New insights from GWAS for the cleft palate among han Chinese population

Genome wide association studies (GWAS) already have identified tens of susceptible loci for nonsyndromic cleft lip with or without cleft palate (NSCL/P). However, whether these loci associated with nonsyndromic cleft palate only (NSCPO) remains unknown. In this study, we replicated 38 SNPs (Single nucleotide polymorphisms) which has the most significant p values in published GWASs, genotyping by using SNPscan among 144 NSCPO trios from Western Han Chinese. We performed the transmission disequilibrium test (TDT) on individual SNPs and gene-gene (GxG) interaction analyses on the family data; Parent-of-Origin effects were assessed by separately considering transmissions from heterozygous fathers versus heterozygous mothers to affected offspring. Allelic TDT results showed that T allele at rs742071 (PAX7) (p=0.025, ORtransmission=3.00, 95%CI: 1.09-8.25) and G allele at rs2485893 (10kb 3? of SYT14) were associated with NSCPO (p=0.0036, ORtransmission= 0.60, 95%CI: 0.42-0.85). Genotypic TDT based on 3 pseudo controls further confirmed that rs742071 (p-value=0.03, ORtransmission=3.00, 95%CI: 1.09-8.25) and rs2485893 were associated with NSCPO under additive model (p-value= 0.02, ORtransmission= 0.66, 95%CI: 0.47-0.92). Genotypic TDT for epistatic interactions showed that rs4844913 (37kb 3? of DIEXF) interacted with rs11119388 (SYT14) (p-value=1.80E-08) and rs6072081 (53kb 3? of MAFB) interacted with rs6102085 (33kb 3? of MAFB) (p-value=3.60E-04) for NSCPO, suggesting they may act in the same pathway in the etiology of NSCPO. In this study, we found that rs742071 and rs2485893 were associated NSCPO from Han Chinese population; also, interactions of rs4844913:rs11119388 and rs6072081:rs6102085 for NSCPO were identified, gene-gene interactions have been proposed as a potential source of the remaining heritability, these findings provided new insights of the previous GWAS

Multi-source thermal model describing multi-region structure of transverse momentum spectra of identified particles and parameter dynamics of system evolution in relativistic collisions

In this article, the multi-region structure of transverse momentum ($p_T$) spectra of identified particles produced in relativistic collisions is studied by the multi-component standard distribution (the Boltzmann, Fermi-Dirac, or Bose-Einstein distribution) in the framework of a multi-source thermal model. Results are interpreted in the framework of string model phenomenology in which the multi-region of $p_T$ spectra corresponds to the string hadronization in the cascade process of string breaking. The contributions of the string hadronizations from the first-, second-, and third-, i.e., last-generations of string breakings mainly form high-, intermediate-, and low-$p_T$ regions, respectively. From the high- to low-$p_T$ regions, the extracted volume parameter increases rapidly, and temperature and flow velocity parameters decrease gradually. The multi-region of $p_T$ spectra reflects the volume, temperature, and flow velocity dynamics of the system evolution. Due to the successful application of the multi-component standard distribution, this work reflects that the simple classical theory can still play a great role in the field of complex relativistic collisions.Comment: 15 pages, 7 figures. Indian Journal of Physics, accepte

Extracting Kinetic Freeze-out Properties in High Energy Collisions Using a Multi-source Thermal Model

We study the transverse momentum ($p_T$) spectra of neutral pions and identified charged hadrons produced in proton--proton ($pp$), deuteron--gold ($d$--Au), and gold--gold (Au--Au) collisions at the center of mass energy $\sqrt{s_{NN}}=200$ GeV. The study is made in the framework of a multi-source thermal model used in the partonic level. It is assumed that the contribution to the $p_T$-value of any hadron comes from two or three partons with an isotropic distribution of the azimuthal angle. The contribution of each parton to the $p_T$-value of a given hadron is assumed to obey any one of the standard (Maxwell-Boltzmann, Fermi-Dirac, and Bose-Einstein) distributions with the kinetic freeze-out temperature and average transverse flow velocity. The $p_T$-spectra of the final-state hadrons can be fitted by the superposition of two or three components. The results obtained from our Monte Carlo method are used to fit the experimental results of the PHENIX and STAR Collaborations. The results of present work serve as a suitable reference baseline for other experiments and simulation studies.Comment: 18 pages, 8 figure

浅谈对药学本科新药质量标准教学的看法

Objective:To explore the drug quality standard teaching of the Pharmacy Undergraduate Students to improve the teaching quality and promote the cultivation of talents, etc. Methods: Discuss simply about the drug quality standard teaching content and teaching mode based on the characteristics of drug quality standard teaching in pharmaceutical. Results: Drug quality standard is very practical, and its teaching content and teaching mode should be adjusted and updated based on its characteristics and social needs. Conclusion: Multiple teaching modes are needed in drug quality standard teaching to provide talent reserve with the ability of reflection, research and innovation for drug quality control.目的  探讨药学本科新药质量标准的教学情况，以期提高教学质量、促进人才培养等。方法  结合药学本科新药质量标准教学的特点，浅谈对教学内容、教学模式等方面的看法。结果  新药质量标准的内容具有很强的实践性，根据其内容特点、社会需求，需要合理地调整和更新教学方式及教学内容。结论  质量标准教学需要多种教学方式相结合，为药品质量的控制提供强大的创新研究型人才储备

Improving Pre-movement Pattern Detection with Filter Bank Selection

Pre-movement decoding plays an important role in movement detection and is able to detect movement onset with low-frequency electroencephalogram (EEG) signals before the limb moves. In related studies, pre-movement decoding with standard task-related component analysis (STRCA) has been demonstrated to be efficient for classification between movement state and resting state. However, the accuracies of STRCA differ among subbands in the frequency domain. Due to individual differences, the best subband differs among subjects and is difficult to be determined. This study aims to improve the performance of the STRCA method by a feature selection on multiple subbands and avoid the selection of best subbands. This study first compares three frequency range settings ($M_1$: subbands with equally spaced bandwidths; $M_2$: subbands whose high cut-off frequencies are twice the low cut-off frequencies; $M_3$: subbands that start at some specific fixed frequencies and end at the frequencies in an arithmetic sequence.). Then, we develop a mutual information based technique to select the features in these subbands. A binary support vector machine classifier is used to classify the selected essential features. The results show that $M_3$ is a better setting than the other two settings. With the filter banks in $M_3$, the classification accuracy of the proposed FBTRCA achieves 0.8700$\pm$0.1022, which means a significantly improved performance compared to STRCA (0.8287$\pm$0.1101) as well as to the cross validation and testing method (0.8431$\pm$0.1078)

Assessing the Potential of Data Augmentation in EEG Functional Connectivity for Early Detection of Alzheimer’s Disease

Electroencephalographic (EEG) signals are acquired non-invasively from electrodes placed on the scalp. Experts in the field can use EEG signals to distinguish between patients with Alzheimer’s disease (AD) and normal control (NC) subjects using classification models. However, the training of deep learning or machine learning models requires a large number of trials. Datasets related to Alzheimer’s disease are typically small in size due to the lack of AD patient samples. The lack of data samples required for the training process limits the use of deep learning techniques for further development in clinical settings. We propose to increase the number of trials in the training set by means of a decomposition–recombination system consisting of three steps. Firstly, the original signals from the training set are decomposed into multiple intrinsic mode functions via multivariate empirical mode decomposition. Next, these intrinsic mode functions are randomly recombined across trials. Finally, the recombined intrinsic mode functions are added together as artificial trials, which are used for training the models. We evaluated the decomposition–recombination system on a small dataset using each subject’s functional connectivity matrices as inputs. Three different neural networks, including ResNet, BrainNet CNN, and EEGNet, were used. Overall, the system helped improve ResNet training in both the mild AD dataset, with an increase of 5.24%, and in the mild cognitive impairment dataset, with an increase of 4.50%. The evaluation of the proposed data augmentation system shows that the performance of neural networks can be improved by enhancing the training set with data augmentation. This work shows the need for data augmentation on the training of neural networks in the case of small-size AD datasets.Fil: Jia, Hao. Universitat de Vic; España. Nankai University; ChinaFil: Huang, Zihao. Nankai University; ChinaFil: Caiafa, César Federico. Provincia de Buenos Aires. Gobernación. Comisión de Investigaciones Científicas. Instituto Argentino de Radioastronomía. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Instituto Argentino de Radioastronomía; ArgentinaFil: Duan, Feng. Nankai University; ChinaFil: Zhang, Yu. Lehigh University; Estados UnidosFil: Sun, Zhe. Juntendo University; ChinaFil: Solé Casals, Jordi. Universitat de Vic; Españ