The effects of halogen elements on the opening of an icosahedral B12 framework

The fully halogenated or hydrogenated B12X12 (X = H, F, Cl, Br and I) clusters are confirmed to be icosahedral. On the other hand, the bare B12 cluster is shown to have a planar structure. A previous study showed that a transformation from an icosahedron to a plane happens when 5 to 7 iodine atoms are substituted. Later, the transition was confirmed to be seven iodine substitutions based on an infrared spectroscopy study. In this study, we investigated the effects of different halogen atoms on the opening of the B12 icosahedral cage by means of density functional theory calculations. We found that the halogen elements do not have significant effects on the geometries of the clusters. The computed IR spectra show similar representative peaks for all halogen substituted clusters. Interestingly, we found a blue-shift in the IR spectra with the increase in the mass of the halogen atoms. Further, we compared the Gibbs free energies at different temperatures for different halogen atoms. The results show that the Gibbs free energy differences between open and close structures of B12X7 become larger when heavier halogen atoms are present. This interesting finding was subsequently investigated by energy decomposition analysis

A distinct sortase SrtB anchors and processes a streptococcal adhesin AbpA with a novel structural property.

Surface display of proteins by sortases in Gram-positive bacteria is crucial for bacterial fitness and virulence. We found a unique gene locus encoding an amylase-binding adhesin AbpA and a sortase B in oral streptococci. AbpA possesses a new distinct C-terminal cell wall sorting signal. We demonstrated that this C-terminal motif is required for anchoring AbpA to cell wall. In vitro and in vivo studies revealed that SrtB has dual functions, anchoring AbpA to the cell wall and processing AbpA into a ladder profile. Solution structure of AbpA determined by NMR reveals a novel structure comprising a small globular α/β domain and an extended coiled-coil heliacal domain. Structural and biochemical studies identified key residues that are crucial for amylase binding. Taken together, our studies document a unique sortase/adhesion substrate system in streptococci adapted to the oral environment rich in salivary amylase

Thermal properties of MECDP copolyesters

MECDP copolyesters based on poly(ethylene terephthalate) were prepared with sodium-5-sulfo-bis-(hydroxyethyl)-isophthalate and poly(ethylene glycol) units as modifiers. Thermal properties of these copolyesters were characterized by differential scanning calorimetry and thermal gravity analyzer. Experimental results indicated that glass transition temperature, melting temperature and thermal degradation temperature reduced with increasing the poly(ethylene glycol) content. The incorporation of poly(ethylene glycol) increased the flexibility and irregularity of molecular chains which led to lower crystallinity, and bought more ether bonds into molecular chains. Besides, the thermal degradation under oxygen condition happened easily compared to that under nitrogen condition

N N bar,Delta bar N, Delta N bar excitation for the pion propagator in nuclear matter

The particle-hole and Delta -hole excitations are well-known elementary excitation modes for the pion propagator in nuclear matter. But, the excitation also involves antiparticles, namely, nucleon-antinucleon, anti-Delta-nucleon and Delta-antinucleon excitations. These are important for high-energy momentum as well, and have not been studied before, to our knowledge. In this paper, we give both the formulas and the numerical calculations for the real and the imaginary parts of these excitations.Comment: Latex, 3 eps file

Microwave Integrated Circuits Design with Relational Induction Neural Network

The automation design of microwave integrated circuits (MWIC) has long been viewed as a fundamental challenge for artificial intelligence owing to its larger solution space and structural complexity than Go. Here, we developed a novel artificial agent, termed Relational Induction Neural Network, that can lead to an automotive design of MWIC and avoid brute-force computing to examine every possible solution, which is a significant breakthrough in the field of electronics. Through the experiments on microwave transmission line circuit, filter circuit and antenna circuit design tasks, strongly competitive results are obtained respectively. Compared with the traditional reinforcement learning method, the learning curve shows that the proposed architecture is able to quickly converge to the pre-designed MWIC model and the convergence rate is up to four orders of magnitude. This is the first study which has been shown that an agent through training or learning to automatically induct the relationship between MWIC's structures without incorporating any of the additional prior knowledge. Notably, the relationship can be explained in terms of the MWIC theory and electromagnetic field distribution. Our work bridges the divide between artificial intelligence and MWIC and can extend to mechanical wave, mechanics and other related fields

Gegen Qinlian Decoction Treats Diarrhea in Piglets by Modulating Gut Microbiota and Short-Chain Fatty Acids

Gut microbiota and its metabolites, short-chain fatty acids (SCFAs), play important roles in diarrheal diseases. Gegen Qinlian decoction (GQD), a Chinese herb formula, has been widely used to treat infectious diarrhea for centuries. However, little is known about the mechanism underlying its efficacy and whether it is mediated by gut microbiota and SCFAs. In this study, the composition of gut microbiota from bacterial diarrheal piglets was assessed using 16S rRNA analysis. The concentrations of fecal SCFAs were determined using a gas chromatography-mass spectrometer (GC-MS). The expression of mucosal pro-inflammatory cytokines in the colon was ascertained. Results showed that GQD reverses the reduction in the richness of gut microbiota, changes its structure, and significantly increases the relative abundances of SCFA-producing bacteria, including Akkermansia, Bacteroides, Clostridium, Ruminococcus, and Phascolarctobacterium. Moreover, GQD increased the levels of fecal SCFAs, including acetic acid, propionic acid, and butyric acid. GQD thus attenuates diarrhea in piglets. Further, our results suggest that the SCFAs could help to attenuate mucosal pro-inflammatory responses following GQD treatment by inhibiting histone deacetylase and the NF-κB pathway. We thus suggseted that gut microbiota play an important role during diarrhea treatment, an effect may be promoted by the GQD-induced structural changes of the gut microbial community and production of SCFAs. The increased levels of SCFAs probably provide further help to attenuate mucosal inflammation and diarrhea. In conclusion, our study might provide evidence that GQD treats diarrhea maybe involved in modulating gut microbiota and increasing SCFA levels

Stacking-ac4C: an ensemble model using mixed features for identifying n4-acetylcytidine in mRNA

N4-acetylcytidine (ac4C) is a modification of cytidine at the nitrogen-4 position, playing a significant role in the translation process of mRNA. However, the precise mechanism and details of how ac4C modifies translated mRNA remain unclear. Since identifying ac4C sites using conventional experimental methods is both labor-intensive and time-consuming, there is an urgent need for a method that can promptly recognize ac4C sites. In this paper, we propose a comprehensive ensemble learning model, the Stacking-based heterogeneous integrated ac4C model, engineered explicitly to identify ac4C sites. This innovative model integrates three distinct feature extraction methodologies: Kmer, electron-ion interaction pseudo-potential values (PseEIIP), and pseudo-K-tuple nucleotide composition (PseKNC). The model also incorporates the robust Cluster Centroids algorithm to enhance its performance in dealing with imbalanced data and alleviate underfitting issues. Our independent testing experiments indicate that our proposed model improves the Mcc by 15.61% and the ROC by 5.97% compared to existing models. To test our model’s adaptability, we also utilized a balanced dataset assembled by the authors of iRNA-ac4C. Our model showed an increase in Sn of 4.1%, an increase in Acc of nearly 1%, and ROC improvement of 0.35% on this balanced dataset. The code for our model is freely accessible at https://github.com/louliliang/ST-ac4C.git, allowing users to quickly build their model without dealing with complicated mathematical equations