SpeechGen: Unlocking the Generative Power of Speech Language Models with Prompts

Large language models (LLMs) have gained considerable attention for Artificial Intelligence Generated Content (AIGC), particularly with the emergence of ChatGPT. However, the direct adaptation of continuous speech to LLMs that process discrete tokens remains an unsolved challenge, hindering the application of LLMs for speech generation. The advanced speech LMs are in the corner, as that speech signals encapsulate a wealth of information, including speaker and emotion, beyond textual data alone. Prompt tuning has demonstrated notable gains in parameter efficiency and competitive performance on some speech classification tasks. However, the extent to which prompts can effectively elicit generation tasks from speech LMs remains an open question. In this paper, we present pioneering research that explores the application of prompt tuning to stimulate speech LMs for various generation tasks, within a unified framework called SpeechGen, with around 10M trainable parameters. The proposed unified framework holds great promise for efficiency and effectiveness, particularly with the imminent arrival of advanced speech LMs, which will significantly enhance the capabilities of the framework. The code and demos of SpeechGen will be available on the project website: \url{https://ga642381.github.io/SpeechPrompt/speechgen}Comment: Work in progress. The first three authors contributed equall

Fabrication and Encapsulation of a Short‐Period Wire Grid Polarizer with Improved Viewing Angle by the Angled‐Evaporation Method

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/101761/1/adom201300276-sup-0001-S1.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/101761/2/adom201300276.pd

MicroRNA Expression Profiling Altered by Variant Dosage of Radiation Exposure

[[abstract]]Various biological effects are associated with radiation exposure. Irradiated cells may elevate the risk for genetic instability, mutation, and cancer under low levels of radiation exposure, in addition to being able to extend the postradiation side effects in normal tissues. Radiation-induced bystander effect (RIBE) is the focus of rigorous research as it may promote the development of cancer even at low radiation doses. Alterations in the DNA sequence could not explain these biological effects of radiation and it is thought that epigenetics factors may be involved. Indeed, some microRNAs (or miRNAs) have been found to correlate radiation-induced damages and may be potential biomarkers for the various biological effects caused by different levels of radiation exposure. However, the regulatory role that miRNA plays in this aspect remains elusive. In this study, we profiled the expression changes in miRNA under fractionated radiation exposure in human peripheral blood mononuclear cells. By utilizing publicly available microRNA knowledge bases and performing cross validations with our previous gene expression profiling under the same radiation condition, we identified various miRNA-gene interactions specific to different doses of radiation treatment, providing new insights for the molecular underpinnings of radiation injury.[[notice]]補正完畢[[incitationindex]]SCI[[incitationindex]]EI[[booktype]]電子

Gene Expression Profiling of Biological Pathway Alterations by Radiation Exposure

[[abstract]]Though damage caused by radiation has been the focus of rigorous research, the mechanisms through which radiation exerts harmful effects on cells are complex and not well-understood. In particular, the influence of low dose radiation exposure on the regulation of genes and pathways remains unclear. In an attempt to investigate the molecular alterations induced by varying doses of radiation, a genome-wide expression analysis was conducted. Peripheral blood mononuclear cells were collected from five participants and each sample was subjected to 0.5 Gy, 1 Gy, 2.5 Gy, and 5 Gy of cobalt 60 radiation, followed by array-based expression profiling. Gene set enrichment analysis indicated that the immune system and cancer development pathways appeared to be the major affected targets by radiation exposure. Therefore, 1 Gy radioactive exposure seemed to be a critical threshold dosage. In fact, after 1 Gy radiation exposure, expression levels of several genes including FADD, TNFRSF10B, TNFRSF8, TNFRSF10A, TNFSF10, TNFSF8, CASP1, and CASP4 that are associated with carcinogenesis and metabolic disorders showed significant alterations. Our results suggest that exposure to low-dose radiation may elicit changes in metabolic and immune pathways, potentially increasing the risk of immune dysfunctions and metabolic disorders.[[notice]]補正完畢[[incitationindex]]SCI[[incitationindex]]EI[[booktype]]電子

High levels of serum macrophage migration inhibitory factor and interleukin 10 are associated with a rapidly fatal outcome in patients with severe sepsis

SummaryObjectivesThe aim of this study was to delineate the association between high macrophage migration inhibitory factor (MIF) and interleukin 10 (IL-10) levels in the early phase of sepsis and rapidly fatal outcome.MethodsOne hundred and fifty-three adult subjects with the main diagnosis of severe sepsis (including septic shock) admitted directly from the emergency department of two tertiary medical centers and one regional teaching hospital between January 2009 and December 2011, were included prospectively. MIF and IL-10 levels were measured and outcomes were analyzed by Cox regression analysis according to the following outcomes: rapidly fatal outcome (RFO, death within 48h), late fatal outcome (LFO, death between 48h and 28 days), and survival at 28 days.ResultsAmong the three outcome groups, IL-10 levels were significantly higher in the RFO group (p < 0.001) and no significant differences were seen between the LFO and survivor groups. After Cox regression analysis, each incremental elevation of 1000 pg/ml in both IL-10 and MIF was independently associated with RFO in patients with severe sepsis. Each incremental elevation of 1000 pg/ml in IL-10 increased the RFO risk by a factor of 1.312 (95% confidence interval 1.094–1.575; p=0.003); this was the most significant factor leading to RFO in patients with severe sepsis.ConclusionsPatients with RFO exhibited simultaneously high MIF and IL-10 levels in the early phase of severe sepsis. Incremental increases in both IL-10 and MIF levels were associated with RFO in this patient group, and of the two, IL-10 was the most significant factor linked to RFO