Covariant tensor formalism for partial wave analyses of ψ→ΔΔˉ\psi \rightarrow\Delta\bar{\Delta}

The covariant tensor formulae for partial wave analysis of $\psi \rightarrow\Delta\bar{\Delta}$, $\Delta \to p \pi^\pm$, $\bar{\Delta} \to \bar{p} \pi^\mp$ are derived, as well as the formulae for the decay sequence of $\psi \rightarrow N^* \bar{p}$, $N^* \to \Delta \pi^\mp$, $\Delta \to p \pi^\pm$. These formulae are practical for the experiments measuring $\psi$ decaying into $p \bar{p} \pi^+ \pi^-$ final states, such as BESIII with its recently collected huge $J/\psi$ and $\psi(2S)$ data samples.Comment: 10 page

Adipose-derived mesenchymal stem cells attenuate ischemic brain injuries in rats by modulating miR-21-3p/MAT2B signaling transduction

Aim To explore the mechanism underlying the protective effect of adipose-derived mesenchymal stem cells (ADMSCs) against ischemic stroke by focusing on miR-21-3p/ MAT2B axis. Methods Ischemic brain injury was induced in 126 rats by middle cerebral artery occlusion (MCAO). The effect of ADMSC administration on blood-brain barrier (BBB) condition, apoptosis, inflammation, and the activity of miR-21- 3p/MAT2B axis was assessed. The role of miR-21-3p inhibition in the function of ADMSCs was further validated in in vitro neural cells. Results ADMSCs administration improved BBB condition, inhibited apoptosis, and suppressed inflammation. It also reduced the abnormally high level of miR-21-3p in MCAO rats. Dual luciferase assays showed that miR-21-3p directly inhibited the MAT2B expression in neural cells, and miR-21 -3p inhibition by inhibitor or ADMSC-derived exosomes in neurons attenuated hypoxia/reoxygenation-induced impairments similarly to that of ADMSCs in vivo. Conclusion This study confirmed the protective effect of ADMSCs against ischemic brain injury exerted by suppressing miR-21-3p level and up-regulating MAT2B level

Monoclonal antibody humanness score and its applications

BACKGROUND: Monoclonal antibody therapeutics are rapidly gaining in popularity for the treatment of a myriad of diseases, ranging from cancer to autoimmune diseases and neurological diseases. Multiple forms of antibody therapeutics are in use today that differ in the amount of human sequence present in both the constant and variable regions, where antibodies that are more human-like usually have reduced immunogenicity in clinical trials. RESULTS: Here we present a method to quantify the humanness of the variable region of monoclonal antibodies and show that this method is able to clearly distinguish human and non-human antibodies with excellent specificity. After creating and analyzing a database of human antibody sequences, we conducted an in-depth analysis of the humanness of therapeutic antibodies, and found that increased humanness score is correlated with decreased immunogenicity of antibodies. We further discovered a surprisingly similarity in the immunogenicity of fully human antibodies and humanized antibodies that are more human-like based on their humanness score. CONCLUSIONS: Our results reveal that in most cases humanizing an antibody and confirming the humanness of the final form may be sufficient to eliminate immunogenicity issues to the same extent as using fully human antibodies. We created a public website to calculate the humanness score of any input antibody sequence based on our human antibody database. This tool will be of great value during the preclinical drug development process for new monoclonal antibody therapeutics

Predictive structure or paradigm size? Investigating the effects of i-complexity and e-complexity on the learnability of morphological systems

Research on cross-linguistic differences in morphological paradigms reveals a wide range of variation on many dimensions, including the number of categories expressed, the number of unique forms, and the number of inflectional classes. However, in an influential paper, Ackerman and Malouf (2013) argue that there is one dimension on which languages do not differ widely: in predictive structure. Predictive structure in a paradigm describes the extent to which forms predict each other, called i-complexity. Ackerman and Malouf (2013) show that although languages differ according to measure of surface paradigm complexity, called e-complexity, they tend to have low i-complexity. They conclude that morphological paradigms have evolved under a pressure for low i-complexity. Here, we evaluate the hypothesis that language learners are more sensitive to i-complexity than e-complexity by testing how well paradigms which differ on only these dimensions are learned. This could result in the typological findings Ackerman and Malouf (2013) report if even paradigms with very high e-complexity are relatively easy to learn, so long as they have low i-complexity. First, we summarize a recent work by Johnson et al. (2020) suggesting that both neural networks and human learners may actually be more sensitive to e-complexity than i-complexity. Then we build on this work, reporting a series of experiments which confirm that, indeed, across a range of paradigms that vary in either e- or icomplexity, neural networks (LSTMs) are sensitive to both, but show a larger effect of e-complexity (and other measures associated with size and diversity of forms). In human learners, we fail to find any effect of i-complexity on learning at all. Finally, we analyse a large number of randomly generated paradigms and show that e- and i-complexity are negatively correlated: paradigms with high e-complexity necessarily show low i-complexity. We discuss what these findings might mean for Ackerman and Malouf’s hypothesis, as well as the role of ease of learning versus generalization to novel forms in the evolution of paradigms

Electron Bunch Train Excited Higher-Order Modes in a Superconducting RF Cavity

Higher-order mode (HOM) based intra-cavity beam diagnostics has been proved effectively and conveniently in superconducting radio-frequency (SRF) accelerators. Our recent research shows that the beam harmonics in the bunch train excited HOM spectrum, which have much higher signal-to-noise ratio than the intrinsic HOM peaks, may also be useful for beam diagnostics. In this paper, we will present our study on bunch train excited HOMs, including the theoretic model and recent experiments carried out based on the DC-SRF photoinjector and SRF linac at Peking University.Comment: Supported by National Natural Science Foundation of China (11275014

A Method of EV Detour-to-Recharge Behavior Modeling and Charging Station Deployment

Electric vehicles (EVs) are increasingly used in transportation. Worldwide use of EVs, for their limited battery capacity, calls for effective planning of EVs charging stations to enhance the efficiency of using EVs. This paper provides a methodology of describing EV detouring behavior for recharging, and based on this, we adopt the extra driving length caused by detouring and the length of uncompleted route as the indicators of evaluating an EV charging station deployment plan. In this way, we can simulate EV behavior based on travel data (demand). Then, a genetic algorithm (GA) based EV charging station sitting optimization method is developed to obtain an effective plan. A detailed case study based on a 100-node 203-branch transportation network within a 30 km * 30 km region is included to test the effectiveness of our method. Insights from our method may be applicable for charging station planning in various transportation networks

Effect of dry heat, microwave and ultrasonic treatments on physicochemical properties of potato starch with or without pectin

Purpose: To investigate the effects of dry heat, microwave and ultrasonic treatments on the physicochemical properties of potato starch alone or blended with pectin. Methods: The physicochemical properties of potato starch gels prepared using microwave, ultrasonic and dry heat treatments were assessed. Pasting properties, gel strength, thermal properties and crystal texture of the potato starch were determined using Rapid Visco analyzer, texture profile analyzer, differential scanning calorimeter and x-ray diffractometer. Results: Dry heat and ultrasonic treatments significantly increased the peak viscosity of the potato starch, and significantly decreased its setback and pasting temperatures (p < 0.05). Dry heat treatment significantly increased the hardness, while dry heat and ultrasonic treatments significantly improved retrogradation of the potato starch (p < 0.05). Transparency of potato starch paste was significantly increased by the different treatments, except microwave treatment (p < 0.05). Potato starch gels blended with pectin and subjected to any of the treatments exhibited significantly increased hardness, when compared with raw potato starch (p < 0.05). The retrogradation of the potato starch was significantly improved by the different treatments. Dry heat and ultrasonic treatments significantly decreased the syneresis of potato starch with or without pectin (p < 0.05). The three treatments also significantly affected the gelatinization enthalpy of the potato starch with or without pectin, and exerted some effects on the crystallinity of the gels. Conclusion: The results obtained in this study suggest that differences in physicochemical properties of potato starch gels are due mainly to the degree of damage to starch granules caused by different treatments. The addition of pectin to potato starch gel greatly improves its hardness and retrogradation