Characterizing Location-based Mobile Tracking in Mobile Ad Networks

Mobile apps nowadays are often packaged with third-party ad libraries to monetize user data

Identification of morphological fingerprint in perinatal brains using quasi-conformal mapping and contrastive learning

The morphological fingerprint in the brain is capable of identifying the uniqueness of an individual. However, whether such individual patterns are present in perinatal brains, and which morphological attributes or cortical regions better characterize the individual differences of ne-onates remain unclear. In this study, we proposed a deep learning framework that projected three-dimensional spherical meshes of three morphological features (i.e., cortical thickness, mean curvature, and sulcal depth) onto two-dimensional planes through quasi-conformal mapping, and employed the ResNet18 and contrastive learning for individual identification. We used the cross-sectional structural MRI data of 682 infants, incorporating with data augmentation, to train the model and fine-tuned the parameters based on 60 infants who had longitudinal scans. The model was validated on 30 longitudinal scanned infant data, and remarkable Top1 and Top5 accuracies of 71.37% and 84.10% were achieved, respectively. The sensorimotor and visual cortices were recognized as the most contributive regions in individual identification. Moreover, the folding morphology demonstrated greater discriminative capability than the cortical thickness, which could serve as the morphological fingerprint in perinatal brains. These findings provided evidence for the emergence of morphological fingerprints in the brain at the beginning of the third trimester, which may hold promising implications for understanding the formation of in-dividual uniqueness in the brain during early development

Characterization of two β-galactosidases LacZ and WspA1 from Nostoc flagelliforme with focus on the latter’s central active region

The identification and characterization of new β-galactosidases will provide diverse candidate enzymes for use in food processing industry. In this study, two β-galactosidases, Nf-LacZ and WspA1, from the terrestrial cyanobacterium Nostoc flagelliforme were heterologously expressed in Escherichia coli, followed by purification and biochemical characterization. Nf-LacZ was characterized to have an optimum activity at 40\ua0\ub0C and pH 6.5, different from that (45\ua0\ub0C and pH 8.0) of WspA1. Two enzymes had a similar Michaelis constant (Km = 0.5\ua0mmol/liter) against the substrate o-nitrophenyl-β-D-galactopyranoside. Their activities could be inhibited by galactostatin bisulfite, with IC50 values of 0.59\ua0\ub5M for Nf-LacZ and 1.18\ua0\ub5M for WspA1, respectively. Gel filtration analysis suggested that the active form of WspA1 was a dimer, while Nf-LacZ was functional as a larger multimer. WspA1 was further characterized by the truncation test, and its minimum central region was found to be from residues 188 to 301, having both the glycosyl hydrolytic and transgalactosylation activities. Finally, transgenic analysis with the GFP reporter protein found that the N-terminus of WspA1 (35 aa) might play a special role in the export of WspA1 from cells. In summary, this study characterized two cyanobacterial β-galactosidases for potential applications in food industry

Preliminary Study:Learning the Impact of Simulation Time on Reentry Location and Morphology Induced by Personalized Cardiac Modeling

Personalized cardiac modeling is widely used for studying the mechanisms of cardiac arrythmias. Due to the high demanding of computational resource of modeling, the arrhythmias induced in the models are usually simulated for just a few seconds. In clinic, it is common that arrhythmias last for more than several minutes and the morphologies of reentries are not always stable, so it is not clear that whether the simulation of arrythmias for just a few seconds is long enough to match the arrhythmias detected in patients. This study aimed to observe how long simulation of the induced arrhythmias in the personalized cardiac models is sufficient to match the arrhythmias detected in patients. A total of 5 contrast enhanced MRI datasets of patient hearts with myocardial infarction were used in this study. Then, a classification method based on Gaussian mixture model was used to detect the infarct tissue. For each reentry, 3 s and 10 s were simulated. The characteristics of each reentry simulated for different duration were studied. Reentries were induced in all 5 ventricular models and sustained reentries were induced at 39 stimulation sites in the model. By analyzing the simulation results, we found that 41% of the sustained reentries in the 3 s simulation group terminated in the longer simulation groups (10 s). The second finding in our simulation was that only 23.1% of the sustained reentries in the 3 s simulation did not change location and morphology in the extended 10 s simulation. The third finding was that 35.9% reentries were stable in the 3 s simulation and should be extended for the simulation time. The fourth finding was that the simulation results in 10 s simulation matched better with the clinical measurements than the 3 s simulation. It was shown that 10 s simulation was sufficient to make simulation results stable. The findings of this study not only improve the simulation accuracy, but also reduce the unnecessary simulation time to achieve the optimal use of computer resources to improve the simulation efficiency and shorten the simulation time to meet the time node requirements of clinical operation on patients