Contrastive Masked Autoencoders for Self-Supervised Video Hashing

Self-Supervised Video Hashing (SSVH) models learn to generate short binary representations for videos without ground-truth supervision, facilitating large-scale video retrieval efficiency and attracting increasing research attention. The success of SSVH lies in the understanding of video content and the ability to capture the semantic relation among unlabeled videos. Typically, state-of-the-art SSVH methods consider these two points in a two-stage training pipeline, where they firstly train an auxiliary network by instance-wise mask-and-predict tasks and secondly train a hashing model to preserve the pseudo-neighborhood structure transferred from the auxiliary network. This consecutive training strategy is inflexible and also unnecessary. In this paper, we propose a simple yet effective one-stage SSVH method called ConMH, which incorporates video semantic information and video similarity relationship understanding in a single stage. To capture video semantic information for better hashing learning, we adopt an encoder-decoder structure to reconstruct the video from its temporal-masked frames. Particularly, we find that a higher masking ratio helps video understanding. Besides, we fully exploit the similarity relationship between videos by maximizing agreement between two augmented views of a video, which contributes to more discriminative and robust hash codes. Extensive experiments on three large-scale video datasets (i.e., FCVID, ActivityNet and YFCC) indicate that ConMH achieves state-of-the-art results. Code is available at https://github.com/huangmozhi9527/ConMH.Comment: This work is accepted by the AAAI 2023. 9 pages, 6 figures, 6 table

Paste structure and rheological properties of lotus seed starch–glycerin monostearate complexes formed by high-pressure homogenization

peer-reviewedStarch–lipid complexes were prepared using lotus seed starch (LS) and glycerin monostearate (GMS) via a high-pressure homogenization (HPH) process, and the effect of HPH on the paste structure and rheological properties of LS–GMS was investigated. Rapid Visco Analyser (RVA) profiles showed that HPH treatment inhibited the formation of the second viscosity peak of the LS–GMS paste, and the extent of this change was dependent on the level of homogenized pressure. Analysis of the size-exclusion chromatography, light microscopy, and low-field 1H nuclear magnetic resonance results revealed that high homogenized pressure (70–100 MPa) decreased molecular weight and size by degrading the branch structure of amylopectin; however, intact LS–GMS granules can optimize the network structure by filler–matrix interaction, which causes free water to transition into immobile water in the starch paste. The steady-shear results showed that the LS–GMS pastes presented non-Newtonian shear-thinning behavior, with higher homogenized pressure producing a smaller hysteresis loop area. During the oscillation process, the LS–GMS pastes prepared at 100 MPa exhibited the lowest loss tangent values in all the complexes, indicating a stronger resistance to vibration

Simultaneous Resonant and Broadband Detection for Dark Sectors

Electromagnetic resonant systems, such as cavities or LC circuits, have emerged as powerful detectors for probing ultralight boson dark matter and high-frequency gravitational waves. However, the limited resonant bandwidth of conventional single-mode resonators, imposed by quantum fluctuations, necessitates numerous scan steps to cover broad unexplored frequency regions. The incorporation of multiple auxiliary modes can realize a broadband detector while maintaining a substantial signal response. The broadened sensitive width can be on the same order as the resonant frequency, encompassing several orders of the source frequency for heterodyne detection, where a background cavity mode transitions into another. Consequently, our approach enables significantly deeper exploration of the parameter space within the same integration time compared to single-mode detection.Comment: 18 pages, 6 figure

Enrichment of Polychlorinated Biphenyls from Aqueous Solutions Using Fe3O4 Grafted Multiwalled Carbon Nanotubes with Poly Dimethyl Diallyl Ammonium Chloride

In this paper, Fe3O4 nanoparticles (Fe3O4 NPs) grafted carboxyl groups of multiwalled carbon nanotubes with cationic polyelectrolyte poly (dimethyldiallylammonium chloride) (PDDA) (MWCNTs-COO−/PDDA@Fe3O4), are successfully synthesized and used for the extraction of six kinds of major toxic polychorinated biphenyls (PCBs) from a large volume of water solution. The hydrophilicity of the PDDA cage can enhance the dispersibility of sorbents in water samples, and the superparamagnetism of the Fe3O4 NPs facilitate magnetic separation which directly led to the simplification of the extraction procedure. With the magnetic solid-phase extraction (MSPE) technique based on the MWCNTs-COO−/PDDA@Fe3O4 sorbents, it requires only 30 min to extract trace levels of PCBs from 500 mL water samples. When the eluate condensed to 1.0 mL, concentration factors for PCBs became over 500. The spiked recoveries of several real water samples for PCBs were in the range of 73.3–98.9% with relative standard deviations varying from 3.8% to 9.4%, reflecting good accuracy of the method. Therefore, preconcentration of trace level of PCBs by using this MWCNTs-COO−/PDDA@Fe3O4 sorbent, which are stable for multiple reuses, from water solution can be performed

NAT10 Maintains OGA mRNA Stability Through ac4C Modification in Regulating Oocyte Maturation

In vitro maturation (IVM) refers to the process of developing immature oocytes into the mature in vitro under the microenvironment analogous to follicle fluid. It is an important technique for patients with polycystic ovary syndrome and, especially, those young patients with the need of fertility preservation. However, as the mechanisms of oocyte maturation have not been fully understood yet, the cultivation efficiency of IVM is not satisfactory. It was confirmed in our previous study that oocyte maturation was impaired after N-acetyltransferase 10 (NAT10) knockdown (KD). In the present study, we further explored the transcriptome alteration of NAT10-depleted oocytes and found that O-GlcNAcase(OGA) was an important target gene for NAT10-mediated ac4C modification in oocyte maturation. NAT10 might regulate OGA stability and expression by suppressing its degradation. To find out whether the influence of NAT10-mediated ac4C on oocyte maturation was mediated by OGA, we further explored the role of OGA in IVM. After knocking down OGA of oocytes, oocyte maturation was inhibited. In addition, as oocytes matured, OGA expression increased and, conversely, O-linked N-acetylglucosamine (O-GlcNAc) level decreased. On the basis of NAT10 KD transcriptome and OGA KD transcriptome data, NAT10-mediated ac4C modification of OGA might play a role through G protein–coupled receptors, molecular transduction, nucleosome DNA binding, and other mechanisms in oocyte maturation. Rsph6a, Gm7788, Gm41780, Trpc7, Gm29036, and Gm47144 were potential downstream genes. In conclusion, NAT10 maintained the stability of OGA transcript by ac4C modification on it, thus positively regulating IVM. Moreover, our study revealed the regulation mechanisms of oocytes maturation and provided reference for improving IVM outcomes. At the same time, the interaction between mRNA ac4C modification and protein O-GlcNAc modification was found for the first time, which enriched the regulation network of oocyte maturation

Immunization with Fc-based recombinant Epstein-Barr virus gp350 elicits potent neutralizing humoral immune response in a BALB/c mice model

Epstein-Barr virus (EBV) was the first human virus proved to be closely associated with tumor development, such as lymphoma, nasopharyngeal carcinoma (NPC) and EBV-associated gastric carcinoma. Despite many efforts to develop prophylactic vaccines against EBV infection and diseases, no candidates have succeeded in effectively blocking EBV infection in clinical trials. Previous investigations showed that EBV gp350 plays a pivotal role in the infection of B lymphocytes. Nevertheless, using monomeric gp350 proteins as antigens has not been effective in preventing infection. Multimeric forms of the antigen are more potently immunogenic than monomers, however the multimerization elements used in previous constructs are not approved for human clinical trials. To prepare a much-needed EBV prophylactic vaccine that is potent, safe and applicable, we constructed an Fc-based form of gp350 to serve as a dimeric antigen. Here we show that the Fc-based gp350 antigen exhibits dramatically enhanced immunogenicity compared to wild-type gp350 protein. The complete or partial gp350 ectodomain was fused with the mouse IgG2a Fc domain. Fusion with the Fc domain did not impair gp350 folding, binding to a conformation-dependent neutralizing antibody and binding to its receptor by ELISA and SPR. Specific antibody titers against gp350 were notably enhanced by immunization with gp350-Fc dimers compared to gp350 monomers. Furthermore, immunization with gp350-Fc fusion proteins elicited potent neutralizing antibodies against EBV. Our data strongly suggest that an EBV gp350 vaccine based on Fc fusion proteins may be an efficient candidate to prevent EBV infection in clinical applications. Please click Additional Files below to see the full abstract

MISSRec: Pre-training and Transferring Multi-modal Interest-aware Sequence Representation for Recommendation

The goal of sequential recommendation (SR) is to predict a user's potential interested items based on her/his historical interaction sequences. Most existing sequential recommenders are developed based on ID features, which, despite their widespread use, often underperform with sparse IDs and struggle with the cold-start problem. Besides, inconsistent ID mappings hinder the model's transferability, isolating similar recommendation domains that could have been co-optimized. This paper aims to address these issues by exploring the potential of multi-modal information in learning robust and generalizable sequence representations. We propose MISSRec, a multi-modal pre-training and transfer learning framework for SR. On the user side, we design a Transformer-based encoder-decoder model, where the contextual encoder learns to capture the sequence-level multi-modal synergy while a novel interest-aware decoder is developed to grasp item-modality-interest relations for better sequence representation. On the candidate item side, we adopt a dynamic fusion module to produce user-adaptive item representation, providing more precise matching between users and items. We pre-train the model with contrastive learning objectives and fine-tune it in an efficient manner. Extensive experiments demonstrate the effectiveness and flexibility of MISSRec, promising an practical solution for real-world recommendation scenarios.Comment: Accepted to ACM MM 202