What Makes for Robust Multi-Modal Models in the Face of Missing Modalities?

With the growing success of multi-modal learning, research on the robustness of multi-modal models, especially when facing situations with missing modalities, is receiving increased attention. Nevertheless, previous studies in this domain exhibit certain limitations, as they often lack theoretical insights or their methodologies are tied to specific network architectures or modalities. We model the scenarios of multi-modal models encountering missing modalities from an information-theoretic perspective and illustrate that the performance ceiling in such scenarios can be approached by efficiently utilizing the information inherent in non-missing modalities. In practice, there are two key aspects: (1) The encoder should be able to extract sufficiently good features from the non-missing modality; (2) The extracted features should be robust enough not to be influenced by noise during the fusion process across modalities. To this end, we introduce Uni-Modal Ensemble with Missing Modality Adaptation (UME-MMA). UME-MMA employs uni-modal pre-trained weights for the multi-modal model to enhance feature extraction and utilizes missing modality data augmentation techniques to better adapt to situations with missing modalities. Apart from that, UME-MMA, built on a late-fusion learning framework, allows for the plug-and-play use of various encoders, making it suitable for a wide range of modalities and enabling seamless integration of large-scale pre-trained encoders to further enhance performance. And we demonstrate UME-MMA's effectiveness in audio-visual datasets~(e.g., AV-MNIST, Kinetics-Sound, AVE) and vision-language datasets~(e.g., MM-IMDB, UPMC Food101)

Achieving low energy consuming bio-based piezoelectric nanogenerators via modulating the inner layer thickness for a highly sensitive pedometer

Considering their drawbacks of environmental pollution, biodegradable cellulose-based materials are becoming one of the most promising alternative candidates for conventional petroleum-based polymers, which are considered the fundamental materials for dynamical units in human-machine interaction systems. Using an up-to-date hydrogen bond replacement strategy, which means using the highly electronegative F− in polyvinylidene fluoride (PVDF) to replace the intramolecular hydrogen bonds in cellulose for weakening the self-assembly behavior, herein, multilayer-structured piezoelectric nanogenerators (PENGs) composed of cellulose, a small amount of PVDF, and Ba0.7Ca0.3Zr0.2Ti0.8O3 (BCZT) fillers were fabricated via modified tape-casting technology. Due to the hydrogen bond network, which was confirmed using multiple characterization methods, the fillers dispersed uniformly in the matrix. Through changing the inner layer thickness, the output performance of the PENGs can be subtly modulated, which is revealed to be caused by the synergistic effect between the trapped electrons and the inter-squeezing between adjacent particles by employing the band theory. When applied to a pedometer, one of the essential devices for monitoring human health, such a modulation can significantly improve its sensitivity. The water contact angle test also indicates their potential for use in humid environments. Compared with some typical cellulose-based PENGs, our device shows outstanding performance in PD/F, defined as the power density triggered by unit force, indicating our PENG's low energy consumption characteristic.</p

USP7 Substrates Identified by Proteomics Analysis Reveal the Specificity of USP7

Deubiquitylating enzymes (DUBs) remove ubiquitin chains from proteins and regulate protein stability and function. USP7 is one of the most extensively studied DUBs, since USP7 has several well-known substrates important for cancer progression, such as MDM2, N-MYC, and PTEN. Thus, USP7 is a promising drug target. However, systematic identification of USP7 substrates has not yet been performed. In this study, we carried out proteome profiling with label-free quantification in control and single/double-KO cells o

Genome-Wide CRISPR Screen Reveals the Synthetic Lethality between BCL2L1 Inhibition and Radiotherapy

Radiation therapy (RT) is one of the most commonly used anticancer therapies. However, the landscape of cellular response to irradiation, especially to a single high-dose irradiation, remains largely unknown. In this study, we performed a whole-genome CRISPR loss-of-function screen and revealed temporal inherent and acquired responses to RT. Specifically, we found that loss of the IL1R1 pathway led to cellular resistance to RT. This is in part because of the involvement of radiation-induced IL1R1-dependent transcriptional regulation, which relies on the NF-κB pathway. Moreover, the mitochondrial anti-apoptotic pathway, particularly the BCL2L1 gene, is crucially important for cell survival after radiation. BCL2L1 inhibition combined with RT dramatically impeded tumor growth in several breast cancer cell lines and syngeneic models. Taken together, our results suggest that the combination of an apoptosis inhibitor such as a BCL2L1 inhibitor with RT may represent a promising anticancer strategy for solid cancers including breast cancer

Construction of a risk prediction model for isolated pulmonary nodules 5–15 mm in diameter

Background: Based on current technology, the accuracy of detecting malignancy in solitary pulmonary nodules (SPNs) is limited. This study aimed to establish a malignant risk prediction model for SPNs 5–15 mm in diameter. Methods: We collected clinical characteristics and imaging features from 317 patients with SPNs 5–15 mm in diameter from the 900th Hospital of the Joint Logistic Support Force as a training cohort and 100 patients with SPNs 5–15 mm in diameter as a validation cohort. Univariate logistic regression analysis, least absolute shrinkage and selection operator (LASSO), and binary logistic regression analysis were used to screen for the independent influencing factors of benign and malignant SPN and to establish a prediction model for benign and malignant SPN with a diameter of 5–15 mm. The model in this study was compared with the Mayo model, Veterans Affairs (VA) model, Brock model, and Peking University People’s Hospital (PKUPH) model. Finally, the clinical application value of this model was assessed. Results: Univariate logistic regression analysis showed that smoking history, nodule diameter, nodule location, nodule density, margin, calcification, lobulation sign, spiculation sign, and vascular cluster sign were statistically significant factors. The results of LASSO and binary logistic regression analysis showed that smoking history, nodule diameter, nodule density, margin, lobulation sign, and vascular cluster sign were independent influencing factors of SPNs. The prediction model was successfully constructed and demonstrated a good predictive performance, with an area under the curve (AUC) value of 0.814 [95% confidence interval (CI): 0.768–0.861; P&lt;0.001] in the training cohort and 0.864 (95% CI: 0.794–0.934; P&lt;0.001) in the validation cohort. This model was shown to be highly accurate in predicting malignant SPNs and thus has a high clinical application value. Compared with previously described prediction models, including the Mayo model, VA model, Brock model, and PKUPH model, the proposed model demonstrated a significantly superior predictive ability. Conclusions: The prediction model developed in this study can be used as an early screening method for SPNs 5–15 mm in diameter

Prevalence and diagnostic value of non-criteria antiphospholipid antibodies for antiphospholipid syndrome in Chinese patients

BackgroundThe presence of antiphospholipid antibodies (aPLs) plays a pivotal role in the pathogenesis of antiphospholipid antibody syndrome (APS). This study aimed to examine the diagnostic value of a set of non−criteria aPLs and their relevance with APS-related criteria and extra-criteria manifestations.MethodsFrom a prospectively constructed database, consecutive APS patients consisting of 114 primary APS (PAPS group), 54 with APS secondary to SLE (SAPS group), 9 seronegative APS (SNAPS), as well as 209 patients with systemic lupus erythematosus (SLE) and 88 healthy controls were included in this study. Levels of criteria aPLs, baseline information, and APS-related criteria and extra-criteria features were extracted from the database. Serum levels of non-criteria aPLs including aPC IgG/IgM, aPI IgG/IgM, aPE IgG/IgM/IgA, aPG IgG/IgM/IgA, anti-phosphatidic acid (aPA) IgG/IgM, aSM IgG/IgM, and aPS/PT IgG/IgM were analyzed with AESKULISA® ELISA Test Kits.ResultsThe addition of aPC IgG/M, aPI IgG/M, aPE IgG/M/A, aSM IgG/M, and aPA IgG/M to aCL or aβ2GPI IgG/M could significantly increase diagnostic sensitivity and accuracy. A significant difference between PAPS or SAPS and HC was presented in all non-criteria aPLs except for aSM IgM and aPG IgA. Eight out of nine SNAPS patients were positive for at least 1 aPL. Pregnancy morbidity was associated with aSM IgM (r = 0.22) and aSM IgG (r = 0.15). Pre-eclampsia or premature birth was associated with aSM IgG (r = 0.16), aPI IgG (r = 0.22), aPC IgG (r = 0.16), and aPG IgG (r = 0.18). Stroke was associated with aPI IgG (r = 0.2). The clinical association was also observed in DVT with aPS/PT IgG (r = 0.17). Valve lesion was positively associated with aSM IgM (Fisher test p = 0.039), APS nephropathy was associated with aPC IgG (OR 3.797), and livedo reticularis was associated with aPE IgM (OR 15.391).ConclusionAdditional detection of non-criteria aPLs including aPC IgG/M, aPE IgG/M/A, aPI IgG/M, aSM IgG/M, and aPA IgG/M could assist in APS diagnosis. The positivity of certain aPLs was statistically associated with both criteria and extra-criteria APS clinical manifestations

A redesigned CRISPR/Cas9 system for marker-free genome editing in Plasmodium falciparum

BACKGROUND: A highly efficient CRISPR/Cas9-based marker-free genome editing system has been established in Plasmodium falciparum (Pf). However, with the current methods, two drug-selectable markers are needed for episome retention, which may present hurdles for consecutive genome manipulations due to the limited number of available selectable markers. The loading capacity of donor DNA is also unsatisfactory due to the large size of the Cas9 nuclease and sgRNA co-expression system, which limits the size of knock-in DNA fragments. Because of the inefficient end joining (EJ) DNA repair mechanism of Pf, a suicide-rescue approach could be used to address the challenges. Cas9 nuclease and sgRNA were co-expressed from a single plasmid (suicide vector) with one selectable marker, and the donor DNA was ligated into the other plasmid (rescue vector) containing only the ampicillin-resistance gene (AmpR) and a ColEl replication origin (ori). Nonetheless, whether this approach can mediate even the regular gene editing in Pf remains unknown. This study aimed to demonstrate the basic gene editing function of this Cas9-mediated suicide-rescue system. FINDINGS: The suicide and rescue vectors were constructed and co-transfected into Pf3D7. This system worked as expected when used to disrupt the Pfset2 gene and to insert a green fluorescent protein-renilla luciferase (gfp-ruc) fusion gene cassette of 3334 base pairs (bp) into the Pf47 locus, demonstrating that the suicide vector actually induced double-strand breaks (DSBs) and that the rescue vector functioned without maintenance via drug selection. CONCLUSIONS: The adapted marker-free CRISPR/Cas9 system with only a single episome-selectable marker performs well as the current systems for general gene editing which lays a solid foundation for further studies including consecutive gene manipulations and large gene knock-ins. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s13071-016-1487-4) contains supplementary material, which is available to authorized users

Genome-Wide CRISPR Screens Using Isogenic Cells Reveal Vulnerabilities Conferred by Loss of Tumor Suppressors

Exploiting cancer vulnerabilities is critical for the discovery of anticancer drugs. However, tumor suppressors cannot be directly targeted because of their loss of function. To uncover specific vulnerabilities for cells with deficiency in any given tumor suppressor(s), we performed genome-scale CRISPR loss-of-function screens using a panel of isogenic knockout cells we generated for 12 common tumor suppressors. Here, we provide a comprehensive and comparative dataset for genetic interactions between the whole-genome protein-coding genes and a panel of tumor suppressor genes, which allows us to uncover known and new high-confidence synthetic lethal interactions. Mining this dataset, we uncover essential paralog gene pairs, which could be a common mechanism for interpreting synthetic lethality. Moreover, we propose that some tumor suppressors could be targeted to suppress proliferation of cells with deficiency in other tumor suppressors. This dataset provides valuable information that can be further exploited for targeted cancer therapy