TikTok Use Motivators: A Latent Profile Analysis of TikTok Use Motives

Prior social media research has identified a range of motives within a classic framework of use and gratification to answer why people use social media. To date, most work has used a variable-centered approach to investigate how TikTok use motives that are quantified with a composite score to influence outcomes. By comparison with prior work, this current study conducted 2 studies (Study 1: full-time employee; Study 2: college student; Ntotal = 680) that investigated TikTok use motives or gratifications following a person-centered approach. We conducted latent profile analysis and identified four profiles of TikTok use motives: deep motivators, lone motivators, mood-elevating motivators, and slight motivators. We also found that these motivator profiles differentially predicted individual outcomes (TikTok addiction, labile self-esteem, subjective well-being, and engagement). Our findings contribute to the TikTok use literature by exploring how TikTok use motives combine and develop different motivator profiles

Deducing topology of protein-protein interaction networks from experimentally measured sub-networks.

BackgroundProtein-protein interaction networks are commonly sampled using yeast two hybrid approaches. However, whether topological information reaped from these experimentally-measured sub-networks can be extrapolated to complete protein-protein interaction networks is unclear.ResultsBy analyzing various experimental protein-protein interaction datasets, we found that they are not random samples of the parent networks. Based on the experimental bait-prey behaviors, our computer simulations show that these non-random sampling features may affect the topological information. We tested the hypothesis that a core sub-network exists within the experimentally sampled network that better maintains the topological characteristics of the parent protein-protein interaction network. We developed a method to filter the experimentally sampled network to result in a core sub-network that more accurately reflects the topology of the parent network. These findings have fundamental implications for large-scale protein interaction studies and for our understanding of the behavior of cellular networks.ConclusionThe topological information from experimental measured networks network as is may not be the correct source for topological information about the parent protein-protein interaction network. We define a core sub-network that more accurately reflects the topology of the parent network

Fe-doping induced superconductivity in charge-density-wave system 1T-TaS2

We report the interplay between charge-density-wave (CDW) and superconductivity of 1$T$-Fe$_{x}$Ta$_{1-x}$S$_{2}$ ($0\leq x \leq 0.05$) single crystals. The CDW order is gradually suppressed by Fe-doping, accompanied by the disappearance of pseudogap/Mott-gap as shown by the density functional theory (DFT) calculations. The superconducting state develops at low temperatures within the CDW state for the samples with the moderate doping levels. The superconductivity strongly depends on $x$ within a narrow range, and the maximum superconducting transition temperature is 2.8 K as $x=0.02$. We propose that the induced superconductivity and CDW phases are separated in real space. For high doping level ($x>0.04$), the Anderson localization (AL) state appears, resulting in a large increase of resistivity. We present a complete electronic phase diagram of 1$T$-Fe$_{x}$Ta$_{1-x}$S$_{2}$ system that shows a dome-like $T_{c}(x)$

(3,5-Dinitro-1,3,5-triazinan-1-yl)methanone

In the title compound, C5H9N5O5, prepared from hexa­mine by acetyl­ation and nitration, the triazine ring adopts a chair conformation with all three substituent groups lying on the same side of the ring

Robust Face Recognition With Kernelized Locality-Sensitive Group Sparsity Representation

In this paper, a novel joint sparse representation method is proposed for robust face recognition. We embed both group sparsity and kernelized locality-sensitive constraints into the framework of sparse representation. The group sparsity constraint is designed to utilize the grouped structure information in the training data. The local similarity between test and training data is measured in the kernel space instead of the Euclidian space. As a result, the embedded nonlinear information can be effectively captured, leading to a more discriminative representation. We show that, by integrating the kernelized local-sensitivity constraint and the group sparsity constraint, the embedded structure information can be better explored, and significant performance improvement can be achieved. On the one hand, experiments on the ORL, AR, extended Yale B, and LFW data sets verify the superiority of our method. On the other hand, experiments on two unconstrained data sets, the LFW and the IJB-A, show that the utilization of sparsity can improve recognition performance, especially on the data sets with large pose variation