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Intervertebral disc degeneration (IVDD) is a significant cause of chronic pain and disability, necessitating innovative therapeutic strategies. This study investigates the combined effect of a novel siCOL1A2-encapsulated hydrogel and acupuncture on IVDD in a rat model. We developed a hydrogel system, siCOL1A2-encapsulated G5-PBA hydrogel (siCOL1A2@G5-PBA@Gel), designed for sustained siRNA delivery to the degenerated discs and assessed its therapeutic efficacy alongside acupuncture treatment. Key inflammatory genes were identified through RNA-seq analysis, with COL1A2 highlighted as a crucial regulator of inflammatory responses in IVDD. Our in vivo experiments involved treating rats with hydrogel alone, acupuncture alone, and combining both. The treatments were evaluated through behavioral pain assessments, imaging techniques (X-ray and MRI), and histological analyses. Results indicated that the combination therapy significantly alleviated pain, reduced inflammation, and promoted disc regeneration more effectively than individual treatments. The hydrogel proved biocompatible and facilitated targeted gene silencing, while acupuncture enhanced therapeutic outcomes by improving local blood circulation and modulating inflammatory responses. These findings suggest that integrating siCOL1A2 hydrogel with acupuncture offers a promising approach to treating IVDD.

Recently, a G-protein coupled receptor 44 (GPR44) was discovered to play a significant role in the process of inflammation-related diseases, including cancer and diabetes. However, the precise role of GPR44 has yet to be fully elucidated. Currently, there is a strong and urgent need for the development of GPR44 radiotracers as a non-invasive methodology to explore the exact mechanism of GPR44 on inflammation-related diseases and monitor the progress of therapy. TM-30089 is a potent GPR44 antagonist that exhibits a high specificity and selectivity for GPR44. Its structure contains a fluorine nuclide, which could potentially be replaced with 18F. In the present study, we successfully took a highly effective synthesis strategy that pretreated the unprotected carboxylic acid group of the precursor and developed a feasible one-step automatic radiosynthesis strategy for [18F]TM-30089 with a high radiochemical purity and a good radiochemical yield. We further evaluated this radiotracer using mice models implanted with 1.1 B4 cell lines (GPR44-enriched cell lines) and human islets (high GPR44 expression), respectively. The results revealed the persistent and specific uptake of [18F]TM-30089 in GPR44 region, indicating that [18F]TM-30089 is a promising candidate for targeting GPR44. Further evaluation is ongoing.

Recently, growing evidence of the relationship between G-protein coupled receptor 44 (GPR44) and the inflammation-cancer system has garnered tremendous interest, while the exact role of GPR44 has not been fully elucidated. Currently, there is a strong and urgent need for the development of non-invasive in vivo GPR44 positron emission tomography (PET) radiotracers that can be used to aid the exploration of the relationship between inflammation and tumor biologic behavior. Accordingly, the choosing and radiolabeling of existing GPR44 antagonists containing a fluorine group could serve as a viable method to accelerate PET tracers development for in vivo imaging to this purpose. The present study aims to evaluate published (2000-present) indole-based and cyclopentenyl-indole-based analogues of the GPR44 antagonist to guide the development of fluorine-18 labeled PET tracers that can accurately detect inflammatory processes. The selected analogues contained a crucial fluorine nuclide and were characterized for various properties including binding affinity, selectivity, and pharmacokinetic and metabolic profile. Overall, 26 compounds with favorable to strong binding properties were identified. This review highlights the potential of GPR44 analogues for the development of PET tracers to study inflammation and cancer development and ultimately guide the development of targeted clinical therapies.

Flood risk assessment and mapping are considered essential tools for the improvement of flood management. This research aims to construct a more comprehensive flood assessment framework by emphasizing factors related to human resilience and integrating them with meteorological and geographical factors. Moreover, two ensemble learning models, namely voting and stacking, which utilize heterogeneous learners, were employed in this study, and their prediction performance was compared with that of traditional machine learning models, including support vector machine, random forest, multilayer perceptron, and gradient boosting decision tree. The six models were trained and tested using a sample database constructed from historical flood events in Hefei, China. The results demonstrated the following findings: (1) the RF model exhibited the highest accuracy, while the SVR model underestimated the extent of extremely high-risk areas. The stacking model underestimated the extent of very-high-risk areas. It should be noted that the prediction results of ensemble learning methods may not be superior to those of the base models upon which they are built. (2) The predicted high-risk and very-high-risk areas within the study area are predominantly clustered in low-lying regions along the rivers, aligning with the distribution of hazardous areas observed in historical inundation events. (3) It is worth noting that the factor of distance to pumping stations has the second most significant driving influence after the DEM (Digital Elevation Model). This underscores the importance of considering human resilience factors. This study expands the empirical evidence for the ability of machine learning methods to be employed in flood risk assessment and deepens our understanding of the potential mechanisms of human resilience in influencing urban flood risk.

With the change in global climate and environment, the prevalence of extreme rainstorms and flood disasters has increased, causing serious economic and property losses. Therefore, accurate and rapid prediction of waterlogging has become an urgent problem to be solved. In this study, Jianye District in Nanjing City of China is taken as the study area. The time series data recorded by rainfall stations and ponding monitoring stations from January 2015 to August 2018 are used to build a ponding prediction model based on the long short-term memory (LSTM) neural network. MSE (mean square error), MAE (mean absolute error) and MSLE (mean squared logarithmic error) were used as loss functions to conduct and train the LSTM model, then three ponding prediction models were built, namely LSTM (mse), LSTM (mae) and LSTM (msle), and a multi-step model was used to predict the depth of ponding in the next 1 h. Using the measured ponding data to evaluate the model prediction results, we selected rmse (root mean squared error), mae, mape (mean absolute percentage error) and NSE (Nash–Sutcliffe efficiency coefficient) as the evaluation indicators. The results showed that LSTM (msle) was the best model among the three models, with evaluation indicators as follows: rmse 5.34, mae 3.45, mape 53.93% and NSE 0.35. At the same time, we found that LSTM (mae) has a better prediction effect than the LSTM (mse) and LSTM (msle) models when the ponding depth exceeds 30 mm.

The East Asian VLBI Network (EAVN) is an international VLBI facility in East Asia and is operated under mutual collaboration between East Asian countries, as well as part of Southeast Asian and European countries. EAVN currently consists of 16 radio telescopes and three correlators located in China, Japan, and Korea, and is operated mainly at three frequency bands, 6.7, 22, and 43 GHz with the longest baseline length of 5078 km, resulting in the highest angular resolution of 0.28 milliarcseconds at 43 GHz. One of distinct capabilities of EAVN is multi-frequency simultaneous data reception at nine telescopes, which enable us to employ the frequency phase transfer technique to obtain better sensitivity at higher observing frequencies. EAVN started its open-use program in the second half of 2018, providing a total observing time of more than 1100 h in a year. EAVN fills geographical gap in global VLBI array, resulting in enabling us to conduct contiguous high-resolution VLBI observations. EAVN has produced various scientific accomplishments especially in observations toward active galactic nuclei, evolved stars, and star-forming regions. These activities motivate us to initiate launch of the ’Global VLBI Alliance’ to provide an opportunity of VLBI observation with the longest baselines on the earth.

Compared with the point prediction, the interval prediction of the load could more effectively guarantee the safe operation of the power system. In view of the problem that the correlation between adjacent load data is not fully utilized so that the prediction accuracy is reduced, this paper proposes the conditional copula function interval prediction method, which could make full use of the correlation relationship between adjacent load data so as to obtain the interval prediction result. At the same time, there are the different prediction results of the method under different parameters, and the evaluation results of the two accuracy evaluation indicators containing PICP (prediction interval coverage probability) and the PIAW (prediction interval average width) are inconsistent, the above result that the optimal parameters and prediction results cannot be obtained, therefore, the NSGA-II (Non-dominated Sorting Genetic Algorithm-II) multi-objective optimization algorithm is proposed to seek out the optimal solution set, and by evaluating the solution set, obtain the optimal prediction model parameters and the corresponding prediction results. Finally, the proposed method is applied to the three regions of Shaanxi Province, China to conduct ultra-short-term load prediction, and compare it with the commonly used load interval prediction method such as Gaussian process regression (GPR) algorithm, artificial neural network (ANN), extreme learning machine (ELM) and others, and the results show that the proposed method always has better prediction accuracy when applying it to different regions.

Microbial foods include microbial biomass, naturally fermented foods, and heterologously synthesized food ingredients derived from microbial fermentation. Terpenoids, using isoprene as the basic structure, possess various skeletons and functional groups. They exhibit diverse physicochemical properties and physiological activities, such as unique flavor, anti-bacterial, anti-oxidant, anti-cancer, and hypolipemic, making them extensively used in the food industry, such as flavor, fragrance, preservatives, dietary supplements, and medicinal health food. Compared to traditional strategies like direct extraction from natural species and chemical synthesis, microbial cell factories for edible terpenoids have higher titers and yields. They can utilize low-cost raw materials and are easily scaling-up, representing a novel green and sustainable production mode. In this review, we briefly introduce the synthetic pathway of terpenoids and the applications of microbial cell factories producing edible terpenoids. Secondly, we highlight several typical and non-typical microbial chassis in edible terpenoid-producing cell factories. In addition, we reviewed the recent advances of representative terpenoid microbial cell factories with a gram-scale titer in food flavor, food preservation, nutritional enhancers, and medicinal health foods. Finally, we predict the future directions of microbial cell factories for edible terpenoids and their commercialization process.

S-metolachlor is a selective pre-emergence herbicide used in dryland. However, it is challenging to employ in paddy fields due to its phytotoxic effects on rice. As a common phytohormone, Gibberellin-3 (GA3) is inferred to have the ability to alleviate herbicide phytotoxicity. This study first quantitatively verified the phytotoxicity of s-metolachlor to rice and then demonstrated the mitigative effect of GA3 on these adverse reactions. Furthermore, a transcriptome of rice seedlings subjected to different treatments was constructed to assemble the reference genes, followed by comparative metabolomics and proteomics analyses. Metabolomics revealed an enrichment of flavonoid metabolites in the group of adding GA3, and these flavonoids can eliminate ROS in plants. Proteomics analysis indicated that differential proteins were enriched in the phenylpropanoid biosynthesis pathway responsible for the synthesis of flavonoids and that the functions of most differential proteins are associated with peroxidase. The proteome, combined with the transcriptome, revealed that the expressions of proteins and genes was related to the POD activity in the group of adding GA3. It was speculated that the elimination of ROS is key to alleviating the stress of s-metolachlor on rice growth. It was inferred that the mechanism of GA3 in alleviating the phytotoxicity of the substance s-metolachlor is by increasing the activity of the POD and influencing the growth of rice seedlings through the restoration of flavonoid synthesis. In this study, we screened GA3 as a safener to alleviate the phytotoxicity of s-metolachlor on rice. On this basis, the mechanism of alleviating phytotoxicity was studied. The application range of s-metolachlor might be expanded, providing a new supplementary method for weed control and herbicide resistance management.

As a layered material with single/multi-atom thickness, two-dimensional transition metal sulfide WS2 has attracted extensive attention in the field of science for its excellent physical, chemical, optical, and electrical properties. The photoelectric properties of WS2 are even more promising than graphene. However, there are many existing preparation methods for WS2, but few reports on its direct growth on tungsten films. Therefore, this paper studies its preparation method and proposes an innovative two-dimensional material preparation method to grow large-sized WS2 with higher quality on metal film. In this experiment, it was found that the reaction temperature could regulate the growth direction of WS2. When the temperature was below 950 °C, the film showed horizontal growth, while when the temperature was above 1000 °C, the film showed vertical growth. At the same time, through Raman and band gap measurements, it is found that the different thicknesses of precursor film will lead to a difference in the number of layers of WS2. The number of layers of WS2 can be controlled by adjusting the thickness of the precursor.

In order to solve the problems of traditional mental stress detection in college students that are time-consuming, random, and subjective, this paper proposes an intelligent perception-driven mental stress assessment method for college students. First, we analyze the factors in SRQ and SCL-90, which can be measured by intelligent sensing methods, including sleep, exercise, social interaction, and environment, and then perform feature extraction. Secondly, we use machine learning methods to build a mental stress assessment model. The Shapley additive explanations (SHAP) model is used to explain the training results. Experimental results show that the model proposed in this article can effectively assess the mental stress state of college students. This means that the collection of intelligent perception data based on the mental stress scale can effectively evaluate the mental stress state of college students and provide a new research idea for further developing a non-intrusive and real-time mental stress assessment for college students.

In offshore engineering, the sand beneath an embankment may be subjected to traffic loads, resulting in a series of engineering issues. The behaviour of the sand beneath the embankment may change under a long-term traffic load. A series of drained cyclic and post-cyclic monotonic triaxial tests were performed on Fujian sand with different relative densities. The drained strength and stress–dilatancy behaviours were studied. The results indicated that the normalised peak strength ratio after cyclic loading was greater than that without cyclic loading, depending on the cyclic stress amplitude, while the critical state strength seemed to be only slightly affected by the cyclic loading history. The dilative response of sand could also be influenced by cyclic loading-induced fabric. Under constant relative density conditions, the higher the cyclic stress amplitude applied to the sand sample, the larger the volume strain produced in the critical state. Furthermore, cyclic-induced fabric could be destroyed after 6% axial strain.