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Exosomes are nano-sized small extracellular vesicles secreted by cells, carrying nucleic acids, proteins, lipids and other bioactive substances to play a role in the body's physiological and pathological processes. Compared to synthetic carriers such as liposomes and nanoparticles, the endogeneity and heterogeneity of exosomes give them extensive and unique advantages in the field of disease diagnosis and treatment. However, the storage stability, low yield, low purity, and weak targeting of exosomes limit its clinical application. For this reason, further exploration is needed to optimize the above problems and facilitate future functional studies of exosomes. In this paper, the origin, classification, preparation and characterization, storage stability and applications of exosome delivery system are summarized and discussed by searching a large number of literatures.

This study investigates the impact of APOE alleles and latent Herpes Simplex Type 1 virus (HSV-1) activation on Alzheimer’s disease (AD) progression using the 5xFAD mouse model. APOE ε4 is recognized as a substantial genetic risk factor for sporadic AD, while HSV-1 has been linked to AD pathogenesis through inflammation and plaque formation. The experimental approach involves the introduction of human neurons carrying latent HSV-1 into 5xFAD mice harboring various APOE alleles (APOE2, APOE3, APOE4), along with stress induction and pharmacological interventions. The study aims to elucidate the combined impact of these variables on AD progression and the formation of Aβ plaques. Our anticipated results suggest that APOE ε4 may accelerate AD development, especially in conjunction with HSV-1 activation, while APOE ε2 may exert a mitigating influence. These findings have the potential to advance our understanding of the intricate mechanisms underpinning AD and provide insights into potential therapeutic approaches. Further exploration of these interactions could offer critical insights into the pursuit of effective AD treatments.

Chemical derivatization is a powerful strategy to enhance sensitivity and selectivity of liquid chromatography-mass spectrometry for non-targeted analysis of chemicals in complex mixtures. However, it remains impossible to obtain large sets of reference spectra for chemically derived molecules (CDMs), representing a major barrier in real-world applications. Herein, we describe a deep learning approach that enables accurate prediction of electrospray ionization tandem mass spectra for CDMs (DeepCDM). DeepCDM is established by transfer learning from a generic spectrum predicting model using a small set of experimentally acquired tandem mass spectra of CDMs, which converts a generic model with low predictability for CDMs into a specialized model with high predictability. We demonstrate DeepCDM by predicting electrospray ionization tandem mass spectra of dansylated molecules. The success in establishing Dns-MS further enables the development of DnsBank, a dansylation-specialized in silico spectral library. DnsBank achieves significant increases of accurate annotation rates of dansylated molecules, facilitating discovery of new hazardous pollutants from an environmental study of leather industrial wastewater. DeepCDM is also highly versatile for other classes of CDMs. Therefore, we envision that DeepCDM will pave a way for high-throughput identification of CDMs in non-targeted analysis to dig unknowns with potential health impacts from emerging anthropogenic chemicals. Chemical derivatization is widely used, but the lack of reference spectra of chemically derived molecules (CDMs) hinders their identification. Here, the authors describe a deep learning approach enabling accurate prediction of ESIMS/MS spectra for CDMs.

CRISPR diagnostics are powerful tools for detecting nucleic acids but are generally not deployable for the detection of clinically important proteins. Here, we report an u ltrasensitive CRISPR-based antibody detection (UCAD) assay that translates the detection of anti-SARS-CoV-2 antibodies into CRISPR-based nucleic acid detection in a homogeneous solution and is 10,000 times more sensitive than the classic immunoassays. Clinical validation using serum samples collected from the general population (n = 197), demonstrates that UCAD has 100% sensitivity and 98.5% specificity. With ultrahigh sensitivity, UCAD enables the quantitative analysis of serum anti-SARS-CoV-2 levels in vaccinated kidney transplant recipients who are shown to produce “undetectable” anti-SARS-CoV-2 using standard immunoassay. Because of the high sensitivity and simplicity, we anticipate that, upon further clinical validation against large cohorts of clinical samples, UCAD will find wide applications for clinical uses in both centralized laboratories and point-of-care settings. CRISPR diagnostics are routinely used for the detecting nucleic acids, but rarely for clinically important proteins. Here, by translating a CRISPR-based DNA test into an ultrasensitive assay for antibodies, the authors achieve antibody detection from serum samples at attomolar concentrations.

Aiming to improve the navigation accuracy during global navigation satellite system (GNSS) outages, an algorithm based on long short-term memory (LSTM) is proposed for aiding inertial navigation system (INS). The LSTM algorithm is investigated to generate the pseudo GNSS position increment substituting the GNSS signal. Almost all existing INS aiding algorithms, like the multilayer perceptron neural network (MLP), are based on modeling INS errors and INS outputs ignoring the dependence of the past vehicle dynamic information resulting in poor navigation accuracy. Whereas LSTM is a kind of dynamic neural network constructing a relationship among the present and past information. Therefore, the LSTM algorithm is adopted to attain a more stable and reliable navigation solution during a period of GNSS outages. A set of actual vehicle data was used to verify the navigation accuracy of the proposed algorithm. During 180 s GNSS outages, the test results represent that the LSTM algorithm can enhance the navigation accuracy 95% compared with pure INS algorithm, and 50% of the MLP algorithm.

Multi-source heterogeneous information fusion based on the Global Navigation Satellite System (GNSS)/Inertial Navigation System (INS)/odometer is an important technical means to solve the problem of navigation and positioning in complex environments. The measurement noise of the GNSS/INS/odometer integrated navigation system is complex and non-stationary; it approximates a Gaussian distribution in an open-sky environment, and it has heavy-tailed properties in the GNSS challenging environment. This work models the measurement noise and one-step prediction as the Gaussian and Student’s t mixture distribution to adjust to different scenarios. The mixture distribution is formulated as the hierarchical Gaussian form by introducing Bernoulli random variables, and the corresponding hierarchical Gaussian state-space model is constructed. Then, the mixing probability of Gaussian and Student’s t distributions could adjust adaptively according to the real-time kinematic solution state. Based on the novel distribution, a robust variational Bayesian Kalman filter is proposed. Finally, two vehicle test cases conducted in GNSS-friendly and challenging environments demonstrate that the proposed robust Kalman filter with the Gaussian–Student’s t mixture distribution can better model heavy-tailed non-Gaussian noise. In challenging environments, the proposed algorithm has position root mean square (RMS) errors of 0.80 m, 0.62 m, and 0.65 m in the north, east, and down directions, respectively. With the assistance of inertial sensors, the positioning gap caused by GNSS outages has been compensated. During seven periods of 60 s simulated GNSS data outages, the RMS position errors in the north, east, and down directions were 0.75 m, 0.30 m, and 0.20 m, respectively.

In this study, 356 MYB transcription factors were identified from the genome of honeysuckle, and combined with transcriptome data analysis, 104 of them were found to respond to drought stress. Through phylogenetic analysis, qRT-PCR analysis and correlation analysis of chemical components, three target genes LjMYB3 , LjMYB8 and LjMYB63 were screened from these 104 MYB transcription factors and overexpressed in Arabidopsis thaliana . The results showed that the drought resistance, total flavonoid content and flavonoid biosynthesis-related gene expression levels of transgenic Arabidopsis were higher than those of wild-type Arabidopsis .Under drought stress, MYB transcription factors can activate the expression of drought resistance-related genes, regulate key genes in the flavonoid synthesis pathway, and promote the accumulation of flavonoids. This study promotes the research on transcriptional regulation of honeysuckle MYB transcription factors and flavonoid biosynthesis and lays a foundation for unraveling the drought resistance mechanism in honeysuckle.

The integrated navigation system consisting of an inertial navigation system (INS) and Global Navigation Satellite System (GNSS) provides continuous high-accuracy positioning whereas the navigation accuracy during a GNSS outage inevitably degrades owing to INS error divergence. To reduce such degradation, a gated recurrent unit (GRU) and adaptive Kalman filter (AKF)-based hybrid algorithm is proposed. The GRU network, which has advantages of high accuracy and efficiency, is constructed to predict the position variations during GNSS outage. Furthermore, this paper takes the GRU-predicted error accumulation into consideration, and introduces AKF as a supplementary methodology to improve the navigation performance. The proposed hybrid algorithm is trained and tested by practical road datasets and compared with four algorithms, including the standard KF, Multi-Layer Perceptron (MLP)-aided KF, Long Short Time Memory (LSTM) aided KF, and GRU-aided KF. Periods of 180 and 120 s GNSS outage are employed to test the performance of the proposed algorithm in different time scales. The comparison result between the standard KF and neural network-aided KF indicates that the neural network is an effective methodology for bridging GNSS outages. The performance comparison between three kinds of neural networks demonstrate that both recurrent neural networks surpass the MLP in prediction position variation, and the GRU transcends the LSTM in prediction accuracy and training efficiency. Furthermore, it is concluded that the adaptive estimation theory is an effective complement to neural network-aided navigation, as the GRU-aided AKF reduced the horizontal error of GRU-aided KF by 31.71% and 16.12% after 180 and 120 s of GNSS outage, respectively.

MicroRNAs (miRNAs) are important regulators of pathobiological processes in various cancer. In the present study, we demonstrated that miR-93 expression was significantly up-regulated in gastric cancer tissues compared with that in matched normal mucosal tissues. High expression of miR-93 was significantly associated with lymph node metastasis and tumor-node-metastasis (TNM) stage. Functionally, ectopic expression of miR-93 promoted cell proliferation, migration, invasion, EMT phenotypes, and repressed apoptosis and G1 cell cycle arrest in vitro, and promoted tumor formation in vivo. We further identified that tissue inhibitor of metalloproteinase 2 (TIMP2) was a direct target of miR-93 by using luciferase reporter assay, qRT-PCR, and immunoblotting assay. Furthermore, knockdown of TIMP2 with specific siRNA showed similar oncogenic effects in gastric cancer cells with that transfected with miR-93 mimics. Our findings indicated that miR-93 serves as a tumor promoter in human gastric carcinogenesis by targeting TIMP2, suggesting that miR-93 might be a promising biomarker and therapeutic target for treatment of gastric cancer.

Knowledge of the spatial and temporal variabilities of soil respiration is important in estimating the soil carbon budget and in understanding how soils may respond to global changes. In areas with complex terrain, the topography can modify the hydrological conditions and other biophysical variables, which complicates the spatial and temporal heterogeneity of soil respiration. Herein, we investigated soil respiration along topographic transects with ridge, middle slope, lower slope and valley positions in a humid subtropical mountain forest in China to assess the driving factors of the variations in soil respiration. Our results showed that there were substantial temporal and spatial variations in soil respiration. The temporal variation of soil respiration could be well explained by the dynamics of soil temperature and moisture. Soil respiration rates also showed clear topographic pattern and decreased significantly from the ridge to valley soils, with the mean rates equaled 3.43 ± 0.13, 2.64 ± 0.30, 2.13 ± 0.26 and 1.88 ± 0.24 μmol m⁻² s⁻¹ at the ridge, middle slope, lower slope, and valley, respectively. Correlation analyses revealed that the spatial variation of soil respiration could be explained by multiple variables (e.g., soil temperature, basal area of the trees, thickness of the forest floor, root biomass and stock of soil dissolved carbon, soil C/N and soil bulk density). Results from partial least squares path modeling suggested that the topography modified the fine root distribution and the lateral losses of light and dissolved organic materials that created areas of high carbon sources for soil respiration at the ridge. The topographically regulated processes further resulted in a high soil C/N at the ridge that favored SOC decomposition. The higher respiration rate for the ridge soil and its higher sensitivity to soil temperature and moisture changes suggested that the ridge position was a potential hot spot for future environmental changes. Future studies and management practices regarding the soil carbon efflux in forest ecosystems with topographical variations should take into account the topographic effects.