stronger hydrogen bonds. The mechanical properties of cold-drawn gels are dramatically enhanced, with tensile strength and elastic modulus up to 140 and 100 MPa, respectively; also, super-high toughness (117 MJ m
) and fracture energy (101 kJ m
) are obtained. Very impressively, the ultra-high tensile strengths of the cold-drawn hydrogels are superior to those of biological tissues and most solid engineered plastics. Characterizations and comparative studies prove that the enhancement of mechanical properties is mainly due to the formation of more hydrogen bonding rather than the loss of water or the change in crystallinity. This study provides a new strategy for preparing super-strong physically cross-linked hydrogels and other polymeric materials. This super-strong and tough hydrogel may find potential applications in biomedical and load-bearing fields.
Gold nanoparticles (AuNPs) have been widely studied and applied in the field of tumor diagnosis and treatment because of their special fundamental properties. In order to make AuNPs more suitable for ...tumor diagnosis and treatment, their natural properties and the interrelationships between these properties should be systematically and profoundly understood. The natural properties of AuNPs were discussed from two aspects: physical and chemical. Among the physical properties of AuNPs, localized surface plasmon resonance (LSPR), radioactivity and high X-ray absorption coefficient are widely used in the diagnosis and treatment of tumors. As an advantage over many other nanoparticles in chemicals, AuNPs can form stable chemical bonds with S-and N-containing groups. This allows AuNPs to attach to a wide variety of organic ligands or polymers with a specific function. These surface modifications endow AuNPs with outstanding biocompatibility, targeting and drug delivery capabilities. In this review, we systematically summarized the physicochemical properties of AuNPs and their intrinsic relationships. Then the latest research advancements and the developments of basic research and clinical trials using these properties are summarized. Further, the difficulties to be overcome and possible solutions in the process from basic laboratory research to clinical application are discussed. Finally, the possibility of applying the results to clinical trials was estimated. We hope to provide a reference for peer researchers to better utilize the excellent physicochemical properties of gold nanoparticles in oncotherapy.
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