Metabolomic analysis of human oral cancer cells with adenylate kinase 2 or phosphorylate glycerol kinase 1 inhibition.

The purpose of this study was to use liquid chromatography-mass spectrometry (LC-MS) with XCMS for a quantitative metabolomic analysis of UM1 and UM2 oral cancer cells after knockdown of metabolic enzyme adenylate kinase 2 (AK2) or phosphorylate glycerol kinase 1 (PGK1). UM1 and UM2 cells were initially transfected with AK2 siRNA, PGK1 siRNA or scrambled control siRNA, and then analyzed with LC-MS for metabolic profiles. XCMS analysis of the untargeted metabolomics data revealed a total of 3200-4700 metabolite features from the transfected UM1 or UM2 cancer cells and 369-585 significantly changed metabolites due to AK2 or PGK1 suppression. In addition, cluster analysis showed that a common group of metabolites were altered by AK2 knockdown or by PGK1 knockdown between the UM1 and UM2 cells. However, the set of significantly changed metabolites due to AK2 knockdown was found to be distinct from those significantly changed by PGK1 knockdown. Our study has demonstrated that LC-MS with XCMS is an efficient tool for metabolomic analysis of oral cancer cells, and knockdown of different genes results in distinct changes in metabolic phenotypes in oral cancer cells

Large area growth and electrical properties of p-type WSe2 atomic layers.

Transition metal dichacogenides represent a unique class of two-dimensional layered materials that can be exfoliated into single or few atomic layers. Tungsten diselenide (WSe(2)) is one typical example with p-type semiconductor characteristics. Bulk WSe(2) has an indirect band gap (∼ 1.2 eV), which transits into a direct band gap (∼ 1.65 eV) in monolayers. Monolayer WSe(2), therefore, is of considerable interest as a new electronic material for functional electronics and optoelectronics. However, the controllable synthesis of large-area WSe(2) atomic layers remains a challenge. The studies on WSe(2) are largely limited by relatively small lateral size of exfoliated flakes and poor yield, which has significantly restricted the large-scale applications of the WSe(2) atomic layers. Here, we report a systematic study of chemical vapor deposition approach for large area growth of atomically thin WSe(2) film with the lateral dimensions up to ∼ 1 cm(2). Microphotoluminescence mapping indicates distinct layer dependent efficiency. The monolayer area exhibits much stronger light emission than bilayer or multilayers, consistent with the expected transition to direct band gap in the monolayer limit. The transmission electron microscopy studies demonstrate excellent crystalline quality of the atomically thin WSe(2). Electrical transport studies further show that the p-type WSe(2) field-effect transistors exhibit excellent electronic characteristics with effective hole carrier mobility up to 100 cm(2) V(-1) s(-1) for monolayer and up to 350 cm(2) V(-1) s(-1) for few-layer materials at room temperature, comparable or well above that of previously reported mobility values for the synthetic WSe(2) and comparable to the best exfoliated materials

SparseGAN: Sparse Generative Adversarial Network for Text Generation

It is still a challenging task to learn a neural text generation model under the framework of generative adversarial networks (GANs) since the entire training process is not differentiable. The existing training strategies either suffer from unreliable gradient estimations or imprecise sentence representations. Inspired by the principle of sparse coding, we propose a SparseGAN that generates semantic-interpretable, but sparse sentence representations as inputs to the discriminator. The key idea is that we treat an embedding matrix as an over-complete dictionary, and use a linear combination of very few selected word embeddings to approximate the output feature representation of the generator at each time step. With such semantic-rich representations, we not only reduce unnecessary noises for efficient adversarial training, but also make the entire training process fully differentiable. Experiments on multiple text generation datasets yield performance improvements, especially in sequence-level metrics, such as BLEU

Infproto-Powered Adaptive Classifier and Agnostic Feature Learning for Single Domain Generalization in Medical Images

Designing a single domain generalization (DG) framework that generalizes from one source domain to arbitrary unseen domains is practical yet challenging in medical image segmentation, mainly due to the domain shift and limited source domain information. To tackle these issues, we reason that domain-adaptive classifier learning and domain-agnostic feature extraction are key components in single DG, and further propose an adaptive infinite prototypes (InfProto) scheme to facilitate the learning of the two components. InfProto harnesses high-order statistics and infinitely samples class-conditional instance-specific prototypes to form the classifier for discriminability enhancement. We then introduce probabilistic modeling and provide a theoretic upper bound to implicitly perform the infinite prototype sampling in the optimization of InfProto. Incorporating InfProto, we design a hierarchical domain-adaptive classifier to elasticize the model for varying domains. This classifier infinitely samples prototypes from the instance and mini-batch data distributions, forming the instance-level and mini-batch-level domain-adaptive classifiers, thereby generalizing to unseen domains. To extract domain-agnostic features, we assume each instance in the source domain is a micro source domain and then devise three complementary strategies, i.e., instance-level infinite prototype exchange, instance-batch infinite prototype interaction, and consistency regularization, to constrain outputs of the hierarchical domain-adaptive classifier. These three complementary strategies minimize distribution shifts among micro source domains, enabling the model to get rid of domain-specific characterizations and, in turn, concentrating on semantically discriminative features. Extensive comparison experiments demonstrate the superiority of our approach compared with state-of-the-art counterparts, and comprehensive ablation studies verify the effect of each proposed component. Notably, our method exhibits average improvements of 15.568% and 17.429% in dice on polyp and surgical instrument segmentation benchmarks

MULTI-SCALE CHARACTERIZIATION OF PORE STRUCTURE AND MASS TRANSPORT IN NATURAL ROCKS

The mass transport process in porous natural rocks is notably influenced by the pore structure with both geometrical and topological attributes. Nevertheless, previous studies have not taken into consideration the sample size effect or the impact of diagenesis processes on petrophysical investigations of rocks, fluids, and rock-fluid interactions. Six rocks (one granodiorite, one limestone, two chalks, one mudstone, and one dolostone) with different extents of heterogeneity at six different particle sizes were studied to describe the effects of pore structure (especially connectivity) on mass transport. Thirteen geologically different rocks (two marbles, four fossiliferous limestones, six mudstones, and one sandstone) were studied to examine the influence of diagenesis on petrophysical parameters and the Archie\u27s cementation factor (m). The methods applied for both studies were (i) porosity measurements of granular rocks, (ii) analyses of gas-phase diffusive transport in a bed of packed particles and intact rocks along with the development of a solid quartz method at six particle sizes to identify the intraparticle diffusion contribution, and (iii) batch sorption tests of multiple ions (anions and cations) using inductively coupled plasma-mass spectrometry. The granular porosity measurement results reveal that with decreasing particle sizes, the effective porosities for the “heterogenous” group of rocks (Grimsel granodiorite and Edwards limestone) increase, whereas the porosities of the “homogeneous” group (two Israel chalk samples, Japan mudstone, and Wyoming dolostone) remain roughly constant. Moreover, the batch sorption work displays a different affinity of rocks for various tracers in anionic and cationic forms. For Grimsel granodiorite, Japan mudstone, and Wyoming dolostone, the adsorption capacity of Sm3+ and Eu3+ increases as the particle size decreases. Cementation factor results show that diagenesis and microfractures could be root causes of various values of cementation factors in 13 natural rock samples. In general, this integrated research of grain size distribution, granular rock porosity, intraparticle diffusivity, ionic sorption capacity, and diagenetic pattern gives insights into the pore connectivity effect on both physical and chemical transport behaviors in different lithologies with different particle sizes

Who should provide a trade-in service under the online agency-selling mode?

In real world practice, trade-in programs are offered by either a manufacturer or an e-commerce platform. Parties that offer a trade-in service are faced with a trade-off between trade-in rebates and the residual income. By adopting the game theory, this paper explored the selection of trade-in provider with respect to a manufacturer and an e-commerce platform. The results show that in some cases, all trade-in models generated higher manufacturing costs than models with no trade-in program. However, in other cases, not all trade-in models can cope with manufacturing costs that are higher than those associated with models that have no trade-in program. Furthermore, both above two firms will offer the trade-ins when profits which they have obtained satisfied a certain condition. We also identified an interesting phenomenon whereby the manufacturer decided whether it wanted to delegate the trade-ins to the e-commerce platform or provide it jointly. The e-commerce platform can decide whether it wants to accept the delegation or jointly offer it. This study also obtain that trade-in models makes customers get more surplus and can produce greater environmental benefits. Moreover, both the customer surplus and the environmental benefits in delegated trade-in model is the same that in jointly trade-in model.</p