Mapping Corporate Water Risk – Investigation of Baseline Water Stress for Japanese Companies

As water risk becomes more severe and noticeable in recent years, corporate water risk also receives more attention in corporate management. Realizing that water risk should be examined on a regional scale, this study started from a location perspective and examined corporate water risk of ten Japanese companies by mapping their facilities on the Aqueduct global baseline water stress map and calculating fraction of facilities that are in areas with high water risk (“number fraction”). It was found that about 40% of water-sensitive facilities both inside and outside Japan are in high-waterrisk areas. Variation in number fraction values is generally weak and become stronger when number of facilities is low. As number of facilities of a certain corporation, line of business or region increases, the number fraction value approaches world average. This indicates that larger entities are able to adopt more universal water management strategies. By using different layers of water risk data provided by Aqueduct, it was observed that results vary with the chosen indicator. When seasonal variability or overall water risk is used, number fraction values drop significantly to less than 20%, especially in Japan. High number fraction values can be attributed to facilities in certain regions, but the results derived from one indicator cannot be used to predict results derived from another indicator because they all focus on different aspects of water risk. This suggests that choice of indicators should be based on specific situations. Finally, validation of number fraction as a measurement of impacts from water risk using share price fluctuation was done. The validation was not successful and the ten corporations don’t show significant differences in their share price behavior with different number fractions. It was suggested that sector-specific indexes and more financial metrics be used for future analysis, which can be focusing on a bigger portfolio.Master of ScienceSchool for Environment and SustainabilityUniversity of Michiganhttps://deepblue.lib.umich.edu/bitstream/2027.42/154878/1/Chen Muhan Thesis.pd

The Effect of Temperature and Strain Rate on the Interfacial Behavior of Glass Fiber Reinforced Polypropylene Composites: A Molecular Dynamics Study

To make better use of fiber reinforced polymer composites in automotive applications, a clearer knowledge of its interfacial properties under dynamic and thermal loadings is necessary. In the present study, the interfacial behavior of glass fiber reinforced polypropylene (PP) composites under different loading temperatures and strain rates were investigated via molecular dynamics simulation. The simulation results reveal that PP molecules move easily to fit tensile deformation at higher temperatures, resulting in a lower interfacial strength of glass fiber–PP interface. The interfacial strength is enhanced with increasing strain rate because the atoms do not have enough time to relax at higher strain rates. In addition, the non-bonded interaction energy plays a crucial role during the tensile deformation of composites. The damage evolution of glass fiber–PP interface follows Weibull’s distribution. At elevated temperatures, tensile loading is more likely to cause cohesive failure because the mechanical property of PP is lower than that of the glass fiber–PP interface. However, at higher strain rates, the primary failure mode is interfacial failure because the strain rate dependency of PP is more pronounced than that of the glass fiber–PP interface. The relationship between the failure modes and loading conditions obtained by molecular dynamics simulation is consistent with the author’s previous experimental studies

MAG-GNN: Reinforcement Learning Boosted Graph Neural Network

While Graph Neural Networks (GNNs) recently became powerful tools in graph learning tasks, considerable efforts have been spent on improving GNNs' structural encoding ability. A particular line of work proposed subgraph GNNs that use subgraph information to improve GNNs' expressivity and achieved great success. However, such effectivity sacrifices the efficiency of GNNs by enumerating all possible subgraphs. In this paper, we analyze the necessity of complete subgraph enumeration and show that a model can achieve a comparable level of expressivity by considering a small subset of the subgraphs. We then formulate the identification of the optimal subset as a combinatorial optimization problem and propose Magnetic Graph Neural Network (MAG-GNN), a reinforcement learning (RL) boosted GNN, to solve the problem. Starting with a candidate subgraph set, MAG-GNN employs an RL agent to iteratively update the subgraphs to locate the most expressive set for prediction. This reduces the exponential complexity of subgraph enumeration to the constant complexity of a subgraph search algorithm while keeping good expressivity. We conduct extensive experiments on many datasets, showing that MAG-GNN achieves competitive performance to state-of-the-art methods and even outperforms many subgraph GNNs. We also demonstrate that MAG-GNN effectively reduces the running time of subgraph GNNs.Comment: Accepted to NeurIPS 202

Extending the Design Space of Graph Neural Networks by Rethinking Folklore Weisfeiler-Lehman

Message passing neural networks (MPNNs) have emerged as the most popular framework of graph neural networks (GNNs) in recent years. However, their expressive power is limited by the 1-dimensional Weisfeiler-Lehman (1-WL) test. Some works are inspired by $k$-WL/FWL (Folklore WL) and design the corresponding neural versions. Despite the high expressive power, there are serious limitations in this line of research. In particular, (1) $k$-WL/FWL requires at least $O(n^k)$ space complexity, which is impractical for large graphs even when $k=3$; (2) The design space of $k$-WL/FWL is rigid, with the only adjustable hyper-parameter being $k$. To tackle the first limitation, we propose an extension, $(k,t)$-FWL. We theoretically prove that even if we fix the space complexity to $O(n^k)$ (for any $k\geq 2$) in $(k,t)$-FWL, we can construct an expressiveness hierarchy up to solving the graph isomorphism problem. To tackle the second problem, we propose $k$-FWL+, which considers any equivariant set as neighbors instead of all nodes, thereby greatly expanding the design space of $k$-FWL. Combining these two modifications results in a flexible and powerful framework $(k,t)$-FWL+. We demonstrate $(k,t)$-FWL+ can implement most existing models with matching expressiveness. We then introduce an instance of $(k,t)$-FWL+ called Neighborhood$^2$-FWL (N$^2$-FWL), which is practically and theoretically sound. We prove that N$^2$-FWL is no less powerful than 3-WL, and can encode many substructures while only requiring $O(n^2)$ space. Finally, we design its neural version named N$^2$-GNN and evaluate its performance on various tasks. N$^2$-GNN achieves record-breaking results on ZINC-Subset (0.059), outperforming previous SOTA results by 10.6%. Moreover, N$^2$-GNN achieves new SOTA results on the BREC dataset (71.8%) among all existing high-expressive GNN methods.Comment: Accepted to NeurIPS 202

Submucosal gland differentiation and implications in esophageal basaloid squamous cell carcinomas

Esophageal basaloid squamous cell carcinoma (BSCC) is a heterogenous entity with multilineage differentiation. It lacks systematical analysis on submucosal gland differentiation (SGD) due to the histological diversity and low incidence of esophageal BSCC. This study aims to find the correlation of SGD and clinicopathological features. A total of 152 esophageal BSCCs were separated into three histological groups: pure, mixed, and borderline group. The clinicopathological features were compared between different groups. The prevalence of SGD was also compared between cases of different groups. A panel of antibodies were used to identify SGD. The pure group differed from the mixed and borderline groups in many aspects, lymph node metastasis (LNM), cancer embolus, perineural invasion, and advanced stage occurred less frequently in the pure group (P<0.01). The pure group had a better but statistically insignificant overall survival (P=0.097). The squamous cell carcinoma (SCC) component or focal squamous differentiation was present in metastatic lymph nodes in almost all mixed BSCCs (95.7%, 22/23) with LNM. The LNM rate of superficial (T1b) BSCCs (17.6%, 6/34) was comparable to that of superficial (T1b) SCCs (18.5%, 57/308). However, LNM exclusively occurred in superficial mixed (3/5) and borderline (3/10) BSCCs. The IHC results demonstrated a prevalence of SGD in pure group (77%, 43/56). SGD is considered to be a favorable factor, while the squamous differentiation or invasive SCC component is an adverse factor in esophageal BSCCs. Refinement of classification is a promising way to improve patient management

Universal Normalization Enhanced Graph Representation Learning for Gene Network Prediction

Effective gene network representation learning is of great importance in bioinformatics to predict/understand the relation of gene profiles and disease phenotypes. Though graph neural networks (GNNs) have been the dominant architecture for analyzing various graph-structured data like social networks, their predicting on gene networks often exhibits subpar performance. In this paper, we formally investigate the gene network representation learning problem and characterize a notion of \textit{universal graph normalization}, where graph normalization can be applied in an universal manner to maximize the expressive power of GNNs while maintaining the stability. We propose a novel UNGNN (Universal Normalized GNN) framework, which leverages universal graph normalization in both the message passing phase and readout layer to enhance the performance of a base GNN. UNGNN has a plug-and-play property and can be combined with any GNN backbone in practice. A comprehensive set of experiments on gene-network-based bioinformatical tasks demonstrates that our UNGNN model significantly outperforms popular GNN benchmarks and provides an overall performance improvement of 16 $\%$ on average compared to previous state-of-the-art (SOTA) baselines. Furthermore, we also evaluate our theoretical findings on other graph datasets where the universal graph normalization is solvable, and we observe that UNGNN consistently achieves the superior performance

Overexpression of eIF-5A2 in mice causes accelerated organismal aging by increasing chromosome instability

<p>Abstract</p> <p>Background</p> <p>Amplification of 3q26 is one of the most frequent genetic alterations in many human malignancies. Recently, we isolated a novel oncogene <it>eIF-5A2 </it>within the 3q26 region. Functional study has demonstrated the oncogenic role of <it>eIF-5A2 </it>in the initiation and progression of human cancers. In the present study, we aim to investigate the physiological and pathological effect of <it>eIF-5A2 </it>in an <it>eIF-5A2 </it>transgenic mouse model.</p> <p>Methods</p> <p>An <it>eIF-5A2 </it>transgenic mouse model was generated using human <it>eIF-5A2 </it>cDNA. The <it>eIF-5A2 </it>transgenic mice were characterized by histological and immunohistochemistry analyses. The aging phenotypes were further characterized by wound healing, bone X-ray imaging and calcification analysis. Mouse embryo fibroblasts (MEF) were isolated to further investigate molecular mechanism of <it>eIF-5A2 </it>in aging.</p> <p>Results</p> <p>Instead of resulting in spontaneous tumor formation, overexpression of eIF-5A2 accelerated the aging process in adult transgenic mice. This included decreased growth rate and body weight, shortened life span, kyphosis, osteoporosis, delay of wound healing and ossification. Investigation of the correlation between cellular senescence and aging showed that cellular senescence is not required for the aging phenotypes in <it>eIF-5A2 </it>mice. Interestingly, we found that activation of <it>eIF-5A2 </it>repressed p19 level and therefore destabilized p53 in transgenic mouse embryo fibroblast (MEF) cells. This subsequently allowed for the accumulation of chromosomal instability, such as errors in cell dividing during metaphase and anaphase. Additionally, a significantly increase in number of aneuploidy cells (<it>p </it>< 0.05) resulted from an increase in the incidences of misaligned and lagging chromosomal materials, anaphase bridges, and micronuclei in the transgenic mice.</p> <p>Conclusion</p> <p>These observations suggest that <it>eIF-5A2 </it>mouse models could accelerate organismal aging by increasing chromosome instability.</p