No abstract

Our findings revealed a novel role of largazole in the treatment of liver fibrosis. Through multiple mechanisms, largazole could be a potentially effective antifibrotic agent.

Model inversion, whose goal is to recover training data from a pre-trained model, has been recently proved feasible. However, existing inversion methods usually suffer from the mode collapse problem, where the synthesized instances are highly similar to each other and thus show limited effectiveness for downstream tasks, such as knowledge distillation. In this paper, we propose Contrastive Model Inversion (CMI), where the data diversity is explicitly modeled as an optimizable objective, to alleviate the mode collapse issue. Our main observation is that, under the constraint of the same amount of data, higher data diversity usually indicates stronger instance discrimination. To this end, we introduce in CMI a contrastive learning objective that encourages the synthesizing instances to be distinguishable from the already synthesized ones in previous batches. Experiments of pre-trained models on CIFAR-10, CIFAR-100, and Tiny-ImageNet demonstrate that CMI not only generates more visually plausible instances than the state of the arts, but also achieves significantly superior performance when the generated data are used for knowledge distillation. Code is available at https://github.com/zju-vipa/DataFree.

While treatment for B-cell malignancies has been revolutionized through the advent of CAR immunotherapy, similar strategies for T-cell malignancies have been limited. Additionally, T-cell leukemias and lymphomas can commonly metastasize to the CNS, where outcomes are poor and treatment options are associated with severe side effects. Consequently, the development of safer and more effective alternatives for targeting malignant T cells that have invaded the CNS remains clinically important. CD5 CAR has previously been shown to effectively target various T-cell cancers in preclinical studies. As IL-15 strengthens the anti-tumor response, we have modified CD5 CAR to secrete an IL-15/IL-15sushi complex. In a Phase I clinical trial, these CD5-IL15/IL15sushi CAR T cells were tested for safety and efficacy in a patient with refractory T-LBL with CNS infiltration. CD5-IL15/IL15sushi CAR T cells were able to rapidly ablate the CNS lymphoblasts within a few weeks, resulting in the remission of the patient’s lymphoma. Despite the presence of CD5 on normal T cells, the patient only experienced a brief, transient T-cell aplasia. These results suggest that CD5-IL15/IL15sushi CAR T cells may be a safe and useful treatment of T-cell malignancies and may be particularly beneficial for patients with CNS involvement.Graphical Abstract

Abstract-Configuration bugs are one of the dominant causes of software failures. Previous studies show that a configuration bug could cause huge financial losses in a software system. The importance of configuration bugs has attracted various research studies, e.g., to detect, diagnose, and fix configuration bugs. Given a bug report, an approach that can identify whether the bug is a configuration bug could help developers reduce debugging effort. We refer to this problem as configuration bug reports prediction. To address this problem, we develop a new automated framework that applies text mining technologies on the naturallanguage description of bug reports to train a statistical model on historical bug reports with known labels (i.e., configuration or non-configuration), and the statistical model is then used to predict a label for a new bug report. Developers could apply our model to automatically predict labels of bug reports to improve their productivity. Our tool first applies feature selection techniques (e.g., information gain and Chi-square) to preprocess the textual information in bug reports, and then applies various text mining techniques (e.g., naive Bayes, SVM, naive Bayes multinomial) to build statistical models. We evaluate our solution on 5 bug report datasets including accumulo, activemq, camel, flume, and wicket. We show that naive Bayes multinomial with information gain achieves the best performance. On average across the 5 projects, its accuracy, configuration F-measure and non-configuration F-measure are 0.811, 0.450, and 0.880, respectively. We also compare our solution with the method proposed by Arshad et al.. The results show that our proposed approach that uses naive Bayes multinomial with information gain on average improves accuracy, configuration F-measure and non-configuration F-measure scores of Arshad et al.'s method by 8.34%, 103.7%, and 4.24%, respectively.