Zebrafish foxo3b Negatively Regulates Antiviral Response through Suppressing the Transactivity of irf3 and irf7

Forkhead box O (FOXO)3, a member of the FOXO family of transcription factors, plays key roles in various cellular processes, including development, longevity, reproduction, and metabolism. Recently, FOXO3 has also been shown to be involved in modulating the immune response. However, how FOXO3 regulates immunity and the underlying mechanisms are still largely unknown. In this study, we show that zebrafish (Danio rerio) foxo3b, an ortholog of mammalian FOXO3, is induced by polyinosinic-polycytidylic acid stimulation and spring viremia of carp virus (SVCV) infection. We found that foxo3b interacted with irf3 and irf7 to inhibit ifr3/irf7 transcriptional activity, thus resulting in suppression of SVCV or polyinosinic-polycytidylic acid-induced IFN activation. By suppressing expression of key antiviral genes, foxo3b negatively regulated the cellular antiviral response. Furthermore, upon SVCV infection, the expression of the key antiviral genes was significantly enhanced in foxo3b-null zebrafish larvae compared with wild-type larvae. Additionally, the replication of SVCV was inhibited in foxo3b-null zebrafish larvae, leading to a higher survival rate. Our findings suggest that by suppressing irf3/irf7 activity, zebrafish foxo3b negatively regulates the antiviral response, implicating the vital role of the FOXO gene family in innate immunity.</p

A Multiobjective Computation Offloading Algorithm for Mobile Edge Computing

In mobile edge computing (MEC), smart mobile devices (SMDs) with limited computation resources and battery lifetime can offload their computing-intensive tasks to MEC servers, thus to enhance the computing capability and reduce the energy consumption of SMDs. Nevertheless, offloading tasks to the edge incurs additional transmission time and thus higher execution delay. This paper studies the trade-off between the completion time of applications and the energy consumption of SMDs in MEC networks. The problem is formulated as a multiobjective computation offloading problem (MCOP), where the task precedence, i.e. ordering of tasks in SMD applications, is introduced as a new constraint in the MCOP. An improved multiobjective evolutionary algorithm based on decomposition (MOEA/D) with two performance enhancing schemes is proposed.1) The problem-specific population initialization scheme uses a latency-based execution location initialization method to initialize the execution location (i.e. either local SMD or MEC server) for each task. 2) The dynamic voltage and frequency scaling based energy conservation scheme helps to decrease the energy consumption without increasing the completion time of applications. The simulation results clearly demonstrate that the proposed algorithm outperforms a number of state-of-the-art heuristics and meta-heuristics in terms of the convergence and diversity of the obtained nondominated solutions

Evaluating breast ultrasound S-detect image analysis for small focal breast lesions

BackgroundS-Detect is a computer-assisted, artificial intelligence-based system of image analysis that has been integrated into the software of ultrasound (US) equipment and has the capacity to independently differentiate between benign and malignant focal breast lesions. Since the revision and upgrade in both the breast imaging-reporting and data system (BI-RADS) US lexicon and the S-Detect software in 2013, evidence that supports improved accuracy and specificity of radiologists’ assessment of breast lesions has accumulated. However, such assessment using S-Detect technology to distinguish malignant from breast lesions with a diameter no greater than 2 cm requires further investigation.MethodsThe US images of focal breast lesions from 295 patients in our hospital from January 2019 to June 2022 were collected. The BI-RADS data were evaluated by the embedded program and as manually modified prior to the determination of a pathological diagnosis. The receiver operator characteristic (ROC) curves were constructed to compare the diagnostic accuracy between the assessments of the conventional US images, the S-Detect classification, and the combination of the two.ResultsThere were 326 lesions identified in 295 patients, of which pathological confirmation demonstrated that 239 were benign and 87 were malignant. The sensitivity, specificity, and accuracy of the conventional imaging group were 75.86%, 93.31%, and 88.65%. The sensitivity, specificity, and accuracy of the S-Detect classification group were 87.36%, 88.28%, and 88.04%, respectively. The assessment of the amended combination of S-Detect with US image analysis (Co-Detect group) was improved with a sensitivity, specificity, and accuracy of 90.80%, 94.56%, and 93.56%, respectively. The diagnostic accuracy of the conventional US group, the S-Detect group, and the Co-Detect group using area under curves was 0.85, 0.88 and 0.93, respectively. The Co-Detect group had a better diagnostic efficiency compared with the conventional US group (Z = 3.882, p = 0.0001) and the S-Detect group (Z = 3.861, p = 0.0001). There was no significant difference in distinguishing benign from malignant small breast lesions when comparing conventional US and S-Detect techniques.ConclusionsThe addition of S-Detect technology to conventional US imaging provided a novel and feasible method to differentiate benign from malignant small breast nodules

Towards a fuzzy domain ontology extraction method for adaptive e-learning

With the widespread applications of electronic learning (e-Learning) technologies to education at all levels, increasing number of online educational resources and messages are generated from the corresponding e-Learning environments. Nevertheless, it is quite difficult, if not totally impossible, for instructors to read through and analyze the online messages to predict the progress of their students on the fly. The main contribution of this paper is the illustration of a novel concept map generation mechanism which is underpinned by a fuzzy domain ontology extraction algorithm. The proposed mechanism can automatically construct concept maps based on the messages posted to online discussion forums. By browsing the concept maps, instructors can quickly identify the progress of their students and adjust the pedagogical sequence on the fly. Our initial experimental results reveal that the accuracy and the quality of the automatically generated concept maps are promising. Our research work opens the door to the development and application of intelligent software tools to enhance e-Learning

Exploring Memorization in Fine-tuned Language Models

LLMs have shown great capabilities in various tasks but also exhibited memorization of training data, thus raising tremendous privacy and copyright concerns. While prior work has studied memorization during pre-training, the exploration of memorization during fine-tuning is rather limited. Compared with pre-training, fine-tuning typically involves sensitive data and diverse objectives, thus may bring unique memorization behaviors and distinct privacy risks. In this work, we conduct the first comprehensive analysis to explore LMs' memorization during fine-tuning across tasks. Our studies with open-sourced and our own fine-tuned LMs across various tasks indicate that fine-tuned memorization presents a strong disparity among tasks. We provide an understanding of this task disparity via sparse coding theory and unveil a strong correlation between memorization and attention score distribution. By investigating its memorization behavior, multi-task fine-tuning paves a potential strategy to mitigate fine-tuned memorization

Correlation model between mesostructure and gradation of asphalt mixture based on statistical method

Asphalt mixture has complex gradation and mesostructure. Accurate prediction of the relationship between gradation and mesostructure is of great significance for the establishment of mesostructure numerical simulation model and image-based gradation detection. In this paper, featurization, stepwise regression, econometric hypothesis test are utilized for establishing the predicting models. Firstly, asphalt mixtures with 64 kinds of gradation are scanned by Computed Tomography (CT) to obtain the mesostructure images; Then a series of mesostructure parameters of voids and aggregates are put forward. On this basis, the relationship model between gradation and mesostructure is established and verified by featurization and statistical modeling method. The results show that for predicting the passing percentage of the 4.75 mm sieve and the mean value of average distance between aggregate centroids for 9.5–4.75 mm aggregates, the prediction error of passing percentage is acceptable. It illustrates that the relationship model between gradation and mesostructure established by statistical method is effective, and it is significance for material design and testing under the condition of big data in the future