Pulmonary epithelioid hemangioendothelioma accompanied by bilateral multiple calcified nodules in lung

Pulmonary epithelioid hemangioendothelioma (PEH) is a rare vascular tumor. It can present either as one solitary nodule or bilateral multiple nodules, usually without calcification. We describe here an unusual case of PEH in a 42-year-old female with a 6.0 cm dominant mass along with bilateral multiple calcified small nodules measuring 0.2-1.0 cm in diameter with a 25-year plus followup history. Overall histologic findings of the solitary tumor accorded with conventional PEH. While multiple calcified small nodules were composed predominantly of intra-alveolar homogeneously eosinophilic matrix, and only a few bland small cells were embedded in it. This lesion has never been reported in the literature. After comprehensive analysis of morphology, radiography, histochemistry, immunohistochemistry and differential diagnoses, PEH presenting multiple calcified small nodules was confirmed

Snorkeling

https://digitalcommons.risd.edu/specialcollections_bookcontest9th2023/1003/thumbnail.jp

Nickel pyrithione induces apoptosis in chronic myeloid leukemia cells resistant to imatinib via both Bcr/Abl-dependent and Bcr/Abl-independent mechanisms

Abstract Background Acquired imatinib (IM) resistance is frequently characterized by Bcr-Abl mutations that affect IM binding and kinase inhibition in patients with chronic myelogenous leukemia (CML). Bcr-Abl-T315I mutation is the predominant mechanism of the acquired resistance to IM. Therefore, it is urgent to search for additional approaches and targeting strategies to overcome IM resistance. We recently reported that nickel pyrithione (NiPT) potently inhibits the ubiquitin proteasome system via targeting the 19S proteasome-associated deubiquitinases (UCHL5 and USP14), without effecting on the 20S proteasome. In this present study, we investigated the effect of NiPT, a novel proteasomal deubiquitinase inhibitor, on cell survival or apoptosis in CML cells bearing Bcr-Abl-T315I or wild-type Bcr-Abl. Methods Cell viability was examined by MTS assay and trypan blue exclusion staining assay in KBM5, KBM5R, K562, BaF3-p210-WT, BaF3-p210-T315I cells, and CML patients’ bone marrow samples treated with NiPT. Cell apoptosis in CML cells was detected with Annexin V-FITC/PI and rhodamine-123 staining followed by fluorescence microscopy and flow cytometry and with western blot analyses for apoptosis-associated proteins. Expression levels of Bcr-Abl in CML cells were analyzed by using western blotting and real-time PCR. The 20S proteasome peptidase activity was measured using specific fluorogenic substrate. Active-site-directed labeling of proteasomal DUBs, as well as the phosphorylation of USP14 was used for evaluating the inhibition of the DUBs activity by NiPT. Mouse xenograft models of KBM5 and KBM5R cells were analyzed, and Bcr-Abl-related proteins and protein biomarkers related to proliferation, differentiation, and adhesion in tumor tissues were detected by western blots and/or immunohistological analyses. Results NiPT induced apoptosis in CML cells and inhibited the growth of IM-resistant Bcr-Abl-T315I xenografts in nude mice. Mechanistically, NiPT induced decreases in Bcr-Abl proteins, which were associated with downregulation of Bcr-Abl transcription and with the cleavage of Bcr-Abl protein by activated caspases. NiPT-induced ubiquitin proteasome system inhibition induced caspase activation in both IM-resistant and IM-sensitive CML cells, and the caspase activation was required for NiPT-induced Bcr-Abl downregulation and apoptotic cell death. Conclusions These findings support that NiPT can overcome IM resistance through both Bcr-Abl-dependent and Bcr-Abl-independent mechanisms, providing potentially a new option for CML treatment

Innovation and Financial Disclosure

We examine how financial disclosure policy affects a firm manager's strategy to innovate within a two‐period bandit problem featuring two production methods: an old method with a known probability of success, and a new method with an unknown probability. Exploring the new method in the first period provides the manager with decision‐useful information for the second period, thus creating a real option that is unavailable under exploiting the old known production method. Voluntary disclosure of the firm's financial performance provides the manager with another option to potentially conceal initial failure from the market. The interaction of these two options determines the manager's incentive to explore. In equilibrium, a myopic manager who cares about the interim market price may over‐ or under‐explore compared to the optimal exploration strategy that maximizes firm value. Our analysis shows that firms operating in an environment with voluntary disclosure early in the trial stage and mandated requirement later are most motivated to explore, while firms subject to early mandated disclosure and late voluntary disclosure are least likely to do so. We also provide empirical predictions about the link between the disclosure environment and the intensity and efficiency of corporate innovation

PreFLMR: Scaling Up Fine-Grained Late-Interaction Multi-modal Retrievers

Large Multimodal Models (LMMs) excel in natural language and visual understanding but are challenged by exacting tasks such as Knowledge-based Visual Question Answering (KB-VQA) which involve the retrieval of relevant information from document collections to use in shaping answers to questions. We present an extensive training and evaluation framework, M2KR, for KB-VQA. M2KR contains a collection of vision and language tasks which we have incorporated into a single suite of benchmark tasks for training and evaluating general-purpose multi-modal retrievers. We use M2KR to develop PreFLMR, a pre-trained version of the recently developed Fine-grained Late-interaction Multi-modal Retriever (FLMR) approach to KB-VQA, and we report new state-of-the-art results across a range of tasks. We also present investigations into the scaling behaviors of PreFLMR intended to be useful in future developments in general-purpose multi-modal retrievers.Comment: ACL 2024; Project page: https://preflmr.github.io

Control-DAG: Constrained Decoding for Non-Autoregressive Directed Acyclic T5 using Weighted Finite State Automata

The Directed Acyclic Transformer is a fast non-autoregressive (NAR) model that performs well in Neural Machine Translation. Two issues prevent its application to general Natural Language Generation (NLG) tasks: frequent Out-Of-Vocabulary (OOV) errors and the inability to faithfully generate entity names. We introduce Control-DAG, a constrained decoding algorithm for our Directed Acyclic T5 (DA-T5) model which offers lexical, vocabulary and length control. We show that Control-DAG significantly enhances DA-T5 on the Schema Guided Dialogue and the DART datasets, establishing strong NAR results for Task-Oriented Dialogue and Data-to-Text NLG.Comment: 11 pages. NAACL 202

Direct Preference Optimization for Neural Machine Translation with Minimum Bayes Risk Decoding

Minimum Bayes Risk (MBR) decoding can significantly improve translation performance of Multilingual Large Language Models (MLLMs). However, MBR decoding is computationally expensive and in this paper, we show how recently developed Reinforcement Learning (RL) technique, Direct Preference Optimization (DPO) can be used to fine-tune MLLMs so that we get the gains from MBR without the additional computation in inference. Our fine-tuned models have significantly improved performance on multiple NMT test sets compared to base MLLMs without preference optimization. Our method boosts the translation performance of MLLMs using relatively small monolingual fine-tuning sets

嶺南大學實測圖

此版為黑底白字的複印本，全書共27頁。內容為廣州嶺南大學的手繪地圖，地圖分成25頁。https://commons.ln.edu.hk/lingnan_history_bks/1041/thumbnail.jp

Improving hateful memes detection via learning hatefulness-aware embedding space through retrieval-guided contrastive learning

Hateful memes have emerged as a significant concern on the Internet. These memes, which are a combination of image and text, often convey messages vastly different from their individual meanings. Thus, detecting hateful memes requires the system to jointly understand the visual and textual modalities. However, our investigation reveals that the embedding space of existing CLIP-based systems lacks sensitivity to subtle differences in memes that are vital for correct hatefulness classification. To address this issue, we propose constructing a hatefulness-aware embedding space through retrieval-guided contrastive training. Specifically, we add an auxiliary loss that utilizes hard negative and pseudo-gold samples to train the embedding space. Our approach achieves state-of-the-art performance on the HatefulMemes dataset with an AUROC of 86.7. Notably, our approach outperforms much larger fine-tuned Large Multimodal Models like Flamingo and LLaVA. Finally, we demonstrate a retrieval-based hateful memes detection system, which is capable of making hatefulness classification based on data unseen in training from a database. This allows developers to update the hateful memes detection system by simply adding new data without retraining, a desirable feature for real services in the constantly-evolving landscape of hateful memes on the Internet

Fine-grained Late-interaction Multi-modal Retrieval for Retrieval Augmented Visual Question Answering

Knowledge-based Visual Question Answering (KB-VQA) requires VQA systems to utilize knowledge from external knowledge bases to answer visually-grounded questions. Retrieval-Augmented Visual Question Answering (RA-VQA), a strong framework to tackle KB-VQA, first retrieves related documents with Dense Passage Retrieval (DPR) and then uses them to answer questions. This paper proposes Fine-grained Late-interaction Multi-modal Retrieval (FLMR) which significantly improves knowledge retrieval in RA-VQA. FLMR addresses two major limitations in RA-VQA's retriever: (1) the image representations obtained via image-to-text transforms can be incomplete and inaccurate and (2) relevance scores between queries and documents are computed with one-dimensional embeddings, which can be insensitive to finer-grained relevance. FLMR overcomes these limitations by obtaining image representations that complement those from the image-to-text transforms using a vision model aligned with an existing text-based retriever through a simple alignment network. FLMR also encodes images and questions using multi-dimensional embeddings to capture finer-grained relevance between queries and documents. FLMR significantly improves the original RA-VQA retriever's PRRecall@5 by approximately 8\%. Finally, we equipped RA-VQA with two state-of-the-art large multi-modal/language models to achieve $\sim61\%$ VQA score in the OK-VQA dataset.Comment: To appear at NeurIPS 2023. This is the camera-ready version. We fixed some numbers and added more experiments to address reviewers' comment