The Clinical Relevance of Serum NDKA, NMDA, PARK7, and UFDP Levels with Phlegm-Heat Syndrome and Treatment Efficacy Evaluation of Traditional Chinese Medicine in Acute Ischemic Stroke

According to the methods of Patient-Reported Outcome (PRO) based on the patient reports internationally and referring to U.S. Food and Drug Administration (FDA) guide, some scholars developed this PRO of stroke which is consistent with China’s national conditions, and using it the feel of stroke patients was introduced into the clinical efficacy evaluation system of stoke. “Ischemic Stroke TCM Syndrome Factor Diagnostic Scale (ISTSFDS)” and “Ischemic Stroke TCM Syndrome Factor Evaluation Scale (ISTSFES)” were by “Major State Basic Research Development Program of China (973 Program) (number 2003CB517102).” ISTSFDS can help to classify and diagnose the CM syndrome reasonably and objectively with application of syndrome factors. Six syndrome factors, internal-wind syndrome, internal-fire syndrome, phlegm-dampness syndrome, blood-stasis syndrome, qi-deficiency syndrome, and yin-deficiency syndrome, were included in ISTSFDS and ISTSFES. TCM syndrome factor was considered to be present if the score was greater than or equal to 10 according to ISTSFDS. In our study, patients with phlegm-heat syndrome were recruited, who met the diagnosis of both “phlegm-dampness” and “internal-fire” according to ISTSFDS. ISTSFES was used to assess the syndrome severity; in our study it was used to assess the severity of phlegm-heat syndrome (phlegm-heat syndrome scores = phlegm-dampness syndrome scores + internal-fire syndrome scores)

FL-SDUAN: A Fuzzy Logic-Based Routing Scheme for Software-Defined Underwater Acoustic Networks

In underwater acoustic networks, the accurate estimation of routing weights is NP-hard due to the time-varying environment. Fuzzy logic is a powerful tool for dealing with vague problems. Software-defined networking (SDN) is a promising technology that enables flexible management by decoupling the data plane from the control plane. Inspired by this, we proposed a fuzzy logic-based software-defined routing scheme for underwater acoustic networks (FL-SDUAN). Specifically, we designed a software-defined underwater acoustic network architecture. Based on fuzzy path optimization (FPO-MST) and fuzzy cut-set optimization (FCO-MST), two minimum spanning tree algorithms under different network scales were proposed. In addition, we compared the proposed algorithms to state-of-the-art methods regarding packet delivery rate, end-to-end latency, and throughput in different underwater acoustic network scenarios. Extensive experiments demonstrated that a trade-off between performance and complexity was achieved in our work

Current prodrug strategies for improving oral absorption of nucleoside analogues

Nucleoside analogues are first line chemotherapy in various severe diseases: AIDS (acquired immunodeficiency disease syndrome), cytomegalovirus infections, cancer, etc. However, many nucleoside analogues exhibit poor oral bioavailability because of their high polarity and low intestinal permeability. In order to get around this drawback, prodrugs have been utilized to improve lipophilicity by chemical modification of the parent drug. Alternatively, prodrugs targeting transporters present in the intestine have been applied to promote the transport of the nucleoside analogues. Valacyclovir and valganciclovir are two classic valine ester prodrugs transported by oligopeptide transporter 1. The ideal prodrug achieves delivery of a parent drug by attaching a non-toxic moiety that is stable during transport, but is readily degraded to the parent drug once at the target. This article presents advances of prodrug approaches for enhancing oral absorption of nucleoside analogues

Review of Remote Sensing Applications in Grassland Monitoring

The application of remote sensing technology in grassland monitoring and management has been ongoing for decades. Compared with traditional ground measurements, remote sensing technology has the overall advantage of convenience, efficiency, and cost effectiveness, especially over large areas. This paper provides a comprehensive review of the latest remote sensing estimation methods for some critical grassland parameters, including above-ground biomass, primary productivity, fractional vegetation cover, and leaf area index. Then, the applications of remote sensing monitoring are also reviewed from the perspective of their use of these parameters and other remote sensing data. In detail, grassland degradation and grassland use monitoring are evaluated. In addition, disaster monitoring and carbon cycle monitoring are also included. Overall, most studies have used empirical models and statistical regression models, while the number of machine learning approaches has an increasing trend. In addition, some specialized methods, such as the light use efficiency approaches for primary productivity and the mixed pixel decomposition methods for vegetation coverage, have been widely used and improved. However, all the above methods have certain limitations. For future work, it is recommended that most applications should adopt the advanced estimation methods rather than simple statistical regression models. In particular, the potential of deep learning in processing high-dimensional data and fitting non-linear relationships should be further explored. Meanwhile, it is also important to explore the potential of some new vegetation indices based on the spectral characteristics of the specific grassland under study. Finally, the fusion of multi-source images should also be considered to address the deficiencies in information and resolution of remote sensing images acquired by a single sensor or satellite

Review of Remote Sensing Applications in Grassland Monitoring

The application of remote sensing technology in grassland monitoring and management has been ongoing for decades. Compared with traditional ground measurements, remote sensing technology has the overall advantage of convenience, efficiency, and cost effectiveness, especially over large areas. This paper provides a comprehensive review of the latest remote sensing estimation methods for some critical grassland parameters, including above-ground biomass, primary productivity, fractional vegetation cover, and leaf area index. Then, the applications of remote sensing monitoring are also reviewed from the perspective of their use of these parameters and other remote sensing data. In detail, grassland degradation and grassland use monitoring are evaluated. In addition, disaster monitoring and carbon cycle monitoring are also included. Overall, most studies have used empirical models and statistical regression models, while the number of machine learning approaches has an increasing trend. In addition, some specialized methods, such as the light use efficiency approaches for primary productivity and the mixed pixel decomposition methods for vegetation coverage, have been widely used and improved. However, all the above methods have certain limitations. For future work, it is recommended that most applications should adopt the advanced estimation methods rather than simple statistical regression models. In particular, the potential of deep learning in processing high-dimensional data and fitting non-linear relationships should be further explored. Meanwhile, it is also important to explore the potential of some new vegetation indices based on the spectral characteristics of the specific grassland under study. Finally, the fusion of multi-source images should also be considered to address the deficiencies in information and resolution of remote sensing images acquired by a single sensor or satellite

Progress of Research into Preformed Particle Gels for Profile Control and Water Shutoff Techniques

Gel treatment is an economical and efficient method of controlling excessive water production. The gelation of in situ gels is prone to being affected by the dilution of formation water, chromatographic during the transportation process, and thus controlling the gelation time and penetration depth is a challenging task. Therefore, a novel gel system termed preformed particle gels (PPGs) has been developed to overcome the drawbacks of in situ gels. PPGs are superabsorbent polymer gels which can swell but not dissolve in brines. Typically, PPGs are a granular gels formed based on the crosslinking of polyacrylamide, characterized by controllable particle size and strength. This work summarizes the application scenarios of PPGs and elucidates their plugging mechanisms. Additionally, several newly developed PPG systems such as high-temperature-resistant PPGs, re-crosslinkable PPGs, and delayed-swelling PPGs are also covered. This research indicates that PPGs can selectively block the formation of fractures or high-permeability channels. The performance of the novel modified PPGs was superior to in situ gels in harsh environments. Lastly, we outlined recommended improvements for the novel PPGs and suggested future research directions

Generating Image Descriptions of Rice Diseases and Pests Based on DeiT Feature Encoder

We propose a DeiT (Data-Efficient Image Transformer) feature encoder-based algorithm for identifying disease types and generating relevant descriptions of diseased crops. It solves the scarcity problem of the image description algorithm applied in agriculture. We divided the original image into a sequence of image patches to fit the input form of the DeiT encoder, which was distilled by RegNet. Then, we used the Transformer decoder to generate descriptions. Compared to “CNN + LSTM” models, our proposed model is entirely convolution-free and has high training efficiency. On the Rice2k dataset created by us, the model achieved a 47.3 BLEU-4 score, 65.0 ROUGE_L score, and 177.1 CIDEr score. The extensive experiments demonstrate the effectiveness and the strong robustness of our model. It can be better applied to automatically generate descriptions of similar crop disease characteristics

Research on stress variation law of floor under coal pillar with different widths based on three-hinged oblique arch

In order to obtain the overlying strata structure and the stress transfer law of floor under the influence of section coal pillar after the upper protective layer is fully mined, the rational arch axis equation is deduced based on the three-hinged inclined arch theory. The stress distribution of coal pillar floor with different widths is simulated numerically. Based on two methods of three-hinged inclined arch and elastic mechanics theory, the analytic solution of stress distribution of bottom plate of protective layer is obtained and verified by field measurement. The results show that the shape parameters of three-hinged inclined arch are dynamically correlated with the width of retained coal pillar, the mining height of coal seam and the pressure relief width of goaf. When the width of coal pillar is larger than 15 m, the additional stress caused by three-hinged inclined arch structure will occur in the re-compacted area of goaf floor, and the extreme stress point will move to the center of goaf with the increase of coal pillar width. The field test shows that the stoping roadway of protected layer should be arranged in the wrong way, and the reasonable wrong distance is 10-20 m