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The highly pathogenic porcine reproductive and respiratory syndrome virus (HP-PRRSV) poses a significant threat to the global swine industry. Vaccination is a preventive measure against viral infections. However, the use of vaccines in livestock healthcare programs faces the challenge of safety and delayed immune responses. Earlier studies have shown the potential of modified Bazhen powder as an immunomodulator with significant biological properties, but its effect on vaccines against HP-PRRSV is yet to be studied. This study elucidated how modified Bazhen powder could improve the safety and efficacy of the conventional PRRSV vaccine by evaluating T-cell responses, antibody levels, clinical symptoms, levels of viremia, organ health, and cytokine production. The results revealed that the oral application of modified Bazhen powder in combination with PRRS vaccination improved both cellular and humoral immunity, accelerated viremia clearance, improved lung injury scores, and reduced viral load in the tonsils. The modified Bazhen powder also effectively reduced inflammatory responses following a PRRSV challenge. These findings further highlight the pharmacological properties of modified Bazhen powder as a potential oral immunomodulatory agent that could enhance vaccine efficacy and ensure broad-spectrum protection against HP-PRRSV in pigs.

BackgroundThe soil fungal community is one of the most important drivers of the soil nutrient cycling that sustains plant growth. However, little research has been done on the effects of different land uses on soil fungal communities in northeast China.MethodsIn this study, we conducted a field experiment to investigate the effects of continuous cropping of grass, maize, and alfalfa on their respective fungal communities and co-occurrence networks.ResultsWe showed that the physicochemical properties of the soil, such as nitrate (NO3-N), available phosphorus, and soil pH, were the most important driving factors affecting the structure of the soil fungal community in different cropping systems. In addition, compared to the cultivation of grass and maize, the continuous cropping of alfalfa increased the abundance of several beneficial as well as pathogenic species, such as Mortierella and Gaiellales. In addition, the networks differed among plant species and according to the number of years of continuous cultivation.ConclusionThis suggests that the continuous cropping of alfalfa results in greater cooperation among fungi, which may be beneficial to the soil as well as to the development of the alfalfa.

Nitrous oxide (N2O) is one of the most important greenhouse gases contributing to global climate warming. Recently, studies have shown that arbuscular mycorrhizal fungi (AMF) could reduce N2O emissions in terrestrial ecosystems; however, the microbial mechanisms of how AMF reduces N2O emissions under climate change are still not well understood. We tested the influence of AMF on N2O emissions by setting up a gradient of precipitation intensity (+50%, +30%, ambient (0%), −30%, −50%, and −70%) and manipulating the presence or exclusion of AMF hyphae in a semiarid grassland located in northeast China. Our results showed that N2O fluxes dramatically declined with the decrease in precipitation gradient during the peak growing season (June–August) in both 2019 and 2020. There was a significantly positive correlation between soil water content and N2O fluxes. Interestingly, N2O fluxes significantly decreased when AMF were present compared to when they were absent under all precipitation conditions. The contribution of AMF to mitigate N2O emission increased gradually with decreasing precipitation magnitudes, but no contribution in the severe drought (−70%). AMF significantly reduced the soil’s available nitrogen concentration and altered the composition of the soil bacteria community including those associated with N2O production. Hyphal length density was negatively correlated with the copy numbers of key genes for N2O production (nirK and nirS) and positively correlated with the copy numbers of key genes for N2O consumption (nosZ). Our results highlight that AMF would reduce the soil N2O emission under precipitation variability in a temperate grassland except for extreme drought.

Paphiopedilum (Orchidaceae) is one of the world’s most popular orchids that is found in tropical and subtropical forests and has an enormous ornamental value. SEPALLATA-like (SEP-like) MADS-box genes are responsible for floral organ specification. In this study, three SEP-like MADS-box genes, PhSEP1, PhSEP2, and PhSEP3, were identified in Paphiopedilum henryanum. These genes were 732–916 bp, with conserved SEPI and SEPII motifs. Phylogenetic analysis revealed that PhSEP genes were evolutionarily closer to the core eudicot SEP3 lineage, whereas none of them belonged to core eudicot SEP1/2/4 clades. PhSEP genes displayed non-ubiquitous expression, which was detectable across all floral organs at all developmental stages of the flower buds. Furthermore, subcellular localization experiments revealed the localization of PhSEP proteins in the nucleus. Yeast two-hybrid assays revealed no self-activation of PhSEPs. The protein–protein interactions revealed that PhSEPs possibly interact with B-class DEFICIENS-like and E-class MADS-box proteins. Our study suggests that the three SEP-like genes may play key roles in flower development in P. henryanum, which will improve our understanding of the roles of the SEP-like MADS-box gene family and provide crucial insights into the mechanisms underlying floral development in orchids.

Maize and alfalfa (Medicago sativa L.) have been used extensively in the animal husbandry to compensate for the lack of livestock and fodder yields in the chilly northeast of China. Little is known, however, about the impact on soil characteristics of consecutive plantings in various crops and alfalfa. In this research, the soil characteristics, bacterial community diversity, and structure of the meadow, maize, and alfalfa continuous cropping fields (i.e., 6, 10, 14, 20, and 30 years) were measured. The results showed that maize cropping and continuous cropping of alfalfa increased the soil bacterial alpha diversity compared with meadow cropping, and alpha diversity of alfalfa increased with the continuous planting years. Soil pH, total phosphorus (TP), available P, total potassium (TK), and nitrate nitrogen (NO3–) content were soil variables significantly impacting the structure of soil bacterial communities in different plant types and different alfalfa continuous cropping systems. In addition, the relative abundance of some beneficial microbial species, such as Arthrobacter and Gaiellales, in the cropping maize and continuous cropping of alfalfa was much higher than that in the meadow field. Moreover, the networks differ among different plant types, and also differ among different continuous cropping years of alfalfa, and topologies of the networks suggested that continuous planting of alfalfa promotes cooperation between bacteria, which facilitates the long growth of alfalfa and is beneficial to the soil.

BackgroundPredicting hepatocellular carcinoma (HCC) prognosis is important for treatment selection, and it is increasingly interesting to predict prognosis through gene expression data. Currently, the prognosis remains of low accuracy due to the high dimension but small sample size of liver cancer omics data. In previous studies, a transfer learning strategy has been developed by pre-training models on similar cancer types and then fine-tuning the pre-trained models on the target dataset. However, transfer learning has limited performance since other cancer types are similar at different levels, and it is not trivial to balance the relations with different cancer types.MethodsHere, we propose an adaptive transfer-learning-based deep Cox neural network (ATRCN), where cancers are represented by 12 phenotype and 10 genotype features, and suitable cancers were adaptively selected for model pre-training. In this way, the pre-trained model can learn valuable prior knowledge from other cancer types while reducing the biases.ResultsATRCN chose pancreatic and stomach adenocarcinomas as the pre-training cancers, and the experiments indicated that our method improved the C-index of 3.8% by comparing with traditional transfer learning methods. The independent tests on three additional HCC datasets proved the robustness of our model. Based on the divided risk subgroups, we identified 10 HCC prognostic markers, including one new prognostic marker, TTC36. Further wet experiments indicated that TTC36 is associated with the progression of liver cancer cells.ConclusionThese results proved that our proposed deep-learning-based method for HCC prognosis prediction is robust, accurate, and biologically meaningful.

Motivation: Long non-coding RNAs (lncRNAs) play important roles in cancer development. Prediction of lncRNA–cancer association is necessary for efficiently discovering biomarkers and designing treatment for cancers. Currently, several methods have been developed to predict lncRNA–cancer associations. However, most of them do not consider the relationships between lncRNA with other molecules and with cancer prognosis, which has limited the accuracy of the prediction.Method: Here, we constructed relationship matrices between 1,679 lncRNAs, 2,759 miRNAs, and 16,410 genes and cancer prognosis on three types of cancers (breast, lung, and colorectal cancers) to predict lncRNA–cancer associations. The matrices were iteratively reconstructed by matrix factorization to optimize low-rank size. This method is called detecting lncRNA cancer association (DRACA).Results: Application of this method in the prediction of lncRNAs–breast cancer, lncRNA–lung cancer, and lncRNA–colorectal cancer associations achieved an area under curve (AUC) of 0.810, 0.796, and 0.795, respectively, by 10-fold cross-validations. The performances of DRACA in predicting associations between lncRNAs with three kinds of cancers were at least 6.6, 7.2, and 6.9% better than other methods, respectively. To our knowledge, this is the first method employing cancer prognosis in the prediction of lncRNA–cancer associations. When removing the relationships between cancer prognosis and genes, the AUCs were decreased 7.2, 0.6, and 5% for breast, lung, and colorectal cancers, respectively. Moreover, the predicted lncRNAs were found with greater numbers of somatic mutations than the lncRNAs not predicted as cancer-associated for three types of cancers. DRACA predicted many novel lncRNAs, whose expressions were found to be related to survival rates of patients. The method is available at https://github.com/Yanh35/DRACA.

As one of the most important limiting factors of grassland productivity, drought is predicted to increase in intensity and frequency. Greenhouse studies suggest that arbuscular mycorrhizal fungi (AMF) can improve plant drought resistance. However, whether AMF can improve plant drought resistance in field conditions and whether the effects of AMF on drought resistance differ among plants with different photosynthetic pathways remain unclear. To evaluate the effect of indigenous AMF on plant drought resistance, an in situ rainfall exclusion experiment was conducted in a temperate meadow in northeast China. The results showed that AMF significantly reduced the negative effects of drought on plant growth. On average, AMF enhanced plant biomass, photosynthetic rate (A), stomatal conductance (gs), intrinsic water use efficiency (iWUE), and superoxide dismutase (SOD) activity of the C3 species Leymus chinensis by 58, 63, 38, 15, and 45%, respectively, and reduced levels of malondialdehyde (MDA) by 32% under light and moderate drought (rainfall exclusion of 30 and 50%, respectively). However, under extreme drought (rainfall exclusion of 70%), AMF elevated only aboveground biomass and catalase (CAT) activities. Averagely, AMF increased the aboveground biomass, A, and CAT activity of Hemarthria altissima (C4) by 37, 28, and 30%, respectively, under light and moderate droughts. The contribution of AMF to plant drought resistance was higher for the C3 species than that for the C4 species under both light and moderate drought conditions. The results highlight potential photosynthetic type differences in the magnitude of AMF-associated enhancement in plant drought resistance. Therefore, AMF may determine plant community structure under future climate change scenarios by affecting the drought resistance of different plant functional groups.

Whether photosynthetic pathway differences exist in the amplitude of nighttime variations in the carbon isotope composition of leaf dark-respired CO2 (δ13Cl) and respiratory apparent isotope fractionation relative to biomass (ΔR,biomass) in response to drought stress is unclear. These differences, if present, would be important for the partitioning of C3-C4 mixed ecosystem C fluxes. We measured δ13Cl, the δ13C of biomass and of potential respiratory substrates and leaf gas exchange in one C3 (Leymus chinensis) and two C4 (Chloris virgata and Hemarthria altissima) grasses during a manipulated drought period. For all studied grasses, δ13Cl decreased from 21:00 to 03:00 h. The magnitude of the nighttime shift in δ13Cl decreased with increasing drought stress. The δ13Cl values were correlated with the δ13C of respiratory substrates, whereas the magnitude of the nighttime shift in δ13Cl strongly depended on the daytime carbon assimilation rate and the range of nighttime variations in the respiratory substrate content. The ΔR,biomass in the C3 and C4 grasses varied in opposite directions with the intensification of the drought stress. The contribution of C4 plant-associated carbon flux is likely to be overestimated if carbon isotope signatures are used for the partitioning of ecosystem carbon exchange and the δ13C of biomass is used as a substitute for leaf dark-respired CO2. The detected drought sensitivities in δ13Cl and differences in respiratory apparent isotope fractionation between C3 and C4 grasses have marked implications for isotope partitioning studies at the ecosystem level.

Nitrous oxide (N2O) is one of the most important greenhouse gases contributing to global climate warming. Recently, studies have shown that arbuscular mycorrhizal fungi (AMF) could reduce N2O emissions in terrestrial ecosystems; however, the microbial mechanisms of how AMF reduces N2O emissions under climate change are still not well understood. We tested the influence of AMF on N2O emissions by setting up a gradient of precipitation intensity (+50%, +30%, ambient (0%), -30%, -50%, and -70%) and manipulating the presence or exclusion of AMF hyphae in a semiarid grassland located in northeast China. Our results showed that N2O fluxes dramatically declined with the decrease in precipitation gradient during the peak growing season (June-August) in both 2019 and 2020. There was a significantly positive correlation between soil water content and N2O fluxes. Interestingly, N2O fluxes significantly decreased when AMF were present compared to when they were absent under all precipitation conditions. The contribution of AMF to mitigate N2O emission increased gradually with decreasing precipitation magnitudes, but no contribution in the severe drought (-70%). AMF significantly reduced the soil's available nitrogen concentration and altered the composition of the soil bacteria community including those associated with N2O production. Hyphal length density was negatively correlated with the copy numbers of key genes for N2O production (nirK and nirS) and positively correlated with the copy numbers of key genes for N2O consumption (nosZ). Our results highlight that AMF would reduce the soil N2O emission under precipitation variability in a temperate grassland except for extreme drought. [ABSTRACT FROM AUTHOR]

Whether photosynthetic pathway differences exist in the amplitude of nighttime variations in the carbon isotope composition of leaf dark-respired CO2 (δ13Cl) and respiratory apparent isotope fractionation relative to biomass (ΔR,biomass) in response to drought stress is unclear. These differences, if present, would be important for the partitioning of C3-C4 mixed ecosystem C fluxes. We measured δ13Cl, the δ13C of biomass and of potential respiratory substrates and leaf gas exchange in one C3 (Leymus chinensis) and two C4 (Chloris virgata and Hemarthria altissima) grasses during a manipulated drought period. For all studied grasses, δ13Cl decreased from 21:00 to 03:00 h. The magnitude of the nighttime shift in δ13Cl decreased with increasing drought stress. The δ13Cl values were correlated with the δ13C of respiratory substrates, whereas the magnitude of the nighttime shift in δ13Cl strongly depended on the daytime carbon assimilation rate and the range of nighttime variations in the respiratory substrate content. The ΔR,biomass in the C3 and C4 grasses varied in opposite directions with the intensification of the drought stress. The contribution of C4 plant-associated carbon flux is likely to be overestimated if carbon isotope signatures are used for the partitioning of ecosystem carbon exchange and the δ13C of biomass is used as a substitute for leaf dark-respired CO2. The detected drought sensitivities in δ13Cl and differences in respiratory apparent isotope fractionation between C3 and C4 grasses have marked implications for isotope partitioning studies at the ecosystem level. [ABSTRACT FROM AUTHOR]

PurposeThe study aimed to identify potential risk factors for family transmission and to provide precautionary guidelines for the general public during novel Coronavirus disease 2019 (COVID-19) waves.MethodsA retrospective cohort study with numerous COVID-19 patients recruited was conducted in Shanghai. Epidemiological data including transmission details, demographics, vaccination status, symptoms, comorbidities, antigen test, living environment, residential ventilation, disinfection and medical treatment of each participant were collected and risk factors for family transmission were determined. ResultsA total of 2,334 COVID-19 patients participated. Compared with non-cohabitation infected patients, cohabitated ones were younger (p = 0.019), more commonly unvaccinated (p = 0.048) or exposed to infections (p