Tertiary lymphoid structures and their therapeutic implications in cancer.

Tertiary lymphoid structures (TLSs) are ectopic lymphoid aggregates formed by the structured accumulation of immune cells such as B cells and T cells in non-lymphoid tissues induced by infection, inflammation, and tumors. They play a crucial role in the immune response, particularly in association with tumor development, where they primarily exert anti-tumor immune functions during tumorigenesis. Current research suggests that TLSs inhibit tumor growth by facilitating immune cell infiltration and are correlated with favorable prognosis in various solid tumors, serving as an indicator of immunotherapy effectiveness to some extent. Therefore, TLSs hold great promise as a valuable biomarker. Most importantly, immunotherapies aimed to prompting TLSs formation are anticipated to be potent adjuncts to current cancer treatment. This review focuses on the formation process of TLSs and their potential applications in cancer therapy.

Recombinant OC43 SARS-CoV-2 spike replacement virus: An improved BSL-2 proxy virus for SARS-CoV-2 neutralization assays.

We generated a replication-competent OC43 human seasonal coronavirus (CoV) expressing the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) spike in place of the native spike (rOC43-CoV2 S). This virus is highly attenuated relative to OC43 and SARS-CoV-2 in cultured cells and animals and is classified as a biosafety level 2 (BSL-2) agent by the NIH biosafety committee. Neutralization of rOC43-CoV2 S and SARS-CoV-2 by S-specific monoclonal antibodies and human sera is highly correlated, unlike recombinant vesicular stomatitis virus-CoV2 S. Single-dose immunization with rOC43-CoV2 S generates high levels of neutralizing antibodies against SARS-CoV-2 and fully protects human ACE2 transgenic mice from SARS-CoV-2 lethal challenge, despite nondetectable replication in respiratory and nonrespiratory organs. rOC43-CoV2 S induces S-specific serum and airway mucosal immunoglobulin A and IgG responses in rhesus macaques. rOC43-CoV2 S has enormous value as a BSL-2 agent to measure S-specific antibodies in the context of a bona fide CoV and is a candidate live attenuated SARS-CoV-2 mucosal vaccine that preferentially replicates in the upper airway.

Improving the accuracy of cotton seedling emergence rate estimation by fusing UAV-based multispectral vegetation indices.

Timely and accurate estimation of cotton seedling emergence rate is of great significance to cotton production. This study explored the feasibility of drone-based remote sensing in monitoring cotton seedling emergence. The visible and multispectral images of cotton seedlings with 2 - 4 leaves in 30 plots were synchronously obtained by drones. The acquired images included cotton seedlings, bare soil, mulching films, and PE drip tapes. After constructing 17 visible VIs and 14 multispectral VIs, three strategies were used to separate cotton seedlings from the images: (1) Otsu's thresholding was performed on each vegetation index (VI); (2) Key VIs were extracted based on results of (1), and the Otsu-intersection method and three machine learning methods were used to classify cotton seedlings, bare soil, mulching films, and PE drip tapes in the images; (3) Machine learning models were constructed using all VIs and validated. Finally, the models constructed based on two modeling strategies [Otsu-intersection (OI) and machine learning (Support Vector Machine (SVM), Random Forest (RF), and K-nearest neighbor (KNN)] showed a higher accuracy. Therefore, these models were selected to estimate cotton seedling emergence rate, and the estimates were compared with the manually measured emergence rate. The results showed that multispectral VIs, especially NDVI, RVI, SAVI, EVI2, OSAVI, and MCARI, had higher crop seedling extraction accuracy than visible VIs. After fusing all VIs or key VIs extracted based on Otsu's thresholding, the binary image purity was greatly improved. Among the fusion methods, the Key VIs-OI and All VIs-KNN methods yielded less noises and small errors, with a RMSE (root mean squared error) as low as 2.69% and a MAE (mean absolute error) as low as 2.15%. Therefore, fusing multiple VIs can increase crop image segmentation accuracy. This study provides a new method for rapidly monitoring crop seedling emergence rate in the field, which is of great significance for the development of modern agriculture.

Dynamic monitoring of radiation-induced white matter microstructure injury in nasopharyngeal carcinoma via high-angular resolution diffusion imaging.

PURPOSE: To investigate white matter microstructural abnormalities caused by radiotherapy in nasopharyngeal carcinoma (NPC) patients using MRI high-angular resolution diffusion imaging (HARDI). METHODS: We included 127 patients with pathologically confirmed NPC: 36 in the pre-radiotherapy group, 29 in the acute response period (post-RT-AP), 23 in the early delayed period (post-RT-ED) group, and 39 in the late-delayed period (post-RT-LD) group. HARDI data were acquired for each patient, and dispersion parameters were calculated to compare the differences in specific fibre bundles among the groups. The Montreal Neurocognitive Assessment (MoCA) was used to evaluate neurocognitive function, and the correlations between dispersion parameters and MoCA were analysed. RESULTS: In the right cingulum frontal parietal bundles, the fractional anisotropy value decreased to the lowest level post-RT-AP and then reversed and increased post-RT-ED and post-RT-LD. The mean, axial, and radial diffusivity were significantly increased in the post-RT-AP (p < 0.05) and decreased in the post-RT-ED and post-RT-LD groups to varying degrees. MoCA scores were decreased post-radiotherapy than those before radiotherapy (p = 0.005). MoCA and mean diffusivity exhibited a mild correlation in the left cingulum frontal parahippocampal bundle. CONCLUSIONS: White matter tract changes detected by HARDI are potential biomarkers for monitoring radiotherapy-related brain damage in NPC patients.

C1q enables influenza hemagglutinin stem binding antibodies to block viral attachment and broadens the antibody escape repertoire.

Antigenic drift, the gradual accumulation of amino acid substitutions in the influenza virus hemagglutinin (HA) receptor protein, enables viral immune evasion. Antibodies (Abs) specific for the drift-resistant HA stem region are a promising universal influenza vaccine target. Although anti-stem Abs are not believed to block viral attachment, here we show that complement component 1q (C1q), a 460-kilodalton protein with six Ab Fc-binding domains, confers attachment inhibition to anti-stem Abs and enhances their fusion and neuraminidase inhibition. As a result, virus neutralization activity in vitro is boosted up to 30-fold, and in vivo protection from influenza PR8 infection in mice is enhanced. These effects reflect increased steric hindrance and not increased Ab avidity. C1q greatly expands the anti-stem Ab viral escape repertoire to include residues throughout the HA, some of which cause antigenic alterations in the globular region or modulate HA receptor avidity. We also show that C1q enhances the neutralization activity of non-receptor binding domain anti-SARS-CoV-2 spike Abs, an effect dependent on spike density on the virion surface. These findings demonstrate that C1q can greatly expand Ab function and thereby contribute to viral evolution and immune escape.

Photo-production of excited triplet-state of dissolved organic matters in inland freshwater and coastal seawater.

The excited triplet-state of dissolved organic matter (3DOM*) is a major reactive intermediate in sunlit waters. Its quantum yield is important in understanding the fate of organic micropollutants. The degradation efficiency of its chemical probe, 2,4,6-trimeythlphenol (fTMP), is generally used as a proxy of the quantum yield. However, fTMP has been described and modelled only for freshwater systems. Therefore, this study quantified fTMP in inland freshwater and coastal seawater sampled in Japan by conducting steady-state photochemical experiments. Optical properties of water were then used to model fTMP. Results indicated that the inland freshwater DOM originated mainly from terrestrial sources, while the coastal seawater DOM were microbial-dominated. On average, inland freshwater exhibited lower fTMP (61.2 M-1) than coastal seawater (79.7 M-1) and the coastal seawater exhibited significant variations in the proportion of high-energy 3DOM* (> 250 kJ/mol). In addition, E2:E3 (ratio of absorbance at 254 to 365 nm) was positively correlated with fTMP of inland freshwater, coastal seawater, and the overall dataset. Catchment conditions such as forest coverage also influenced the production of 3DOM* and high-energy 3DOM* in inland freshwater. Furthermore, the developed models estimated fTMP based on the optical properties of both freshwater and seawater, providing valuable insights about 3DOM* photochemistry in the aquatic environment.

Omicron Spike confers enhanced infectivity and interferon resistance to SARS-CoV-2 in human nasal tissue.

Omicron emerged following COVID-19 vaccination campaigns, displaced previous SARS-CoV-2 variants of concern worldwide, and gave rise to lineages that continue to spread. Here, we show that Omicron exhibits increased infectivity in primary adult upper airway tissue relative to Delta. Using recombinant forms of SARS-CoV-2 and nasal epithelial cells cultured at the liquid-air interface, we show that mutations unique to Omicron Spike enable enhanced entry into nasal tissue. Unlike earlier variants of SARS-CoV-2, our findings suggest that Omicron enters nasal cells independently of serine transmembrane proteases and instead relies upon metalloproteinases to catalyze membrane fusion. Furthermore, we demonstrate that this entry pathway unlocked by Omicron Spike enables evasion from constitutive and interferon-induced antiviral factors that restrict SARS-CoV-2 entry following attachment. Therefore, the increased transmissibility exhibited by Omicron in humans may be attributed not only to its evasion of vaccine-elicited adaptive immunity, but also to its superior invasion of nasal epithelia and resistance to the cell-intrinsic barriers present therein.

Improving the estimation accuracy of rapeseed leaf photosynthetic characteristics under salinity stress using continuous wavelet transform and successive projections algorithm.

Soil salinization greatly restricts crop production in arid areas for salinity stress can inhibit crop photosynthesis and growth. Chlorophyll fluorescence and photosynthetic gas exchange (CFPGE) parameters are important indicators of crop photosynthesis and have been widely used to evaluate the impacts of salinity stress on crop photosynthesis and growth. Remote sensing technology can quickly and non-destructively obtain crop information under salinity stress, however, at present, the distribution of spectral features of CFPGE parameters in different regions is still unclear. In this study (2019-2020), under salinity stress conditions, the spectral data of rapeseed leaves were acquired and the CFPGE parameters were simultaneously determined. Then, continuous wavelet transformation (CWT) and standard normal variate (SNV) transformation were utilized to preprocess the raw spectral data. After that, a CFPGE parameter estimation model was constructed by using the partial least squares regression (PLSR) algorithm and the support vector machines (SVM) algorithm based on the spectral features in the red region (600-800 nm) and those in the red, blue-green (350-600 nm), and near-infrared (800-2500 nm) regions. The results showed that the spectral features of CFPGE parameters could be extracted by successive projections algorithm (SPA) based on the CWT preprocessing. The CFPGE parameter estimation model constructed based on the spectral features in the red region (675 nm, 680 nm, 688 nm, 749 nm, and 782 nm) had the highest Fv/Fm estimation accuracy on day 30, with R2c, R2p, and RPD of 0.723, 0.585, and 1.68, respectively. Based on this, the spectral features (578 nm, 976 nm, 1088 nm, 1476 nm, and 2250 nm) in the blue-green and near-infrared regions were added in the variables for modeling, which significantly improved the accuracy and stability of the model, with R2c, R2p, and RPD of 0.886, 0.815, and 2.58, respectively. Therefore, the fusion of the spectral features in the red, blue-green, and near-infrared regions could improve the estimation accuracy of rapeseed leaf CFPGE parameters. This study will provide technical reference for rapid estimation of photosynthetic performance of crops under salinity stress in arid and semi-arid areas.