From SCAN to Real Data: Systematic Generalization via Meaningful Learning

Humans can systematically generalize to novel compositions of existing concepts. There have been extensive conjectures into the extent to which neural networks can do the same. Recent arguments supported by evidence on the SCAN dataset claim that neural networks are inherently ineffective in such cognitive capacity. In this paper, we revisit systematic generalization from the perspective of meaningful learning, an exceptional capability of humans to learn new concepts by connecting them with other previously known knowledge. We propose to augment a training dataset in either an inductive or deductive manner to build semantic links between new and old concepts. Our observations on SCAN suggest that, following the meaningful learning principle, modern sequence-to-sequence models, including RNNs, CNNs, and Transformers, can successfully generalize to compositions of new concepts. We further validate our findings on two real-world datasets on semantic parsing and consistent compositional generalization is also observed. Moreover, our experiments demonstrate that both prior knowledge and semantic linking play a key role to achieve systematic generalization. Meanwhile, inductive learning generally works better than deductive learning in our experiments. Finally, we provide an explanation for data augmentation techniques by concluding them into either inductive-based or deductive-based meaningful learning. We hope our findings will encourage excavating existing neural networks' potential in systematic generalization through more advanced learning schemes.Comment: 19 pages, 4 figures, 14 table

Super-resolution imaging and tracking of protein–protein interactions in sub-diffraction cellular space

Imaging the location and dynamics of individual interacting protein pairs is essential but often difficult because of the fluorescent background from other paired and non-paired molecules, particularly in the sub-diffraction cellular space. Here we develop a new method combining bimolecular fluorescence complementation and photoactivated localization microscopy for super-resolution imaging and single-molecule tracking of specific protein–protein interactions. The method is used to study the interaction of two abundant proteins, MreB and EF-Tu, in Escherichia coli cells. The super-resolution imaging shows interesting distribution and domain sizes of interacting MreB–EF-Tu pairs as a subpopulation of total EF-Tu. The single-molecule tracking of MreB, EF-Tu and MreB–EF-Tu pairs reveals intriguing localization-dependent heterogonous dynamics and provides valuable insights to understanding the roles of MreB–EF-Tu interactions

Unsupervisedly Prompting AlphaFold2 for Few-Shot Learning of Accurate Folding Landscape and Protein Structure Prediction

Data-driven predictive methods which can efficiently and accurately transform protein sequences into biologically active structures are highly valuable for scientific research and medical development. Determining accurate folding landscape using co-evolutionary information is fundamental to the success of modern protein structure prediction methods. As the state of the art, AlphaFold2 has dramatically raised the accuracy without performing explicit co-evolutionary analysis. Nevertheless, its performance still shows strong dependence on available sequence homologs. Based on the interrogation on the cause of such dependence, we presented EvoGen, a meta generative model, to remedy the underperformance of AlphaFold2 for poor MSA targets. By prompting the model with calibrated or virtually generated homologue sequences, EvoGen helps AlphaFold2 fold accurately in low-data regime and even achieve encouraging performance with single-sequence predictions. Being able to make accurate predictions with few-shot MSA not only generalizes AlphaFold2 better for orphan sequences, but also democratizes its use for high-throughput applications. Besides, EvoGen combined with AlphaFold2 yields a probabilistic structure generation method which could explore alternative conformations of protein sequences, and the task-aware differentiable algorithm for sequence generation will benefit other related tasks including protein design.Comment: version 2.0; 28 pages, 6 figure

LDL receptor related protein 1 is an adverse prognostic biomarker that correlates with stromal remodeling and macrophages infiltration in bladder cancer

IntroductionBladder cancer (BLCA) is a highly heterogeneous disease influenced by the tumor microenvironment, which may affect patients' response to immune checkpoint blockade therapy. Therefore, identifying molecular markers and therapeutic targets to improve treatment is essential. In this study, we aimed to investigate the prognostic significance of LRP1 in BLCA.MethodsWe analyzed TCGA and IMvigor210 cohorts to investigate the relationship of LRP1 with BLCA prognosis. We utilized gene mutation analysis and enrichment to identify LRP1-associated mutated genes and biological processes. Deconvolution algorithms and single-cell analysis were used to understand the tumor-infiltrated cells and biological pathways associated with LRP1 expression. Immunohistochemistry was conducted to validate the bioinformatics analysis.ResultsOur study revealed that LRP1 was an independent risk factor for overall survival in BLCA patients and was associated with clinicopathological features and FGFR3 mutation frequency. Enrichment analysis demonstrated that LRP1 was involved in extracellular matrix remodeling and tumor metabolic processes. Furthermore, the ssGSEA algorithm revealed that LRP1 was positively correlated with the activities of tumor-associated pathways. Our study also found that high LRP1 expression impaired patients' responsiveness to ICB therapy in BLCA, which was predicted by TIDE prediction and validated by IMvigor210 cohort. Immunohistochemistry confirmed the expression of LRP1 in Cancer-Associated Fibroblasts (CAFs) and macrophages in the tumor microenvironment of BLCA.DiscussionOur study suggests that LRP1 may be a potential prognostic biomarker and therapeutic target in BLCA. Further research on LRP1 may improve BLCA precision medicine and enhance the efficacy of immune checkpoint blockade therapy

A 90-Day Oral Toxicity Study of the Ethanol Extract from Eupatorium japonicum Thunb and Foeniculum vulgare in Rats

Eupatorium japonicum Thunb and Foeniculum vulgare are two of the most widely used folk herbs and constituents in many traditional Chinese herbal formulas. Nonetheless, little toxicological and safety information associated with following daily repeated exposure is obtained according to previous research. The present study was performed to assess the toxicity of ethanol extract from Eupatorium japonicum Thunb and Foeniculum vulgare (EFE) in male rats administered by dietary oral gavage at target doses of 0.39, 0.78, and 1.56 g/kg body weight/day for 90 days. There were no significant adverse effects on clinical signs, body weight, food conversion efficiency, and vital hematological indices. However, some hematology and biochemical indices such as WCV, MCH, MCHC, LY, MPV, T-CHO, as well as TG revealed significant changes in Sprague–Dawley rats and organ weights in lung and spleen showed diminished in male rats. Necropsy and histopathology findings suggested that no significant differences in absolute weights were found in all organs except lung and spleen, and no treatment-related alteration was identified in any organs. All results obtained in the present study indicated that the proper use of EFE in traditional medicine at oral dosages up to 1.56 g/kg/day body weight may harbor no prolonged toxicity to rats. However, further studies of EFE are still necessary to assess its oral safety in patients

Sulforaphane attenuates irradiation induced testis injury in mice

ABSTRACTObjective: The testis is vulnerable to ionizing radiation, sexual dysfunction and male infertility are common problems after local radiation or whole-body exposure. Currently, there are no approved drugs for the prevention or treatment of radiation testicular injury. Sulforaphane (SFN) is an indirect antioxidant that induces phase II detoxification enzymes and antioxidant genes. Herein, we investigated the radiation protective effect of SFN on testicular injury in mice and its potential mechanism.Materials and Methods: Mice were randomly divided into blank control group (Ctrl), radiation + no pretreatment group (IR), and radiation + SFN groups (IRS). In the radiation + SFN groups, starting from 72 h before radiation, SFN solution was intraperitoneally injected once a day until they were sacrificed. Mice in the blank control group and the radiation + no pretreatment group were simultaneously injected intraperitoneally with an equal volume of the solvent used to dissolve SFN (PBS with a final concentration of 0.1%DMSO) until they were sacrificed. They were subjected to 6Mev-ray radiation to the lower abdominal testis area (total dose 2Gy). Twenty-four hours after radiation, six mice in each group were randomly sacrificed. Seventy-two hours after radiation, the remaining mice were sacrificed.Results: The results showed that the harmful effects of ionizing radiation on testes were manifested as damage to histoarchitecture, increased oxidative stress, and apoptosis, and thus impaired male fertility. SFN injections can reverse these symptoms.Conclusions: The results showed that SFN can improve the damage of mouse testis caused by irradiation. Furthermore, SFN prevents spermatogenesis dysfunction caused by ionizing radiation by activating Nrf2 and its downstream antioxidant gene