Nucleolar Proteins are Essential for Ribosome Biogenesis and Nuclear Homeostasis in Drosophila melanogaster

Four nucleostemin-like proteins (NS1-4) were identified previously in Drosophila melanogaster. NS1 and NS2 are nucleolar proteins, while NS3 and NS4 are cytoplasmic. Here we show that NS2 is homologous to the human nucleostemin-like protein, Ngp1, and that endogenous NS2 and exogenous GFP-NS2 enriches within peripheral regions of nucleoli in larval polyploid midgut cells. Like NS1, depletion of NS2 in midgut cells blocked the release of the 60S ribosomal subunit as detected by the accumulation GFP-RpL11 within nucleoli, and this likely led to a general loss of 60S subunit proteins as shown by immunoblot analyses. At the ultrastructural level, nucleoli in midgut cells depleted of NS2 displayed enlarged nucleolar granular components (GCs) not only on the nucleolar periphery, but interspersed within dense fibrillar components (DFCs). Depletion of NS2 also caused nucleolar stress: larval midgut cells displayed prominent autophagy where mitochondria were found enclosed by isolation membranes derived from the rER. Larval imaginal wing disc cells depleted of NS2 responded by inducing apoptosis as marked by anti-caspase 3 labeling; loss of these cells resulted in defective adult wings. We conclude that nucleolar proteins NS1 and NS2 have similar but non-overlapping roles in the final maturation or nucleolar release of 60S ribosomal subunits. Nopp140, the nucleolar and Cajal body (CB) phosphoprotein of 140 kDa, is considered involved in several steps of ribosome biogenesis. In Drosophila, loss of Nopp140 causes redistribution of fibrillarin, the C/D small nucleolar ribonucleoprotein (snoRNP) methyltransferase; correspondingly, the 2’-O-methylation of ribosomal RNA (rRNA) was reduced. Nopp140 knockout also leads to a loss of cytoplasmic ribosomes and a significant drop in protein translation. Here we show that coilin, an essential component of CB, redistributed in the Nopp140-/- cells by immunofluorescence microscopy. Both immunofluorescence microscopy and Western blot analyses showed that the coilin protein level was elevated, and RT-PCR supported the results by showing coilin mRNA increased approximately two fold in Nopp140-/- larvae. NS1 and NS2 were also found to redistribute from the nucleolus upon Nopp140 gene knockout. In summary, the phenotypes described in Chapter 3 indicate that Nopp140 participates in several aspects of ribosome biogenesis, and it is essential to maintain nuclear homeostasis

MAXIMIZING THE SPEED OF INFLUENCE IN SOCIAL NETWORKS

Influence maximization in social networks is the problem of selecting a limited size of influential users as seed nodes so that the influence from these seed nodes can propagate to the largest number of other nodes in the network. Previous studies in influence maximization focused on three areas, i.e., designing propagation models, improving algorithms of seed-node selection and exploiting the structure of social networks. However, most of these studies ignored the time constraint in influence propagation. In this paper, I studied how to maximize influence propagation in a given time, i.e., maximizing the speed of influence propagation in social networks. I extended the classic Independent Cascade (IC) model to a Continuous Dynamic Extended Independent Cascade (CDE-IC) model. In addition, I propose a novel heuristic algorithm and evaluate the algorithm using two large academic collaboration data sets from www.arXiv.org. Comparing with previous classic heuristic algorithms on the CDE-IC model, the new algorithm is 9%-18% faster in influence propagation. Furthermore, I gave solution to calculate propagation probability between adjacent nodes by exploiting the structure of social networks

THE IMPACT OF PRODUCT PHOTO ON ONLINE CONSUMER PURCHASE INTENTION: AN IMAGE-PROCESSING ENABLED EMPIRICAL STUDY

Determinants of online consumer’s purchase decisions are of long-term interest to researchers and practitioners. Since product photos directly aid consumers’ understanding of products, retailers often put a lot of effort into polishing them. However, there is limited research on the impact of product photos on purchase decisions. Most previous studies took an experiment-based approach, which delivered strict theories on some aspects of product photos. This research takes advantage of image-processing techniques to study product photos’ impact. These techniques allow us to investigate a large set of photo characteristics simultaneously in an empirical study. To rule out possible confounding factors, we collect a dataset from a social shopping Website, which has a simple interface allowing users to judge products mainly based on their photos. We examine product photo characteristics from the aspects of information, emotion, aesthetics, and social presence. We found that consumers prefer product photos with a larger key object, lower entropy on key objects, a warmer color, a higher contrast, a higher depth-of-field, and more social presences. This research introduces a Big Data-based approach to study the impact of e-commerce systems’ visual features on consumers

Augmenting Black-box LLMs with Medical Textbooks for Clinical Question Answering

Large-scale language models (LLMs), such as ChatGPT, are capable of generating human-like responses for various downstream tasks, such as task-oriented dialogues and question answering. However, applying LLMs to medical domains remains challenging due to their inability to leverage domain-specific knowledge. In this study, we present the Large-scale Language Models Augmented with Medical Textbooks (LLM-AMT), which integrates authoritative medical textbooks as the cornerstone of its design, enhancing its proficiency in the specialized domain through plug-and-play modules, comprised of a Hybrid Textbook Retriever, supplemented by the Query Augmenter and the LLM Reader. Experimental evaluation on three open-domain medical question-answering tasks reveals a substantial enhancement in both the professionalism and accuracy of the LLM responses when utilizing LLM-AMT, exhibiting an improvement ranging from 11.4% to 13.2%. Despite being 100 times smaller, we found that medical textbooks as the retrieval corpus serves as a more valuable external knowledge source than Wikipedia in the medical domain. Our experiments show that textbook augmentation results in a performance improvement ranging from 9.7% to 12.2% over Wikipedia augmentation

YZR-net : Self-supervised Hidden representations Invariant to Transformations for profanity detection

On current {\it e-}learning platforms, live classes are an important tool that provides students with an opportunity to get more involved while learning new concepts. In such classes, the element of interaction with teachers and fellow peers helps in removing learning silos and gives each student a chance to experience some aspects relevant to offline learning in this era of virtual classes. One common way of interaction in a class is through the chats / messaging framework, where the teacher can broadcast messages as well as get instant feedback from the students in the live class. This freedom of interaction is a crucial aspect for any student's learning growth but misuse of it can have serious repercussions. Some miscreants use this framework to send profane messages which can have a negative impact on other students as well as the teacher of the class. These rare but high impact situations obviate the need for automatic detection mechanisms that prevent the posting of such chats on any platform. In this work we develop YZR-Net which is a self-supervised framework that is able to robustly detect profane words used in a chat even if the student tries to add clever modifications to fool the system. The matching mechanism on token / word level allows us to maintain a compact as well as dynamic profane vocabulary which can be updated without retraining the underlying model. Our profanity detection framework is language independent and can handle abuses in both English as well as its transliterated counterpart Hinglish (Hindi language words written in English)

Impact of the covid-19 on China’s economy

What is the impact of the COVID-19 on China’s economy? After using the linear regression to analyze the data of GDP, national fiscal revenue and the added value of nine major industries, we find that the COVID-19 had a great impact on China’s economy in 2020 but not in 2021. Then, we use the principal component analysis to reduce the dimension of data and define a new variable to represent China’s economy, which is called “Chinese economic quantity”. It can well describe China’s economy, which has the same function as GDP. By comparing its changes in different years, it can verify the influence on China’s economy. Finally, some suggestions and advices will be given