Possible Molecular Structure of the Newly Observed Y(4260)

We suggest that the newly observed resonance Y(4260) is a $\chi_{c}-\rho^0$ molecule, which is an isovector. In this picture, we can easily interpret why $Y(4260)\to \pi^+\pi^-J/\psi$ has a larger rate than $Y(4260)\to D\bar D$ which has not been observed, and we also predict existence of the other two components of the isotriplet and another two possible partner states which may be observed in the future experiments. A direct consequence of this structure is that for this molecular structure $Y(4260)\to \pi^+\pi^-J/\psi$ mode is more favorable than $Y(4260)\to K\bar KJ/\psi$ which may have a larger fraction if other proposed structures prevail.Comment: 5 pages, 2 figures. Some descriptions changed, more references added and typos corrected. Published version in PR

On the second-order zero differential spectra of some power functions over finite fields

Boukerrou et al. (IACR Trans. Symmetric Cryptol. 2020(1), 331-362) introduced the notion of Feistel Boomerang Connectivity Table (FBCT), the Feistel counterpart of the Boomerang Connectivity Table (BCT), and the Feistel boomerang uniformity (which is the same as the second-order zero differential uniformity in even characteristic). FBCT is a crucial table for the analysis of the resistance of block ciphers to power attacks such as differential and boomerang attacks. It is worth noting that the coefficients of FBCT are related to the second-order zero differential spectra of functions. In this paper, by carrying out certain finer manipulations of solving specific equations over the finite field $\mathbb{F}_{p^n}$, we explicitly determine the second-order zero differential spectra of some power functions with low differential uniformity, and show that our considered functions also have low second-order zero differential uniformity. Our study pushes further former investigations on second-order zero differential uniformity and Feistel boomerang differential uniformity for a power function $F$

DeepEP: A Deep Learning Framework for Identifying Essential Proteins

Background: Essential proteins are crucial for cellular life and thus, identification of essential proteins is an important topic and a challenging problem for researchers. Recently lots of computational approaches have been proposed to handle this problem. However, traditional centrality methods cannot fully represent the topological features of biological networks. In addition, identifying essential proteins is an imbalanced learning problem; but few current shallow machine learning-based methods are designed to handle the imbalanced characteristics. Results: We develop DeepEP based on a deep learning framework that uses the node2vec technique, multi-scale convolutional neural networks and a sampling technique to identify essential proteins. In DeepEP, the node2vec technique is applied to automatically learn topological and semantic features for each protein in protein-protein interaction (PPI) network. Gene expression profiles are treated as images and multi-scale convolutional neural networks are applied to extract their patterns. In addition, DeepEP uses a sampling method to alleviate the imbalanced characteristics. The sampling method samples the same number of the majority and minority samples in a training epoch, which is not biased to any class in training process. The experimental results show that DeepEP outperforms traditional centrality methods. Moreover, DeepEP is better than shallow machine learning-based methods. Detailed analyses show that the dense vectors which are generated by node2vec technique contribute a lot to the improved performance. It is clear that the node2vec technique effectively captures the topological and semantic properties of PPI network. The sampling method also improves the performance of identifying essential proteins. Conclusion: We demonstrate that DeepEP improves the prediction performance by integrating multiple deep learning techniques and a sampling method. DeepEP is more effective than existing methods

Antimicrobial resistance and the growing threat of drug-resistant tuberculosis

The purpose of this study was to investigate the associations between birth weight, chest circumference, and lung function in preschool children from e-waste exposure area. A total of 206 preschool children from Guiyu (an e-waste recycling area) and Haojiang and Xiashan (the reference areas) in China were recruited and required to undergo physical examination, blood tests, and lung function tests during the study period. Birth outcome such as birth weight and birth height were obtained by questionnaire. Children living in the e-waste-exposed area have a lower birth weight, chest circumference, height, and lung function when compare to their peers from the reference areas (all p value <0.05). Both Spearman and partial correlation analyses showed that birth weight and chest circumference were positively correlated with lung function levels including forced vital capacity (FVC) and forced expiratory volume in 1 s (FEV1). After adjustment for the potential confounders in further linear regression analyses, birth weight, and chest circumference were positively associated with lung function levels, respectively. Taken together, birth weight and chest circumference may be good predictors for lung function levels in preschool children