FunCodec: A Fundamental, Reproducible and Integrable Open-source Toolkit for Neural Speech Codec

This paper presents FunCodec, a fundamental neural speech codec toolkit, which is an extension of the open-source speech processing toolkit FunASR. FunCodec provides reproducible training recipes and inference scripts for the latest neural speech codec models, such as SoundStream and Encodec. Thanks to the unified design with FunASR, FunCodec can be easily integrated into downstream tasks, such as speech recognition. Along with FunCodec, pre-trained models are also provided, which can be used for academic or generalized purposes. Based on the toolkit, we further propose the frequency-domain codec models, FreqCodec, which can achieve comparable speech quality with much lower computation and parameter complexity. Experimental results show that, under the same compression ratio, FunCodec can achieve better reconstruction quality compared with other toolkits and released models. We also demonstrate that the pre-trained models are suitable for downstream tasks, including automatic speech recognition and personalized text-to-speech synthesis. This toolkit is publicly available at https://github.com/alibaba-damo-academy/FunCodec.Comment: 5 pages, 3 figures, submitted to ICASSP 202

The current status of tumor microenvironment and cancer stem cells in sorafenib resistance of hepatocellular carcinoma

Hepatocellular carcinoma (HCC) is a heterogeneous and aggressive liver cancer that presents limited treatment options. Despite being the standard therapy for advanced HCC, sorafenib frequently encounters resistance, emphasizing the need to uncover the underlying mechanisms and develop effective treatments. This comprehensive review highlights the crucial interplay between the tumor microenvironment, cancer stem cells (CSCs), and epithelial-mesenchymal transition (EMT) in the context of sorafenib resistance. The tumor microenvironment, encompassing hypoxia, immune cells, stromal cells, and exosomes, exerts a significant impact on HCC progression and therapy response. Hypoxic conditions and immune cell infiltration create an immunosuppressive milieu, shielding tumor cells from immune surveillance and hindering therapeutic efficacy. Additionally, the presence of CSCs emerges as a prominent contributor to sorafenib resistance, with CD133+ CSCs implicated in drug resistance and tumor initiation. Moreover, CSCs undergo EMT, a process intimately linked to tumor progression, CSC activation, and further promotion of sorafenib resistance, metastasis, and tumor-initiating capacity. Elucidating the correlation between the tumor microenvironment, CSCs, and sorafenib resistance holds paramount importance in the quest to develop reliable biomarkers capable of predicting therapeutic response. Novel therapeutic strategies must consider the influence of the tumor microenvironment and CSC activation to effectively overcome sorafenib resistance in HCC

Multimodal N-of-1 trials: A Novel Personalized Healthcare Design

N-of-1 trials aim to estimate treatment effects on the individual level and can be applied to personalize a wide range of physical and digital interventions in mHealth. In this study, we propose and apply a framework for multimodal N-of-1 trials in order to allow the inclusion of health outcomes assessed through images, audio or videos. We illustrate the framework in a series of N-of-1 trials that investigate the effect of acne creams on acne severity assessed through pictures. For the analysis, we compare an expert-based manual labelling approach with different deep learning-based pipelines where in a first step, we train and fine-tune convolutional neural networks (CNN) on the images. Then, we use a linear mixed model on the scores obtained in the first step in order to test the effectiveness of the treatment. The results show that the CNN-based test on the images provides a similar conclusion as tests based on manual expert ratings of the images, and identifies a treatment effect in one individual. This illustrates that multimodal N-of-1 trials can provide a powerful way to identify individual treatment effects and can enable large-scale studies of a large variety of health outcomes that can be actively and passively assessed using technological advances in order to personalized health interventions

Mapping Observations of Peptide-like molecules around Sagittarius B2

Peptide-like molecule, which has a close connection with the origin of life, has been detected in universe. Mapping observations of HCONH$_2$ and CH$_3$CONH$_2$, two simplest peptide-like molecules, are performed towards Sagittarius B2 (Sgr B2) complex with the IRAM 30m telescope. Seven transitions of HCONH$_2$ and five transitions of CH$_3$CONH$_2$ are used in analysis. The spatial distribution of excitation temperature and column density of HCONH$_2$ in the molecular envelope of Sgr B2 are obtained by the rotation diagrams. Assuming the same excitation temperature as HCONH$_2$, the column densities of CH$_3$CONH$_2$ are also calculated. The results show that excitation temperature ranges from 6 K to 46 K in the molecular envelope of Sgr B2. The abundance ratio between HCONH$_2$ and CH$_3$CONH$_2$ are calculated to explore the relationship among them, as well as HNCO mentioned in our pervious research. The abundance ratio of CH$_3$CONH$_2$/HCONH$_2$ varies from 10% to 20%, while that of HCONH$_2$/HNCO ranges from 1.5% to 10%. CH$_3$CONH$_2$ is enhanced with respect to HCONH$_2$ in the northwest region of Sgr B2. One transition of H$^{13}$CONH$_2$ is detected toward 12 positions of Sgr B2, from which a $^{12}$C/$^{13}$C ratio of 28.7 is obtained. A time-dependent chemical model with a short duration of X-ray burst is used to explain the observed abundances of HCONH$_2$ and CH$_3$CONH$_2$, with the best fitting result at T$\rm_{dust}$ = 53-56 K. More chemical reactions are required to be included into the model since the modeled abundance is lower than the observed one at the observed T$\rm_{dust}$

FLM-101B: An Open LLM and How to Train It with $100K Budget

Large language models (LLMs) have achieved remarkable success in NLP and multimodal tasks, among others. Despite these successes, two main challenges remain in developing LLMs: (i) high computational cost, and (ii) fair and objective evaluations. In this paper, we report a solution to significantly reduce LLM training cost through a growth strategy. We demonstrate that a 101B-parameter LLM with 0.31T tokens can be trained with a budget of 100K US dollars. Inspired by IQ tests, we also consolidate an additional range of evaluations on top of existing evaluations that focus on knowledge-oriented abilities. These IQ evaluations include symbolic mapping, rule understanding, pattern mining, and anti-interference. Such evaluations minimize the potential impact of memorization. Experimental results show that our model, named FLM-101B, trained with a budget of 100K US dollars, achieves performance comparable to powerful and well-known models, e.g., GPT-3 and GLM-130B, especially on the additional range of IQ evaluations. The checkpoint of FLM-101B is released at https://huggingface.co/CofeAI/FLM-101B

Genome Characterization and Potential Risk Assessment of the Novel SARS-CoV-2 Variant Omicron (B.1.1.529)

As the novel coronavirus SARS-CoV-2 spread around the world, multiple waves of variants emerged, thus leading to local or global population shifts during the pandemic. A new variant named Omicron (PANGO lineage B.1.1.529), which was first discovered in southern Africa, has recently been proposed by the World Health Organization to be a Variant of Concern. This variant carries an unusually large number of mutations, particularly on the spike protein and receptor binding domain, in contrast to other known major variants. Some mutation sites are associated with enhanced viral transmission, infectivity, and pathogenicity, thus enabling the virus to evade the immune protective barrier. Given that the emergence of the Omicron variant was accompanied by a sharp increase in infection cases in South Africa, the variant has the potential to trigger a new global epidemic peak. Therefore, continual attention and a rapid response are required to decrease the possible risks to public health

Case report: Gene mutation analysis and skin imaging of isolated café-au-lait macules

Background: Café-au-lait macules (CALMs) are common birthmarks associated with several genetic syndromes, such as neurofibromatosis type 1 (NF1). Isolated CALMs are defined as multiple café-au-lait macules in patients without any other sign of NF1. Typical CALMs can have predictive significance for NF1, and non-invasive techniques can provide more accurate results for judging whether café-au-lait spots are typical.Objectives: The study aimed to investigate gene mutations in six Chinese Han pedigrees of isolated CALMs and summarize the characteristics of CALMs under dermoscopy and reflectance confocal microscopy (RCM).Methods: In this study, we used Sanger sequencing to test for genetic mutations in six families and whole exome sequencing (WES) in two families. We used dermoscopy and RCM to describe the imaging characteristics of CALMs.Results: In this study, we tested six families for genetic mutations, and two mutations were identified as novel mutations. The first family identified [NC_000017.11(NM_001042492.2):c.7355G>A]. The second family identified [NC_000017.11(NM_001042492.2):c.2739_2740del]. According to genotype-phenotype correlation analyses, proband with frameshift mutation tended to have a larger number of CALMs and a higher rate of having atypical CALMs. Dermoscopy showed uniform and consistent tan-pigmented network patches with poorly defined margins with a lighter color around the hair follicles. Under RCM, the appearance of NF1 comprised the increased pigment granules in the basal layer and significantly increased refraction.Conclusion: A new heterozygous mutation and a new frameshift mutation of NF1 were reported. This article can assist in summarizing the properties of dermoscopy and RCM with CALMs