Cell Signaling of Caenorhabditis elegans in Response to Enterotoxigenic Escherichia coli Infection and Lactobacillus zeae Protection

Enterotoxigenic Escherichia coli (ETEC) infection causes the death of Caenorhabditis elegans, which can be prevented by certain Lactobacillus isolates. The host response of C. elegans to ETEC infection and its regulation by the isolates are, however, largely unclear. This study has revealed that, in agreement with the results of life-span assays, the expression of the genes encoding p38 mitogen-activated protein kinase (MAPK) pathway (nsy-1, sek-1, and pmk-1), insulin/insulin-like growth factor (DAF/IGF) pathway (daf-16), or antimicrobial peptides (lys-7, spp-1, and abf-3) and other defensing molecules (abf-2, clec-85) was upregulated significantly when the wild-type nematode (N2) was subjected to ETEC infection. This upregulation was further enhanced by the pretreatment with Lactobacillus zeae LB1, but not with L. casei CL11. Mutants defective in the cell signaling of C. elegans were either more susceptible (defective in NSY-1, SEK-1, PMK-1, or DAF16) or more resistant (defective in AGE-1, DBL-1, SKN-1, or SOD-3) to ETEC infection compared with the wild-type. Mutants defective in antimicrobial peptides (LYS-7, SPP1, or ABF-3) were also more susceptible. In addition, mutants that are defective in NSY-1, SEK-1, PMK-1, DAF16, ABF-3, LYS-7, or SPP1 showed no response to the protection from L. zeae LB1. The expression of the genes encoding antimicrobial peptides (lys-7, spp-1, and abf-3) and other defensing molecules (abf-2, clec-60, and clec-85) were almost all upregulated in AGE-1- or DBL-1-defective mutant compared with the wild-type, which was further enhanced by the pretreatment of L. zeae LB1. The expression of these genes was, however, mostly downregulated in NSY-1- or DAF-16-defective mutant. These results suggest that L. zeae LB1 regulates C. elegans signaling through the p38 MAPK and DAF/IGF pathways to control the production of antimicrobial peptides and defensing molecules to combat ETEC infection

Training Trajectories of Language Models Across Scales

Scaling up language models has led to unprecedented performance gains, but little is understood about how the training dynamics change as models get larger. How do language models of different sizes learn during pre-training? Why do larger language models demonstrate more desirable behaviors? In this paper, we analyze the intermediate training checkpoints of differently sized OPT models (Zhang et al.,2022)--from 125M to 175B parameters--on next-token prediction, sequence-level generation, and downstream tasks. We find that 1) at a given perplexity and independent of model sizes, a similar subset of training tokens see the most significant reduction in loss, with the rest stagnating or showing double-descent behavior; 2) early in training, all models learn to reduce the perplexity of grammatical sequences that contain hallucinations, with small models halting at this suboptimal distribution and larger ones eventually learning to assign these sequences lower probabilities; 3) perplexity is a strong predictor of in-context learning performance on 74 multiple-choice tasks from BIG-Bench, and this holds independent of the model size. Together, these results show that perplexity is more predictive of model behaviors than model size or training computation.Comment: Accepted to ACL 2023; The code and analysis results are available at https://github.com/xiamengzhou/training_trajectory_analysi

Research on the Influence of the Change of Consumption Concept on the Development of Digital Products in the Post Epidemic Era

The outbreak of the COVID-19 epidemic has caused a huge impact on the global economy. Great changes have taken place in people’s consumption patterns. In order to explore the changes of people’s consumption concept in the post epidemic era, and the impact of these changes on digital products, this paper analyzes the changes of consumption concept from different life scenes such as life, work, learning, entertainment. Then, the article summarizes the development trend of digital products in the post epidemic era. Finally, the key points of future digital product design are put forward

Research on the Influence of the Change of Consumption Concept on the Development of Digital Products in the Post Epidemic Era

The outbreak of the COVID-19 epidemic has caused a huge impact on the global economy. Great changes have taken place in people’s consumption patterns. In order to explore the changes of people’s consumption concept in the post epidemic era, and the impact of these changes on digital products, this paper analyzes the changes of consumption concept from different life scenes such as life, work, learning, entertainment. Then, the article summarizes the development trend of digital products in the post epidemic era. Finally, the key points of future digital product design are put forward

Optimizing the texture and retrogradation properties of Niangao (Rice Cake) made with naturally fermented rice flour

Abstract Niangao is prepared from polished round-grained or waxy rice flour and it is a popular and traditional steamed rice cake in China, but its preparation is different. In this study, Niangao was produced with naturally fermented rice flour and the effects of fermentation on physical properties and rheological characteristics were investigated. The results suggest that the water-holding capacity, texture, and color of the samples were significantly improved after natural fermentation, while amylose content was decreased. Additionally, fermentation had a marked effect on retarding the retrogradation of Niangao because fermented Niangao exhibited lower degree of retrogradation compared with the control. Control means Niangao which was produced by unfermented rice flour. Fermented Niangao was harder to be digested than the control was

Machine Learning Models for the Classification of CK2 Natural Products Inhibitors with Molecular Fingerprint Descriptors

Casein kinase 2 (CK2) is considered an important target for anti-cancer drugs. Given the structural diversity and broad spectrum of pharmaceutical activities of natural products, numerous studies have been performed to prove them as valuable sources of drugs. However, there has been little study relevant to identifying structural factors responsible for their inhibitory activity against CK2 with machine learning methods. In this study, classification studies were conducted on 115 natural products as CK2 inhibitors. Seven machine learning methods along with six molecular fingerprints were employed to develop qualitative classification models. The performances of all models were evaluated by cross-validation and test set. By taking predictive accuracy(CA), the area under receiver operating characteristic (AUC), and (MCC)as three performance indicators, the optimal models with high reliability and predictive ability were obtained, including the Extended Fingerprint-Logistic Regression model (CA = 0.859, AUC = 0.826, MCC = 0.520) for training test andPubChem fingerprint along with the artificial neural model (CA = 0.826, AUC = 0.933, MCC = 0.628) for test set. Meanwhile, the privileged substructures responsible for their inhibitory activity against CK2 were also identified through a combination of frequency analysis and information gain. The results are expected to provide useful information for the further utilization of natural products and the discovery of novel CK2 inhibitors

Systematic Review on Learning-based Spectral CT

28 pages, 9 figuresSpectral computed tomography (CT) has recently emerged as an advanced version of medical CT and significantly improves conventional (single-energy) CT. Spectral CT has two main forms: dual-energy computed tomography (DECT) and photon-counting computed tomography (PCCT), which offer image improvement, material decomposition, and feature quantification relative to conventional CT. However, the inherent challenges of spectral CT, evidenced by data and image artifacts, remain a bottleneck for clinical applications. To address these problems, machine learning techniques have been widely applied to spectral CT. In this review, we present the state-of-the-art data-driven techniques for spectral CT

<i>Lactobacillus zeae</i> Protects <i>Caenorhabditis elegans</i> from Enterotoxigenic <i>Escherichia coli</i>-Caused Death by Inhibiting Enterotoxin Gene Expression of the Pathogen

<div><p>Background</p><p>The nematode <i>Caenorhabditis elegans</i> has become increasingly used for screening antimicrobials and probiotics for pathogen control. It also provides a useful tool for studying microbe-host interactions. This study has established a <i>C. elegans</i> life-span assay to preselect probiotic bacteria for controlling K88⁺ enterotoxigenic <i>Escherichia coli</i> (ETEC), a pathogen causing pig diarrhea, and has determined a potential mechanism underlying the protection provided by <i>Lactobacillus</i>.</p><p>Methodology/Principal Findings</p><p>Life-span of <i>C. elegans</i> was used to measure the response of worms to ETEC infection and protection provided by lactic acid-producing bacteria (LAB). Among 13 LAB isolates that varied in their ability to protect <i>C. elegans</i> from death induced by ETEC strain JG280, <i>Lactobacillus zeae</i> LB1 offered the highest level of protection (86%). The treatment with <i>Lactobacillus</i> did not reduce ETEC JG280 colonization in the nematode intestine. Feeding <i>E. coli</i> strain JFF4 (K88⁺ but lacking enterotoxin genes of <i>estA</i>, <i>estB</i>, and <i>elt</i>) did not cause death of worms. There was a significant increase in gene expression of <i>estA</i>, <i>estB</i>, and <i>elt</i> during ETEC JG280 infection, which was remarkably inhibited by isolate LB1. The clone with either <i>estA</i> or <i>estB</i> expressed in <i>E. coli</i> DH5α was as effective as ETEC JG280 in killing the nematode. However, the <i>elt</i> clone killed only approximately 40% of worms. The killing by the clones could also be prevented by isolate LB1. The same isolate only partially inhibited the gene expression of enterotoxins in both ETEC JG280 and <i>E. coli</i> DH5α <i>in-vitro</i>.</p><p>Conclusions/Significance</p><p>The established life-span assay can be used for studies of probiotics to control ETEC (for effective selection and mechanistic studies). Heat-stable enterotoxins appeared to be the main factors responsible for the death of <i>C. elegans</i>. Inhibition of ETEC enterotoxin production, rather than interference of its intestinal colonization, appears to be the mechanism of protection offered by <i>Lactobacillus</i>.</p></div

Effect of individual clones harboring an enterotoxin gene from ETEC JG280 on the life span of <i>C. elegans</i> in the absence or presence of <i>Lactobacillus</i>.

<p>(A) Effect of individual enterotoxin clones on the life span of <i>C. elegans.</i> Worms were fed one of the following for 10 days: ♦,clone DH5α-16SRNA at 2×10⁸CFU/ml; ×, clone DH5α-LT at 2×10⁸CFU/ml; ▪, clone DH5α-STa at 2×10⁸CFU/ml; ▴, clone DH5α-STb at 2×10⁸CFU/ml; +, OP50 then ETEC JG280 at 2×10⁸CFU/ml. (B) and (C) Effect of isolates LB1 (<i>L. zeae</i>) and CL11 (<i>L. casei</i>)) on the life span of <i>C. elegans</i> infected with individual enterotoxin clones. Worms treated with ETEC JG280 isolate LB1, or CL11 only served as the controls for corresponding treatments. The concentration of all bacterial cultures used for the assays was 2×10⁸CFU/ml. In the treatment groups, worms were treated initially with a <i>Lactobacillus</i> isolate at 10⁸ CFU/ml for 18 h and then with an individual clone or ETEC JG280 (2×10⁸CFU/ml) for the remaining days. All the assays were kept for 10 days. ▪, isolate LB1 or CL11 only; •, isolate LB1 or CL11 and then clone DH5α-LT; ×, isolate LB1 or CL11 and then clone DH5α-STa; ♦, isolate LB1 or CL11 and then clone DH5α-STb; ▴, isolate LB1 or CL11 and then ETEC JG280; +, OP50 and then ETEC JG280.</p