Lotka-Volterra Models for Extraterrestrial Self-Replicating Probes

A sufficiently advanced extraterrestrial civilization can send out a swarm of self-replicating probes for space exploration. Given the fast-growing number of such a probe, even if there is only one extraterrestrial civilization sending out such probes in the Milky Way galaxy, we should still expect to see them. The fact that we do not consists part of the Fermi paradox. The suggestion that self-replicating probes will eventually mutate to consume their progenitors and therefore significantly reduce the number of total probes has been investigated and dismissed in the literature. In this work, we re-visit this question with a more realistic Lotka-Volterra model, and show that mutated probes would drive the progenitor probes into "extinction", thereby replacing them to spread throughout the galaxy. Thus, the efficiency of mutated probes in reducing the total number of self-replicating probes is even less than previously thought. As part of the analysis, we also suggest that, somewhat counter-intuitively, in designing self-replicating probes, one should not program them to stop replicating when sufficient mutation causes the probes to fail to recognize the progenitor probes as "self".Comment: Revised version to appear in EPJ Plu

Automatic Generation of Electronic Medical Record Based on GPT2 Model

Writing Electronic Medical Records (EMR) as one of daily major tasks of doctors, consumes a lot of time and effort from doctors. This paper reports our efforts to generate electronic medical records using the language model. Through the training of massive real-world EMR data, the CMedGPT2 model provided by us can achieve the ideal Chinese electronic medical record generation. The experimental results prove that the generated electronic medical record text can be applied to the auxiliary medical record work to reduce the burden on the compose and provide a fast and accurate reference for composing work

An Word2vec based on Chinese Medical Knowledge

Introducing a large amount of external prior domain knowledge will effectively improve the performance of the word embedded language model in downstream NLP tasks. Based on this assumption, we collect and collate a medical corpus data with about 36M (Million) characters and use the data of CCKS2019 as the test set to carry out multiple classifications and named entity recognition (NER) tasks with the generated word and character vectors. Compared with the results of BERT, our models obtained the ideal performance and efficiency results

Disease Diagnosis Prediction of EMR Based on BiGRL-Att-CapsNetwork Model

Electronic Medical Records (EMR) carry a large number of diseases characteristics, history and other specific details of patients, which has great value for medical diagnosis. These data with diagnostic labels can help automated diagnostic assistant to predict disease diagnosis and provide a rapid diagnostic reference for doctors. In this study, we designed a BiGRU-Att-CapsNetwork model based on our proposed CMedBERT Chinese medical domain pre-trained language model to predict disease diagnosis in Chinese EMR. In the wide-ranging comparative experiments involving a real EMR dataset (SAHSU) and an academic evaluation task dataset (CCKS 2019), our model obtained competitive performance

A Joint Model of Clinical Domain Classification and Slot Filling Based on RCNN and BiGRU-CRF

The task of the Intent Classification & Slot Filling serves as a key joint task in the voice assistant, which also plays the role of the pre-work in the construction of the medical consultation assistant system. How to distribute a doctor-patient conversation into a formatted electronic medical record to an accurate department (Intent Classification) to extract the key named entities or mentions (Slot Filling) through a specialized domain knowledge recognizer is one of the key steps of the entire system. In real cases, the medical vocabulary and clinical entities in different departments of the hospital often differ to some extent. Therefore, we propose a comprehensive model based on CMed-BERT, RCNN and BiGRU-CRF for a joint task of department identification and slot filling of the specific domain. Experimental results confirmed the competitiveness of our model

Autophagy-dependent ferroptosis in kidney disease

Ferroptosis is a new type of cell death caused by the lack of glutathione peroxidase 4 (GPX4) and the imbalance of cellular redox. It is characterized by the accumulation of lipid peroxides on cell membranes. Multiple regulatory pathways of ferroptosis include the GPX4, glutamate-cystine antiporter (System Xc–), lipid metabolism, and iron metabolism pathways. Recent studies have reported that autophagy-dependent ferroptosis (ferroptosis meditated by ferritinophagy, lipophagy, and clockophagy) plays a significant role in the occurrence of several diseases, including diseases affecting the nerves, liver, lungs, and kidneys. This review provides an overview of research progress made on autophagy-dependent ferroptosis in kidney diseases

Distribution-Aware Continual Test Time Adaptation for Semantic Segmentation

Since autonomous driving systems usually face dynamic and ever-changing environments, continual test-time adaptation (CTTA) has been proposed as a strategy for transferring deployed models to continually changing target domains. However, the pursuit of long-term adaptation often introduces catastrophic forgetting and error accumulation problems, which impede the practical implementation of CTTA in the real world. Recently, existing CTTA methods mainly focus on utilizing a majority of parameters to fit target domain knowledge through self-training. Unfortunately, these approaches often amplify the challenge of error accumulation due to noisy pseudo-labels, and pose practical limitations stemming from the heavy computational costs associated with entire model updates. In this paper, we propose a distribution-aware tuning (DAT) method to make the semantic segmentation CTTA efficient and practical in real-world applications. DAT adaptively selects and updates two small groups of trainable parameters based on data distribution during the continual adaptation process, including domain-specific parameters (DSP) and task-relevant parameters (TRP). Specifically, DSP exhibits sensitivity to outputs with substantial distribution shifts, effectively mitigating the problem of error accumulation. In contrast, TRP are allocated to positions that are responsive to outputs with minor distribution shifts, which are fine-tuned to avoid the catastrophic forgetting problem. In addition, since CTTA is a temporal task, we introduce the Parameter Accumulation Update (PAU) strategy to collect the updated DSP and TRP in target domain sequences. We conduct extensive experiments on two widely-used semantic segmentation CTTA benchmarks, achieving promising performance compared to previous state-of-the-art methods

Engineering Colloidal Metal-Semiconductor Nanorods Hybrid Nanostructures for Photocatalysis

Comprehensive Summary Emerging engineering strategies of colloidal metal-semiconductor nanorod hybrid nanostructures spanning from type, size, dimension, and location of both metal nanoparticles and semiconductors, co-catalyst, band gap structure, surface ligand to hole scavenger are elaborated symmetrically to rationalize the design of this type of intriguing materials for efficient photocatalytic applications. This article is protected by copyright. All rights reserved

Associations between composite dietary antioxidant index and estimated 10-year atherosclerotic cardiovascular disease risk among U.S. adults

BackgroundAtherosclerotic cardiovascular disease (ASCVD) remains the leading cause of death and disability both in U.S. and worldwide. Antioxidants have been proved critical in mitigating the development of atherosclerosis. This study aimed to investigate the associations between composite dietary antioxidant index (CDAI) and estimated 10-year ASCVD risk among U.S. adults.MethodsData extracted from the National Health and Nutrition Examination Survey were analyzed. A total of 10,984 adults aged 18 years and above were included in this study. CDAI was calculated based on the dietary intake reported in their 24-h recall interviews. The estimated 10-year ASCVD risk was calculated via Pooled Cohort Equations (PCE).ResultsAfter adjusting potential confounders, it was indicated that CDAI score was negatively correlated with 10-year ASCVD risk (OR 0.97, 95% CI 0.95–0.99). Stratify CDAI score by quartile, results showed that participants in the second, third, and fourth quartiles had lower ASCVD odds ratio (Q2: OR 0.87, 95% CI 0.69–1.09; Q3: OR 0.78, 95% CI 0.62–0.98; Q4: OR 0.74, 95% CI 0.59–0.94) than those in the first quartile (Q1, lowest CDAI score group), which was confirmed by the trend test as well (p < 0.05). Subgroup analyses stratified by sex, age, race/ethnicity, and smoking status did not show significant effect modification.ConclusionHigher dietary antioxidants intake is associated with lower ASCVD risk among U.S. adults, for which policymakers and healthcare professionals may consider increasing the consumption of antioxidant-rich foods as a preventive strategy for ASCVD