High-Level Expression of Notch1 Increased the Risk of Metastasis in T1 Stage Clear Cell Renal Cell Carcinoma

Background: Although metastasis of clear cell renal cell carcinoma (ccRCC) is basically observed in late stage tumors, T1 stage metastasis of ccRCC can also be found with no definite molecular cause resulting inappropriate selection of surgery method and poor prognosis. Notch signaling is a conserved, widely expressed signal pathway that mediates various cellular processes in normal development and tumorigenesis. This study aims to explore the potential role and mechanism of Notch signaling in the metastasis of T1 stage ccRCC. Methodology/Principal Findings: The expression of Notch1 and Jagged1 were analyzed in tumor tissues and matched normal adjacent tissues obtained from 51 ccRCC patients. Compared to non-tumor tissues, Notch1 and Jagged1 expression was significantly elevated both in mRNA and protein levels in tumors. Tissue samples of localized and metastatic tumors were divided into three groups based on their tumor stages and the relative mRNA expression of Notch1 and Jagged1 were analyzed. Compared to localized tumors, Notch1 expression was significantly elevated in metastatic tumors in T1 stage while Jagged1 expression was not statistically different between localized and metastatic tumors of all stages. The average size of metastatic tumors was significantly larger than localized tumors in T1 stage ccRCC and the elevated expression of Notch1 was significantly positive correlated with the tumor diameter. The functional significance of Notch signaling was studied by transfection of 786-O, Caki-1 and HKC cell lines with full-length expression plasmids of Notch1 and Jagged1

Potential of Core-Collapse Supernova Neutrino Detection at JUNO

JUNO is an underground neutrino observatory under construction in Jiangmen, China. It uses 20kton liquid scintillator as target, which enables it to detect supernova burst neutrinos of a large statistics for the next galactic core-collapse supernova (CCSN) and also pre-supernova neutrinos from the nearby CCSN progenitors. All flavors of supernova burst neutrinos can be detected by JUNO via several interaction channels, including inverse beta decay, elastic scattering on electron and proton, interactions on C12 nuclei, etc. This retains the possibility for JUNO to reconstruct the energy spectra of supernova burst neutrinos of all flavors. The real time monitoring systems based on FPGA and DAQ are under development in JUNO, which allow prompt alert and trigger-less data acquisition of CCSN events. The alert performances of both monitoring systems have been thoroughly studied using simulations. Moreover, once a CCSN is tagged, the system can give fast characterizations, such as directionality and light curve

Detection of the Diffuse Supernova Neutrino Background with JUNO

As an underground multi-purpose neutrino detector with 20 kton liquid scintillator, Jiangmen Underground Neutrino Observatory (JUNO) is competitive with and complementary to the water-Cherenkov detectors on the search for the diffuse supernova neutrino background (DSNB). Typical supernova models predict 2-4 events per year within the optimal observation window in the JUNO detector. The dominant background is from the neutral-current (NC) interaction of atmospheric neutrinos with 12C nuclei, which surpasses the DSNB by more than one order of magnitude. We evaluated the systematic uncertainty of NC background from the spread of a variety of data-driven models and further developed a method to determine NC background within 15\% with {\it{in}} {\it{situ}} measurements after ten years of running. Besides, the NC-like backgrounds can be effectively suppressed by the intrinsic pulse-shape discrimination (PSD) capabilities of liquid scintillators. In this talk, I will present in detail the improvements on NC background uncertainty evaluation, PSD discriminator development, and finally, the potential of DSNB sensitivity in JUNO

Real-time Monitoring for the Next Core-Collapse Supernova in JUNO

Core-collapse supernova (CCSN) is one of the most energetic astrophysical events in the Universe. The early and prompt detection of neutrinos before (pre-SN) and during the SN burst is a unique opportunity to realize the multi-messenger observation of the CCSN events. In this work, we describe the monitoring concept and present the sensitivity of the system to the pre-SN and SN neutrinos at the Jiangmen Underground Neutrino Observatory (JUNO), which is a 20 kton liquid scintillator detector under construction in South China. The real-time monitoring system is designed with both the prompt monitors on the electronic board and online monitors at the data acquisition stage, in order to ensure both the alert speed and alert coverage of progenitor stars. By assuming a false alert rate of 1 per year, this monitoring system can be sensitive to the pre-SN neutrinos up to the distance of about 1.6 (0.9) kpc and SN neutrinos up to about 370 (360) kpc for a progenitor mass of 30$M_{\odot}$ for the case of normal (inverted) mass ordering. The pointing ability of the CCSN is evaluated by using the accumulated event anisotropy of the inverse beta decay interactions from pre-SN or SN neutrinos, which, along with the early alert, can play important roles for the followup multi-messenger observations of the next Galactic or nearby extragalactic CCSN.Comment: 24 pages, 9 figure

DeepTM: A deep learning algorithm for prediction of melting temperature of thermophilic proteins directly from sequences

Thermally stable proteins find extensive applications in industrial production, pharmaceutical development, and serve as a highly evolved starting point in protein engineering. The thermal stability of proteins is commonly characterized by their melting temperature (Tm). However, due to the limited availability of experimentally determined Tm data and the insufficient accuracy of existing computational methods in predicting Tm, there is an urgent need for a computational approach to accurately forecast the Tm values of thermophilic proteins. Here, we present a deep learning-based model, called DeepTM, which exclusively utilizes protein sequences as input and accurately predicts the Tm values of target thermophilic proteins on a dataset consisting of 7790 thermophilic protein entries. On a test set of 1550 samples, DeepTM demonstrates excellent performance with a coefficient of determination (R2) of 0.75, Pearson correlation coefficient (P) of 0.87, and root mean square error (RMSE) of 6.24 ℃. We further analyzed the sequence features that determine the thermal stability of thermophilic proteins and found that dipeptide frequency, optimal growth temperature (OGT) of the host organisms, and the evolutionary information of the protein significantly affect its melting temperature. We compared the performance of DeepTM with recently reported methods, ProTstab2 and DeepSTABp, in predicting the Tm values on two blind test datasets. One dataset comprised 22 PET plastic-degrading enzymes, while the other included 29 thermally stable proteins of broader classification. In the PET plastic-degrading enzyme dataset, DeepTM achieved RMSE of 8.25 ℃. Compared to ProTstab2 (20.05 ℃) and DeepSTABp (20.97 ℃), DeepTM demonstrated a reduction in RMSE of 58.85% and 60.66%, respectively. In the dataset of thermally stable proteins, DeepTM (RMSE=7.66 ℃) demonstrated a 51.73% reduction in RMSE compared to ProTstab2 (RMSE=15.87 ℃). DeepTM, with the sole requirement of protein sequence information, accurately predicts the melting temperature and achieves a fully end-to-end prediction process, thus providing enhanced convenience and expediency for further protein engineering

A Recombinant Thermophilic and Glucose-Tolerant GH1 β-Glucosidase Derived from Hehua Hot Spring

As a crucial enzyme for cellulose degradation, β-glucosidase finds extensive applications in food, feed, and bioethanol production; however, its potential is often limited by inadequate thermal stability and glucose tolerance. In this study, a functional gene (lq-bg5) for a GH1 family β-glucosidase was obtained from the metagenomic DNA of a hot spring sediment sample and heterologously expressed in E. coli and the recombinant enzyme was purified and characterized. The optimal temperature and pH of LQ-BG5 were 55 °C and 4.6, respectively. The relative residual activity of LQ-BG5 exceeded 90% at 55 °C for 9 h and 60 °C for 6 h and remained above 100% after incubation at pH 5.0–10.0 for 12 h. More importantly, LQ-BG5 demonstrated exceptional glucose tolerance with more than 40% activity remaining even at high glucose concentrations of 3000 mM. Thus, LQ-BG5 represents a thermophilic β-glucosidase exhibiting excellent thermal stability and remarkable glucose tolerance, making it highly promising for lignocellulose development and utilization

Exogenous Oleic Acid and Palmitic Acid Improve Boar Sperm Motility via Enhancing Mitochondrial Β-Oxidation for ATP Generation

It takes several hours for mammalian sperm to migrate from the ejaculation or insemination site to the fertilization site in the female reproductive tract in which glucose, amino acids, and fatty acids are regarded as the primary substrates for ATP generation. The present study was designed to investigate whether oleic acid and palmitic acid were beneficial to boar sperm in vitro; and if yes, to elucidate the mechanism that regulates sperm motility. Therefore, the levels of oleic acid and palmitic acid, motility, membrane integrity, acrosome integrity, and apoptosis of sperm were evaluated. Moreover, the enzymes involved in mitochondrial β-oxidation (CPT1: carnitine palmitoyltransferase 1; ACADVL: long-chain acyl-coenzyme A dehydrogenase) were detected with immunofluorescence and Western blotting. Consequently, the ATP content and the activities of CPT1, ACADVL, malate dehydrogenase (MDH), and succinate dehydrogenase (SDH) were also measured. We observed that CPT1 and ACADVL were expressed in boar sperm and localized in the midpiece. The levels of oleic acid and palmitic acid were decreased during storage at 17 °C. The addition of oleic acid and palmitic acid significantly increased sperm motility, progressive motility, straight-line velocity (VSL), membrane integrity, and acrosome integrity with a simultaneous decrease in sperm apoptosis after seven days during storage. When sperm were incubated with oleic acid and palmitic acid at 37 °C for 3 h, the activities of CPT1 and ACADVL, the ATP level, the mitochondrial membrane potential, the activities of MDH and SDH, as well as sperm motility patterns were significantly increased compared to the control (p < 0.05). Moreover, the addition of etomoxir to the diluted medium in the presence of either oleic acid or palmitic acid and the positive effects of oleic acid and palmitic acid were counteracted. Together, these data suggest that boar sperm might utilize oleic acid and palmitic acid as energy substrates for ATP production via β-oxidation. The addition of these acids could improve sperm quality

Rice rhizodeposition and its utilization by microbial groups depends on N fertilization

Rhizodeposits have received considerable attention, as they play an important role in the regulation of soil carbon (C) sequestration and global C cycling and represent an important C and energy source for soil microorganisms. However, the utilization of rhizodeposits by microbial groups, their role in the turnover of soil organic matter (SOM) pools in rice paddies, and the effects of nitrogen (N) fertilization on rhizodeposition are nearly unknown. Rice (Oryza sativa L.) plants were grown in soil at five N fertilization rates (0, 10, 20, 40, or 60 mg N kg−1 soil) and continuously labeled in a 13CO2 atmosphere for 18 days during tillering. The utilization of root-derived C by microbial groups was assessed by 13C incorporation into phospholipid fatty acids. Rice shoot and root biomass strongly increased with N fertilization. Rhizodeposition increased with N fertilization, whereas the total 13C incorporation into microorganisms, as indicated by the percentage of 13C recovered in microbial biomass, decreased. The contribution of root-derived 13C to SOM formation increased with root biomass. The ratio of 13C in soil pools (SOM and microbial biomass) to 13C in roots decreased with N fertilization showing less incorporation and faster turnover with N. The 13C incorporation into fungi (18:2ω6,9c and 18:1ω9c), arbuscular mycorrhizal fungi (16:1ω5c), and actinomycetes (10Me 16:0 and 10Me 18:0) increased with N fertilization, whereas the 13C incorporation into gram-positive (i14:0, i15:0, a15:0, i16:0, i17:0, and a17:0) and gram-negative (16:1ω7c, 18:1ω7c, cy17:0, and cy19:0) bacteria decreased with N fertilization. Thus, the uptake and microbial processing of root-derived C was affected by N availability in soil. Compared with the unfertilized soil, the contribution of rhizodeposits to SOM and microorganisms increased at low to intermediate N fertilization rates but decreased at the maximum N input. We conclude that belowground C allocation and rhizodeposition by rice, microbial utilization of rhizodeposited C, and its stabilization within SOM pools are strongly affected by N availability: N fertilization adequate to the plant demand increases C incorporation in all these polls, but excessive N fertilization has negative effects not only on environmental pollution but also on C sequestration in soil

Microorganisms maintain C:N stoichiometric balance by regulating the priming effect in long-term fertilized soils

Labile carbon (C) inputs affect the soil carbon:nitrogen (C:N) ratio and microbial stoichiometric homeostasis, which control the intensity and direction of the priming effect (PE). Here, we clarified how soil microorganisms regulate enzyme production and PE to maintain the C:N stoichiometric balance. Specifically, we conducted an incubation experiment by adding 13C-labeled glucose to four long-term fertilized paddy soils: no fertilization; fertilization with mineral nitrogen, phosphorus, and potassium (NPK); NPK combined with straw; and NPK with manure (NPKM). After glucose addition, the dissolved organic carbon-to-ammonium (DOC:NH4+) ratio (24–39) initially increased, but subsequently decreased after day 2 following glucose exhaustion. In parallel, the microbial C:N imbalance [(DOC:NH4+):(microbial biomass C:microbial biomass N)] rapidly decreased from day 2 (4.6–7.2) to day 20 (<0.5). Thus, microorganisms became C limited after 20 days of incubation. Excess C, resulting from glucose addition, increased N-hydrolase (chitinase) production and N mining from soil organic matter (SOM) through positive PEs. However, C hydrolase (β-1,4-glucosidase and β-xylosidase) activity increased, while that of N hydrolase (chitinase) decreased, following glucose exhaustion. Consequently, the C:N microbial biomass ratio increased as the DOC:NH4+ ratio decreased, leading to negative PEs. NPKM-fertilized soil had the largest cumulative PE (2.3% of soil organic carbon) because it had the highest microbial biomass and iron (Fe) reduction rate. Thus, this increased N mining from SOM maintained the microbial C:N stoichiometric balance. We concluded that soil microorganisms regulate C- and N-hydrolase production to control the intensity and direction of PE, maintaining the C:N stoichiometric balance in response to labile C inputs