ICU Outcome Predictions Using Real-Time Signals with Wavelet-Transform-based Convolutional Neural Network

Intensive care units (ICUs) serve patients with life-threatening conditions. The limited ICU resources cause severe economic and healthcare burdens worldwide. It is critical to conduct ICU outcome predictions at an early stage and promote efficient use of ICU resources. However, all the current prediction methods have limitations such as unsatisfactory accuracy and depending on resource-demanding laboratory tests or expert domain knowledge. In this research, we design a wavelet-transformed-based convolutional neural network, WTCNN, which only requires patients’ vital sign series and information at ICU admission for real-time ICU outcome predictions. The model is evaluated using a large real-world ICU database and outperforms state-of-art baselines on both ICU mortality and length-of-stay prediction tasks. We conduct LIME for model interpretation and prescriptive analysis. Our work provides an efficient tool for ICU outcome predictions, allowing healthcare providers to take action promptly on patients at risk and reduce the negative impacts on patient outcomes

Prompt, Plan, Perform: LLM-based Humanoid Control via Quantized Imitation Learning

In recent years, reinforcement learning and imitation learning have shown great potential for controlling humanoid robots' motion. However, these methods typically create simulation environments and rewards for specific tasks, resulting in the requirements of multiple policies and limited capabilities for tackling complex and unknown tasks. To overcome these issues, we present a novel approach that combines adversarial imitation learning with large language models (LLMs). This innovative method enables the agent to learn reusable skills with a single policy and solve zero-shot tasks under the guidance of LLMs. In particular, we utilize the LLM as a strategic planner for applying previously learned skills to novel tasks through the comprehension of task-specific prompts. This empowers the robot to perform the specified actions in a sequence. To improve our model, we incorporate codebook-based vector quantization, allowing the agent to generate suitable actions in response to unseen textual commands from LLMs. Furthermore, we design general reward functions that consider the distinct motion features of humanoid robots, ensuring the agent imitates the motion data while maintaining goal orientation without additional guiding direction approaches or policies. To the best of our knowledge, this is the first framework that controls humanoid robots using a single learning policy network and LLM as a planner. Extensive experiments demonstrate that our method exhibits efficient and adaptive ability in complicated motion tasks

Reactivating aberrantly hypermethylated p15 gene in leukemic T cells by a phenylhexyl isothiocyanate mediated inter-active mechanism on DNA and chromatin

<p>Abstract</p> <p>Background</p> <p>We have previously demonstrated that phenylhexyl isothiocyanate (PHI), a synthetic isothiocyanate, inhibits histone deacetylases and remodels chromatins to induce growth arrest in HL-60 myeloid leukemia cells in a concentration-dependent manner.</p> <p>Methods</p> <p>To investigate the effect of PHI, a novel histone deacetylases inhibitor (HDACi), on demethylation and activation of transcription of <it>p15 </it>in acute lymphoid leukemia cell line Molt-4, and to further decipher the potential mechanism of demethylation, DNA sequencing and modified methylation specific PCR (MSP) were used to screen <it>p15</it>-M and <it>p15</it>-U mRNA after Molt-4 cells were treated with PHI, 5-Aza and TSA. DNA methyltransferase 1 (DNMT1), 3A (DNMT3A), 3B (DNMT3B) and <it>p15 </it>mRNA were measured by RT-PCR. P15 protein, acetylated histone H3 and histone H4 were detected by Western Blot.</p> <p>Results</p> <p>The gene <it>p15 </it>in Molt-4 cells was hypermethylated and inactive. Hypermethylation of gene <it>p15 </it>was attenuated and <it>p15 </it>gene was activated de novo after 5 days exposure to PHI in a concentration-dependent manner. DNMT1 and DNMT3B were inhibited by PHI (P < 0.05). Alteration of DNMT3A was not significant at those concentrations. Acetylated histone H3 and histone H4 were accumulated markedly after exposure to PHI.</p> <p>Conclusion</p> <p>PHI could induce both DNA demethylation and acetylated H3 and H4 accumulation in Molt-4 cells. Hypermethylation of gene <it>p15 </it>was reversed and <it>p15 </it>transcription could be reactivated de novo by PHI.</p

Quantifying and Attributing the Hallucination of Large Language Models via Association Analysis

Although demonstrating superb performance on various NLP tasks, large language models (LLMs) still suffer from the hallucination problem, which threatens the reliability of LLMs. To measure the level of hallucination of LLMs, previous works first categorize the hallucination according to the phenomenon similarity, then quantify the proportion that model outputs contain hallucinatory contents. However, such hallucination rates could easily be distorted by confounders. Moreover, such hallucination rates could not reflect the reasons for the hallucination, as similar hallucinatory phenomena may originate from different sources. To address these issues, we propose to combine the hallucination level quantification and hallucination reason investigation through an association analysis, which builds the relationship between the hallucination rate of LLMs with a set of risk factors. In this way, we are able to observe the hallucination level under each value of each risk factor, examining the contribution and statistical significance of each risk factor, meanwhile excluding the confounding effect of other factors. Additionally, by recognizing the risk factors according to a taxonomy of model capability, we reveal a set of potential deficiencies in commonsense memorization, relational reasoning, and instruction following, which may further provide guidance for the pretraining and supervised fine-tuning process of LLMs to mitigate the hallucination

Sp1, Instead of AhR, Regulates the Basal Transcription of Porcine CYP1A1 at the Proximal Promoter

Pigs are commonly used as an animal model to evaluate the toxic effects of exogenous compounds. Cytochrome P450 1A1 (CYP1A1) metabolizes numerous exogenous compounds and is abundantly expressed in the liver, kidneys, and intestines. The high amino acid similarity between human and porcine CYP1A1 indicates that they probably have the same metabolic characteristics. Therefore, understanding the regulatory mechanism of CYP1A1 expression in pigs is particularly important for predicting the toxicology and metabolic kinetics of exogenous chemicals. Currently, the transcriptional regulation of porcine CYP1A1 has rarely been studied, especially regarding basal transcription. In this study, we first confirmed that the key regulatory elements of porcine CYP1A1 basal transactivation are in the proximal promoter region using promoter truncation analysis via a dual luciferase assay in a porcine kidney cell line LLC-PK1. Two overlapping cis-elements, the xenobiotic response element (XRE) and GC box, in this proximal region potentially play key roles in the basal transactivation of porcine CYP1A1. Furthermore, using electrophoretic mobility shift assay and chromatin immunoprecipitation, the GC box binding protein Sp1 was confirmed to bind to the proximal promoter of porcine CYP1A1, instead of AhR, the XRE binding protein. In LLC-PK1 cells, by knocking down either Sp1 or AhR, the expression of porcine CYP1A1 at the mRNA level and protein level was significantly downregulated, suggesting both proteins are important for porcine CYP1A1 expression. However, promoter activity analysis in LLC-PK1 cells treated with an AhR agonist and antagonist confirmed that AhR does not participate in the basal regulation of porcine CYP1A1 at the proximal promoter. In conclusion, our study revealed that the proximal promoter is the key regulatory region for porcine CYP1A1 basal expression. Although AhR plays an important role in the transactivation of porcine CYP1A1 expression, the key determinant transcription factor for its basal transactivation is Sp1 at the proximal promoter of porcine CYP1A1

Impacts of coagulation on the appearance time method for new particle growth rate evaluation and their corrections

The growth rate of atmospheric new particles is a key parameter that determines their survival probability of becoming cloud condensation nuclei and hence their impact on the climate. There have been several methods to estimate the new particle growth rate. However, due to the impact of coagulation and measurement uncertainties, it is still challenging to estimate the initial growth rate of new particles, especially in polluted environments with high background aerosol concentrations. In this study, we explore the influences of coagulation on the appearance time method to estimate the growth rate of sub-3 nm particles. The principle of the appearance time method and the impacts of coagulation on the retrieved growth rate are clarified via derivations. New formulae in both discrete and continuous spaces are proposed to correct for the impacts of coagulation. Aerosol dynamic models are used to test the new formulae. New particle formation in urban Beijing is used to illustrate the importance of considering the impacts of coagulation on the sub-3 nm particle growth rate and its calculation. We show that the conventional appearance time method needs to be corrected when the impacts of coagulation sink, coagulation source, and particle coagulation growth are non-negligible compared to the condensation growth. Under the simulation conditions with a constant concentration of non-volatile vapors, the corrected growth rate agrees with the theoretical growth rates. However, the uncorrected parameters, e.g., vapor evaporation and the variation in vapor concentration, may impact the growth rate obtained with the appearance time method. Under the simulation conditions with a varying vapor concentration, the average bias in the corrected 1.5-3 nm particle growth rate ranges from 6 %-44 %, and the maximum bias in the size-dependent growth rate is 150 %. During the test new particle formation event in urban Beijing, the corrected condensation growth rate of sub-3 nm particles was in accordance with the growth rate contributed by sulfuric acid condensation, whereas the conventional appearance time method overestimated the condensation growth rate of 1.5 nm particles by 80 %.Peer reviewe

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