The Effect of Degrading the Transcription Factor NF-KB Subunit Proteins on NF-KB\u27 s Oncological Activity

Nuclear factor-kappa B (NF-κB) is a transcription factor that becomes functional when any two of its five component proteins (p50, p52, p65, c-Rel, and RelB) join together. NF-κB plays an important role in bringing out cell proliferation, or cell growth. When NF-κB malfunctions and becomes hyperactive, excessive NF-κB activity promotes abnormally high cell growth, which is a symptom of cancer. Because of its tie to cancer, NF-κB is commonly subjected to modification to curb cancer growth. In this project, each component protein of NF- κB was degraded via a method called RNAi to see if it would have any negative influence on NF-κB activity of glioblastoma, or brain cancer, cells. It was found that degradation of p50 and p52 significantly reduced NF-κB activity while the remaining three failed to produce significant reduction

P-13 The Effect of Degrading the Transcription Factor NF-κB Subunit Proteins on NF-κB’s Oncological Activity

Nuclear factor-kappa B (NF-κB) is a transcription factor that becomes functional when any two of its five component proteins (p50, p52, p65, c-Rel, and RelB) join together. Because of its involvement in oncogenesis, NF-κB is commonly subjected to modification to curb cancer growth. In this project, each component protein of NF-κB was degraded using RNAi to see if it would have any influence on glioblastoma, a form of brain cancer. It was found that degradation of p50 and p52 significantly reduced NF-κB activity while the remaining three failed to produce significant reduction

Unsupervised Dialogue Topic Segmentation in Hyperdimensional Space

We present HyperSeg, a hyperdimensional computing (HDC) approach to unsupervised dialogue topic segmentation. HDC is a class of vector symbolic architectures that leverages the probabilistic orthogonality of randomly drawn vectors at extremely high dimensions (typically over 10,000). HDC generates rich token representations through its low-cost initialization of many unrelated vectors. This is especially beneficial in topic segmentation, which often operates as a resource-constrained pre-processing step for downstream transcript understanding tasks. HyperSeg outperforms the current state-of-the-art in 4 out of 5 segmentation benchmarks -- even when baselines are given partial access to the ground truth -- and is 10 times faster on average. We show that HyperSeg also improves downstream summarization accuracy. With HyperSeg, we demonstrate the viability of HDC in a major language task. We open-source HyperSeg to provide a strong baseline for unsupervised topic segmentation.Comment: Interspeech 202

Complementarities in Platform Ecosystems: A Study of Coevolution of Made-in-Korea Digital Entertainment Phenomenon

Business ecosystems are pushed by competition to develop complementarities that increase their chances of survival. However, scholars continue to cite the lack of understanding in coevolution as a complementarity mechanisms of businesses, especially in the digital platform ecosystems. In this research-in-progress paper, we explore the development of complementarities, found in the coevolution of entities in digital platform ecosystems. Through initial case studies of globally developed Korean entertainment and culture industry, we discover a possible categorization of different types of coevolution in the digital platform ecosystem; namely ‘digital transformation’ – business coevolving with its environment, ‘platformization’ – core platform coevolving with its complementors, and ‘reconfiguration’- core ecosystem coevolving with its sub-ecosystems. Based on the findings, we suggest that there is a need to extend the definition of platform ecosystems to also incorporate the sub-ecosystems’ coevolutionary interaction. A new conceptual framework is presented with future plans to develop both the work and the model

The Red Queen Hypothesis and Improvisational Capabilities for Resilience: A Study of Korean Online Gaming Industry

Developing organizational resilience (OR) is now one of the core competencies for organizations’ survival. Yet, OR development, as a response to disruptions, is context specific. With previous studies highlighting the type of disruption addressed, we find that the technology-incurred disruptions have received less spotlight due to the prevailing ‘pro-ICT bias’. However, technology may also heavily disrupt organizations. Should an organization not be resilient towards it, its survival can be at risk. Among various methods and means of developing OR, digital resilience, which is to utilize information systems to develop resilience, is known to be critical. Therefore, we ask the following research question “How do organizations develop digital resilience addressing technology-driven disruptions? . Using the improvisational capabilities and the red queen hypothesis as our guide, we conduct an exploratory case study on the Korean online gaming industry. Preliminary analysis and results are shared and concluded with plans for future research developmen

Rapid and high-capacity MgO composites by salt-controllable precipitation for pre- combustion CO2 capture

Pre-combustion CO2 capture at intermediate temperatures can allow for more flexibility to control over CO2 emission in various industrial processes. For example, the pre-combustion capture can be applied for an Integrated Gasification Combined Cycle (IGCC) due to the use of relatively mild operating temperatures and accessible heat sources. Efficient materials for CO2 capture and H2 production in water gas shift reactor can contribute to improving the overall reliability and efficiency in IGCC process. As a first step, we presented triple salt-promoted MgO composites (NaNaLi salts) by a precipitation method to enhance sorption capacity, rate, and stability. In the conventional precipitation method, a filtration step makes control and reproductivity of the salt composition difficult owing to the unknown residual salts. In this study, we developed a synthesis procedure of precipitation method to control the composition of salts as well as improve physical properties. As-prepared MgO exhibited excellent sorption capacities of 73.0 wt.% at 325 °C in pure CO2 and high sorption rate within 10 min. Stability of composites were evaluated under various gas and time condition and were superior to those of the other MgO-based sorbents reported. With a wet gas mixture (29% CO2, 3% H2O, and balance N2) for sorption and CO2 regeneration, the working capacity stabilized after 20 cycles at 23 and 4.6 wt% for 60/15 min and 10/5 min cycles, respectively. The enhancement and reduction of working capacity along cycles were explained based on liquid phase sintering, i.e., rearrangement, solid-reprecipitation, and densification. However, too long sorption time in the capacity evaluation is not practical because a fixed bed or fluidized bed has a difficulty of temperature control and a large bed size to control high volumes of gases. Therefore, further development is required for an advanced sorbent with high sorption rate and capacity in practical utilization. Therefore, as a second step, a facile method for sorbent with rapid and high-capacity CO2 capture was developed by incorporating additional metal ioninto salt-promoted MgO sorbents using a coprecipitation. At the same fast cycle (10min/5min), the cyclic sorption capacity of 12 wt.% was observed from the developed MgO composite by using wet mixture sorption (29 vol.% CO2, vol.% H2O and N2 balance) and CO2 regeneration. Please click Additional Files below to see the full abstract

Toward a Better Understanding of Loss Functions for Collaborative Filtering

Collaborative filtering (CF) is a pivotal technique in modern recommender systems. The learning process of CF models typically consists of three components: interaction encoder, loss function, and negative sampling. Although many existing studies have proposed various CF models to design sophisticated interaction encoders, recent work shows that simply reformulating the loss functions can achieve significant performance gains. This paper delves into analyzing the relationship among existing loss functions. Our mathematical analysis reveals that the previous loss functions can be interpreted as alignment and uniformity functions: (i) the alignment matches user and item representations, and (ii) the uniformity disperses user and item distributions. Inspired by this analysis, we propose a novel loss function that improves the design of alignment and uniformity considering the unique patterns of datasets called Margin-aware Alignment and Weighted Uniformity (MAWU). The key novelty of MAWU is two-fold: (i) margin-aware alignment (MA) mitigates user/item-specific popularity biases, and (ii) weighted uniformity (WU) adjusts the significance between user and item uniformities to reflect the inherent characteristics of datasets. Extensive experimental results show that MF and LightGCN equipped with MAWU are comparable or superior to state-of-the-art CF models with various loss functions on three public datasets.Comment: Accepted by CIKM 202

Is the Leaderboard Information Useful to Investors? : The Leaderboard Effect in P2P Lending

P2P (Online Peer-to-Peer) lending provides an open marketplace where borrowers make requests for loans by lenders who subsequently decide whether to bid or not following an examination of the relevant information posted by borrowers. In this P2P lending context, the leaderboard, where popular loan requests are displayed at the web’s front page, provides information for lenders to use when evaluating the requests. We empirically examine the effects of leaderboard information regarding the most popular existing loan requests. Our results show that the leaderboard information works ex ante in attracting additional bids to get loan requests successfully financed. However, it does not work ex post in improving the performance so that it has less potential for default