National Culture\u27s Impact on Effectiveness of Supply Chain Disruption Management

The purpose of this research is to understand the national cultural antecedents that may help explain differences in supply chain disruptions mitigation abilities of companies from different countries. An analysis of survey data on disruption planning and response collected from various organizations worldwide was performed using weighted least square regression and factor analysis. We find that culture influences disruption planning and response. Statistical findings suggest that differences in disruption planning and response abilities between companies from different countries could be partly attributed to national culture. All five Hofstede’s dimensions of national culture, i.e., Power Distance, Individualism, Masculinity, Uncertainty Avoidance, and Long-term Orientation were shown to have a significant positive effect on disruption planning and response. National cultural dimensions and economic status of a country could be effectively used to predict disruption planning and response abilities of companies in various countries. Managers could benefit from our research as it could help them assess disruptions mitigation abilities of their partners located in other countries. Increasing international trade and globalization of supply chains accentuate the importance of our research

THE APPLICATION OF METABOLIC NETWORK ANALYSIS IN UNDERSTANDING AND TARGETING METABOLISM FOR DRUG DISCOVERY

Metabolic networks provide a vital framework for understanding the cellular metabolism in both physiological and pathophysiological states, which will ultimately facilitate network analysis-based drug discovery. In this thesis, we aim to employ a metabolic network analysis approach to study cancer metabolism (a pathophysiological state) and the metabolism of the bacterial pathogen, S. aureus (a physiological state), in order to understand, predict, and ultimately target cell metabolism for drug discovery. Cancer cells have distinct metabolism that highly depend on glycolysis instead of mitochondrial oxidative phosphorylation alone, even in the presence of oxygen, also called aerobic glycolysis or the Warburg effect, which may offer novel therapeutic opportunities. However, the origin of the Warburg effect is only partially understood. To understand the origin of cancer metabolism, our theoretical collaborator, Prof. Alexei Vazquez, developed a reduced flux balance model of human cell metabolism incorporating the macromolecular crowding (MC) constraint and the maximum glucose uptake constraint. The simulations successfully captured the main characteristics of cancer metabolism (aerobic glycolysis), indicating that MC constraint may be a potential origin of the Warburg effect. Notably, when we experimentally tested the model with mammalian cells from low to high growth rates as a proxy of MC alteration, we find that, consistent with the model, faster growing cells indeed have increased aerobic glycolysis. Moreover, the metabolic network analysis approach has also been shown to be capable of predicting the drug targets against pathogen metabolism when completely reconstructed metabolic networks are available. We deduced common antibiotic targets in Escherichia coli and Staphylococcus aureus by identifying shared tissue-specific or uniformly essential metabolic reactions in their metabolic networks. We then predicted through virtual screening dozens of potential inhibitors for several enzymes of these reactions and demonstrated experimentally that a subset of these inhibited both enzyme activities in vitro and bacterial cell viability. Our results indicate that the metabolic network analysis approach is able to facilitate the understanding of cellular metabolism by identifying potential constraints and predicting as well as ultimately targeting the metabolism of the organisms whose complete metabolic networks are available through the seamless integration of virtual screening with experimental validation

Empirical Research on the Relation Between Shares Reduction of Senior Executives and Earnings Management

Using listed companies with a presence of shares reduction of senior executives after the split share structure reform as research objects, we systematically study whether there are changes in earnings management behavior of senior executives’ shares reduction, as well as the relationship between the shares reduction degree and earnings management degree. Our analysis reveals that companies with a presence of shares reduction of senior executives have significantly positive controls over accounting earnings in the years of 2008 and 2009. However, there is no significant correlation between the level of earnings management of listed companies in China and the scale of shares reduction of senior executives

Contrastive Cross-Domain Sequential Recommendation

Cross-Domain Sequential Recommendation (CDSR) aims to predict future interactions based on user's historical sequential interactions from multiple domains. Generally, a key challenge of CDSR is how to mine precise cross-domain user preference based on the intra-sequence and inter-sequence item interactions. Existing works first learn single-domain user preference only with intra-sequence item interactions, and then build a transferring module to obtain cross-domain user preference. However, such a pipeline and implicit solution can be severely limited by the bottleneck of the designed transferring module, and ignores to consider inter-sequence item relationships. In this paper, we propose C^2DSR to tackle the above problems to capture precise user preferences. The main idea is to simultaneously leverage the intra- and inter- sequence item relationships, and jointly learn the single- and cross- domain user preferences. Specifically, we first utilize a graph neural network to mine inter-sequence item collaborative relationship, and then exploit sequential attentive encoder to capture intra-sequence item sequential relationship. Based on them, we devise two different sequential training objectives to obtain user single-domain and cross-domain representations. Furthermore, we present a novel contrastive cross-domain infomax objective to enhance the correlation between single- and cross- domain user representations by maximizing their mutual information. To validate the effectiveness of C^2DSR, we first re-split four e-comerce datasets, and then conduct extensive experiments to demonstrate the effectiveness of our approach C^2DSR.Comment: This paper has been accepted by CIKM 202

Enhancing Multimodal Entity and Relation Extraction with Variational Information Bottleneck

This paper studies the multimodal named entity recognition (MNER) and multimodal relation extraction (MRE), which are important for multimedia social platform analysis. The core of MNER and MRE lies in incorporating evident visual information to enhance textual semantics, where two issues inherently demand investigations. The first issue is modality-noise, where the task-irrelevant information in each modality may be noises misleading the task prediction. The second issue is modality-gap, where representations from different modalities are inconsistent, preventing from building the semantic alignment between the text and image. To address these issues, we propose a novel method for MNER and MRE by Multi-Modal representation learning with Information Bottleneck (MMIB). For the first issue, a refinement-regularizer probes the information-bottleneck principle to balance the predictive evidence and noisy information, yielding expressive representations for prediction. For the second issue, an alignment-regularizer is proposed, where a mutual information-based item works in a contrastive manner to regularize the consistent text-image representations. To our best knowledge, we are the first to explore variational IB estimation for MNER and MRE. Experiments show that MMIB achieves the state-of-the-art performances on three public benchmarks

Catabolic efficiency of aerobic glycolysis: The Warburg effect revisited

<p>Abstract</p> <p>Background</p> <p>Cancer cells simultaneously exhibit glycolysis with lactate secretion and mitochondrial respiration even in the presence of oxygen, a phenomenon known as the Warburg effect. The maintenance of this mixed metabolic phenotype is seemingly counterintuitive given that aerobic glycolysis is far less efficient in terms of ATP yield per moles of glucose than mitochondrial respiration.</p> <p>Results</p> <p>Here, we resolve this apparent contradiction by expanding the notion of metabolic efficiency. We study a reduced flux balance model of ATP production that is constrained by the glucose uptake capacity and by the solvent capacity of the cell's cytoplasm, the latter quantifying the maximum amount of macromolecules that can occupy the intracellular space. At low glucose uptake rates we find that mitochondrial respiration is indeed the most efficient pathway for ATP generation. Above a threshold glucose uptake rate, however, a gradual activation of aerobic glycolysis and slight decrease of mitochondrial respiration results in the highest rate of ATP production.</p> <p>Conclusions</p> <p>Our analyses indicate that the Warburg effect is a favorable catabolic state for all rapidly proliferating mammalian cells with high glucose uptake capacity. It arises because while aerobic glycolysis is less efficient than mitochondrial respiration in terms of ATP yield per glucose uptake, it is more efficient in terms of the required solvent capacity. These results may have direct relevance to chemotherapeutic strategies attempting to target cancer metabolism.</p

A Genome-Wide SNP Scan Reveals Novel Loci for Egg Production and Quality Traits in White Leghorn and Brown-Egg Dwarf Layers

Availability of the complete genome sequence as well as high-density SNP genotyping platforms allows genome-wide association studies (GWAS) in chickens. A high-density SNP array containing 57,636 markers was employed herein to identify associated variants underlying egg production and quality traits within two lines of chickens, i.e., White Leghorn and brown-egg dwarf layers. For each individual, age at first egg (AFE), first egg weight (FEW), and number of eggs (EN) from 21 to 56 weeks of age were recorded, and egg quality traits including egg weight (EW), eggshell weight (ESW), yolk weight (YW), eggshell thickness (EST), eggshell strength (ESS), albumen height(AH) and Haugh unit(HU) were measured at 40 and 60 weeks of age. A total of 385 White Leghorn females and 361 brown-egg dwarf dams were selected to be genotyped. The genome-wide scan revealed 8 SNPs showing genome-wise significant (P<1.51E-06, Bonferroni correction) association with egg production and quality traits under the Fisher's combined probability method. Some significant SNPs are located in known genes including GRB14 and GALNT1 that can impact development and function of ovary, but more are located in genes with unclear functions in layers, and need to be studied further. Many chromosome-wise significant SNPs were also detected in this study and some of them are located in previously reported QTL regions. Most of loci detected in this study are novel and the follow-up replication studies may be needed to further confirm the functional significance for these newly identified SNPs