Conformational Dynamics of Insulin

We have exploited a prandial insulin analog to elucidate the underlying structure and dynamics of insulin as a monomer in solution. A model was provided by insulin lispro (the active component of Humalog®; Eli Lilly and Co.). Whereas NMR-based modeling recapitulated structural relationships of insulin crystals (T-state protomers), dynamic anomalies were revealed by amide-proton exchange kinetics in D2O. Surprisingly, the majority of hydrogen bonds observed in crystal structures are only transiently maintained in solution, including key T-state-specific inter-chain contacts. Long-lived hydrogen bonds (as defined by global exchange kinetics) exist only at a subset of four α-helical sites (two per chain) flanking an internal disulfide bridge (cystine A20–B19); these sites map within the proposed folding nucleus of proinsulin. The anomalous flexibility of insulin otherwise spans its active surface and may facilitate receptor binding. Because conformational fluctuations promote the degradation of pharmaceutical formulations, we envisage that “dynamic re-engineering” of insulin may enable design of ultra-stable formulations for humanitarian use in the developing world

J/ψJ/\psi decays into ω(ϕ)f1(1285)\omega (\phi) f_1(1285) and \omega (\phi) \,''f_1(1420)''

We perform a theoretical study of the $J/\psi \to \omega (\phi) K^* \bar{K} + c.c. \to \omega (\phi) K^0 \pi^+ K^-$ reactions with the assumption that the $f_1(1285)$ is dynamically generated from a single channel $K^* \bar{K} + c.c$ interaction in the chiral unitary approach. Two peaks in the $K^0 \pi^+ K^-$ invariant mass distribution are observed, one clear peak locates at the $f_1(1285)$ nominal mass, the other peak locates at around $1420\; \rm MeV$ with about $70 \;\rm MeV$ width. We conclude that the former peak is associated with the $f_1(1285)$ and the latter peak is not a genuine resonance but a manifestation of the kinematic effect in the higher energy region caused by the $K^* \bar{K} + c.c.$ decay mode of the $f_1(1285)$.Comment: 17 pages, 5 figures

Innovative modeling framework of chloride resistance of recycled aggregate concrete using ensemble-machine-learning methods

This study investigates the feasibility of introducing machine learning algorithms to predict the diffusion resistance to chloride penetration of recycled aggregate concrete (RAC). A total of 226 samples collated from published literature were used to train and test the developed machine learning framework, which integrated four standalone models and two ensemble models. The hyperparameters involved were fine-tuned by grid search and 10-fold cross-validation. Results showed that all the models had good performance in predicting the chloride penetration resistance of RAC and among them, the gradient boosting model outperformed the others. The water content was identified as the most critical factor affecting the chloride ion permeability of RAC based on the standardized regression coefficient analysis. The model’s interpretability was greatly improved through a two-way partial dependence analysis. Finally, based on the proposed machine learning models, a performance-based mixture design method and a service life prediction approach for RAC were developed, thereby offering novel and robust design tools for achieving more durable and resilient development goals in procuring sustainable concrete.This work was supported by the National Natural Science Foundation of China (52108123), Guangdong Basic and Applied Basic Research Foundation (2020A1515110101), and Guangdong Provincial Key Laboratory of Modern Civil Engineering Technology (2021B1212040003)

Enhancing CT Image synthesis from multi-modal MRI data based on a multi-task neural network framework

Image segmentation, real-value prediction, and cross-modal translation are critical challenges in medical imaging. In this study, we propose a versatile multi-task neural network framework, based on an enhanced Transformer U-Net architecture, capable of simultaneously, selectively, and adaptively addressing these medical image tasks. Validation is performed on a public repository of human brain MR and CT images. We decompose the traditional problem of synthesizing CT images into distinct subtasks, which include skull segmentation, Hounsfield unit (HU) value prediction, and image sequential reconstruction. To enhance the framework's versatility in handling multi-modal data, we expand the model with multiple image channels. Comparisons between synthesized CT images derived from T1-weighted and T2-Flair images were conducted, evaluating the model's capability to integrate multi-modal information from both morphological and pixel value perspectives.Comment: 4 pages, 3 figures, 2 table

Clinical study of intraocular pressure and retinal thickness affected by residual triamcinolone acetonide after vitrectomy

AIM: To study the effect of residual triamcinolone acetonide(TA)to intraocular pressure(IOP)and retinal thickness in patients after vitrectomy. METHODS: Retrospective study. The medical data of 83 patients(83 eyes)after vitrectomy in our hospital from October 2016 to October 2017 were analyzed retrospectively. The 83 patients were treated with TA as vitreous dyeing. Vitreous cavity was not filled with silicone oil or gas. Totally 32 eyes were observed that triamcinolone acetonide was residual in vitreous cavity, 51 eyes were not observed the residual, and after 1wk and 3mo, intraocular pressure and macular center concave thickness(CMT)of two groups was compared. RESULTS: There was no statistical difference in preoperative average intraocular pressure between two groups(t=0.56, P>0.05). After 1wk, IOP of no residual group was 15.48±3.8mmhg, IOP of residual Group was 20.09±6.14mmhg. IOP of residual group were higher than IOP of no residual group, the difference was statistically significant(t=3.81,Pt=4.54, Pt=3.75,Pt=0.21, P>0.05). CONCLUSION: The residual triamcinolone acetonide as a dyeing agent during vitrectomy may raise the risk of postoperative intraocular pressure in short term, and after 3mo without any significant effect on the thickness of macular center

Molecular pentaquark states with open charm and bottom flavors

We study the possibly-existing molecular pentaquark states with open charm and bottom flavors, {\it i.e.}, the states with the quark contents $c\bar{b}qqq$ and $b\bar{c}qqq$ ($q=u,d,s$). We investigate the meson-baryon interactions through the coupled-channel unitary approach within the local hidden-gauge formalism, and extract the poles by solving the Bethe-Salpeter equation in coupled channels. These poles qualify as molecular pentaquark states, which are dynamically generated from the meson-baryon interactions through the exchange of vector mesons. We calculate their masses and widths as well as their couplings to various coupled channels. Our results suggest the existence of the $\Sigma_c^{(*)} B^{(*)}$ and $\Sigma_b^{(*)} \bar{D}^{(*)}$ molecular states with isospin $I=1/2$ as well as the $\Xi_c^{(\prime,*)} B^{(*)}$ and $\Xi_b^{(\prime,*)} \bar{D}^{(*)}$ molecular states with isospin $I=0$.Comment: 48 pages, 5 figures, 5 tables, suggestions and comments welcom

A Four-Stage Data Augmentation Approach to ResNet-Conformer Based Acoustic Modeling for Sound Event Localization and Detection

In this paper, we propose a novel four-stage data augmentation approach to ResNet-Conformer based acoustic modeling for sound event localization and detection (SELD). First, we explore two spatial augmentation techniques, namely audio channel swapping (ACS) and multi-channel simulation (MCS), to deal with data sparsity in SELD. ACS and MDS focus on augmenting the limited training data with expanding direction of arrival (DOA) representations such that the acoustic models trained with the augmented data are robust to localization variations of acoustic sources. Next, time-domain mixing (TDM) and time-frequency masking (TFM) are also investigated to deal with overlapping sound events and data diversity. Finally, ACS, MCS, TDM and TFM are combined in a step-by-step manner to form an effective four-stage data augmentation scheme. Tested on the Detection and Classification of Acoustic Scenes and Events (DCASE) 2020 data sets, our proposed augmentation approach greatly improves the system performance, ranking our submitted system in the first place in the SELD task of DCASE 2020 Challenge. Furthermore, we employ a ResNet-Conformer architecture to model both global and local context dependencies of an audio sequence to yield further gains over those architectures used in the DCASE 2020 SELD evaluations.Comment: 12 pages, 8 figure