Self-Supervised Video Hashing with Hierarchical Binary Auto-encoder

Existing video hash functions are built on three isolated stages: frame pooling, relaxed learning, and binarization, which have not adequately explored the temporal order of video frames in a joint binary optimization model, resulting in severe information loss. In this paper, we propose a novel unsupervised video hashing framework dubbed Self-Supervised Video Hashing (SSVH), that is able to capture the temporal nature of videos in an end-to-end learning-to-hash fashion. We specifically address two central problems: 1) how to design an encoder-decoder architecture to generate binary codes for videos; and 2) how to equip the binary codes with the ability of accurate video retrieval. We design a hierarchical binary autoencoder to model the temporal dependencies in videos with multiple granularities, and embed the videos into binary codes with less computations than the stacked architecture. Then, we encourage the binary codes to simultaneously reconstruct the visual content and neighborhood structure of the videos. Experiments on two real-world datasets (FCVID and YFCC) show that our SSVH method can significantly outperform the state-of-the-art methods and achieve the currently best performance on the task of unsupervised video retrieval

DECTIN-1: A modifier protein in CTLA-4 haploinsufficiency.

Autosomal dominant loss-of-function (LoF) variants in cytotoxic T-lymphocyte associated protein 4 (CTLA4) cause immune dysregulation with autoimmunity, immunodeficiency and lymphoproliferation (IDAIL). Incomplete penetrance and variable expressivity are characteristic of IDAIL caused by CTLA-4 haploinsufficiency (CTLA-4h), pointing to a role for genetic modifiers. Here, we describe an IDAIL proband carrying a maternally inherited pathogenic CTLA4 variant and a paternally inherited rare LoF missense variant in CLEC7A, which encodes for the β-glucan pattern recognition receptor DECTIN-1. The CLEC7A variant led to a loss of DECTIN-1 dimerization and surface expression. Notably, DECTIN-1 stimulation promoted human and mouse regulatory T cell (Treg) differentiation from naïve αβ and γδ T cells, even in the absence of transforming growth factor-β. Consistent with DECTIN-1's Treg-boosting ability, partial DECTIN-1 deficiency exacerbated the Treg defect conferred by CTL4-4h. DECTIN-1/CLEC7A emerges as a modifier gene in CTLA-4h, increasing expressivity of CTLA4 variants and acting in functional epistasis with CTLA-4 to maintain immune homeostasis and tolerance.S

TLR7 gain-of-function genetic variation causes human lupus

Although circumstantial evidence supports enhanced Toll-like receptor 7 (TLR7) signalling as a mechanism of human systemic autoimmune disease evidence of lupus-causing TLR7 gene variants is lacking. Here we describe human systemic lupus erythematosus caused by a TLR7 gain-of-function variant. TLR7 is a sensor of viral RNA and binds to guanosine. We identified a de novo, previously undescribed missense TLR7Y264H variant in a child with severe lupus and additional variants in other patients with lupus. The TLR7Y264H variant selectively increased sensing of guanosine and 2',3'-cGMP1 and was sufficient to cause lupus when introduced into mice. We show that enhanced TLR7 signalling drives aberrant survival of B cell receptor (BCR)-activated B cells, and in a cell-intrinsic manner, accumulation of CD11c+ age-associated B cells and germinal centre B cells. Follicular and extrafollicular helper T cells were also increased but these phenotypes were cell-extrinsic. Deficiency of MyD88 (an adaptor protein downstream of TLR7) rescued autoimmunity, aberrant B cell survival, and all cellular and serological phenotypes. Despite prominent spontaneous germinal-centre formation in Tlr7Y264H mice, autoimmunity was not ameliorated by germinal-centre deficiency, suggesting an extrafollicular origin of pathogenic B cells. We establish the importance of TLR7 and guanosine-containing self-ligands for human lupus pathogenesis, which paves the way for therapeutic TLR7 or MyD88 inhibition

The role of Tfh-derived LIF in B cell responses

Germinal center (GC) B-cells are critical for long-lived antibody responses and their induction and maintenance depends on T follicular helper (Tfh) cells. Increasing BCR affinity through somatic hypermutation favours a shift from memory B cells to plasma cell output over time. The contribution of Tfh cells to this shift is unclear, with some evidence that their product IL-21 promotes plasma cell formation while limiting memory B cells. Here we show that human and mouse Tfh cells produce leukemia inhibitory factor (LIF) and expression increases and sustains through late GC stages, after IL-21 has peaked. LIF signals directly in B and T-cells to enhance BCL6 expression, promote GC B cell responses and limit GC B cell apoptosis. In the absence of LIF, GCs, plasma cells and antibody affinity were reduced, while memory B cells did not change. LIF appeared more effective at inducing BCL6 in human memory B-cells compared to naive and GC B-cells. Unlike IL-21 that is abundantly expressed by both human follicular regulatory (CD25+) and helper (CD25-) T cells, LIF was selectively produced by CD25- Tfh. IL-21 and LIF activated STAT3 but only LIF induced type-I interferon responses and NFAT5 phosphorylation in B-cells. LIF emerges as an important determinant of long-lived antibody responses, with non-redundant roles in GC homeostasis

Digital transformation and innovation output of manufacturing companies-An analysis of the mediating role of internal and external transaction costs.

Digital transformation, based on digital technologies, has triggered economic growth in many industries and brought about production and service transformation in the manufacturing sector. As an important source of innovation output and a driving force for national economic development, it is of great significance to study the impact of digital transformation on innovation output in manufacturing companies. This study analyzes the effects of digital transformation on the quality, quantity, and overall innovation output of manufacturing companies from both the macro provincial-level digital transformation and micro enterprise-level digital transformation perspectives. Additionally, using data from manufacturing companies listed on the Shanghai and Shenzhen stock exchanges from 2012 to 2022, this study empirically tests the mechanism through which digital transformation affects innovation output from the perspectives of internal transaction costs and external transaction costs. The results show that digital transformation promotes overall improvement in innovation output of manufacturing companies and leads to improvements in both the quality and quantity of innovation output. Furthermore, the study finds that the effect of digital transformation on innovation output has a nonlinear characteristic under different levels of market competitiveness and market freedom. The mediation analysis reveals that the influence of digital transformation on innovation output can be attributed to the reduction of internal transaction costs and the enhancement of external transaction efficiency. In terms of digital policy formulation, it is necessary to coordinate the development of diverse and innovative digital infrastructure at the macro level with the micro-level ecosystems of enterprises, in order to reduce transaction costs within and outside innovative entities. Ultimately, it is essential for the government to foster a conducive free market environment that enhances transaction efficiency and timely regulates the excessive competition resulting from oligopolistic monopolies, thus maximizing the potential of digital transformation in promoting innovation output

Explanation of variables.

Digital transformation, based on digital technologies, has triggered economic growth in many industries and brought about production and service transformation in the manufacturing sector. As an important source of innovation output and a driving force for national economic development, it is of great significance to study the impact of digital transformation on innovation output in manufacturing companies. This study analyzes the effects of digital transformation on the quality, quantity, and overall innovation output of manufacturing companies from both the macro provincial-level digital transformation and micro enterprise-level digital transformation perspectives. Additionally, using data from manufacturing companies listed on the Shanghai and Shenzhen stock exchanges from 2012 to 2022, this study empirically tests the mechanism through which digital transformation affects innovation output from the perspectives of internal transaction costs and external transaction costs. The results show that digital transformation promotes overall improvement in innovation output of manufacturing companies and leads to improvements in both the quality and quantity of innovation output. Furthermore, the study finds that the effect of digital transformation on innovation output has a nonlinear characteristic under different levels of market competitiveness and market freedom. The mediation analysis reveals that the influence of digital transformation on innovation output can be attributed to the reduction of internal transaction costs and the enhancement of external transaction efficiency. In terms of digital policy formulation, it is necessary to coordinate the development of diverse and innovative digital infrastructure at the macro level with the micro-level ecosystems of enterprises, in order to reduce transaction costs within and outside innovative entities. Ultimately, it is essential for the government to foster a conducive free market environment that enhances transaction efficiency and timely regulates the excessive competition resulting from oligopolistic monopolies, thus maximizing the potential of digital transformation in promoting innovation output.</div

Baseline regression results.

Digital transformation, based on digital technologies, has triggered economic growth in many industries and brought about production and service transformation in the manufacturing sector. As an important source of innovation output and a driving force for national economic development, it is of great significance to study the impact of digital transformation on innovation output in manufacturing companies. This study analyzes the effects of digital transformation on the quality, quantity, and overall innovation output of manufacturing companies from both the macro provincial-level digital transformation and micro enterprise-level digital transformation perspectives. Additionally, using data from manufacturing companies listed on the Shanghai and Shenzhen stock exchanges from 2012 to 2022, this study empirically tests the mechanism through which digital transformation affects innovation output from the perspectives of internal transaction costs and external transaction costs. The results show that digital transformation promotes overall improvement in innovation output of manufacturing companies and leads to improvements in both the quality and quantity of innovation output. Furthermore, the study finds that the effect of digital transformation on innovation output has a nonlinear characteristic under different levels of market competitiveness and market freedom. The mediation analysis reveals that the influence of digital transformation on innovation output can be attributed to the reduction of internal transaction costs and the enhancement of external transaction efficiency. In terms of digital policy formulation, it is necessary to coordinate the development of diverse and innovative digital infrastructure at the macro level with the micro-level ecosystems of enterprises, in order to reduce transaction costs within and outside innovative entities. Ultimately, it is essential for the government to foster a conducive free market environment that enhances transaction efficiency and timely regulates the excessive competition resulting from oligopolistic monopolies, thus maximizing the potential of digital transformation in promoting innovation output.</div

Regression results of the threshold model.

Digital transformation, based on digital technologies, has triggered economic growth in many industries and brought about production and service transformation in the manufacturing sector. As an important source of innovation output and a driving force for national economic development, it is of great significance to study the impact of digital transformation on innovation output in manufacturing companies. This study analyzes the effects of digital transformation on the quality, quantity, and overall innovation output of manufacturing companies from both the macro provincial-level digital transformation and micro enterprise-level digital transformation perspectives. Additionally, using data from manufacturing companies listed on the Shanghai and Shenzhen stock exchanges from 2012 to 2022, this study empirically tests the mechanism through which digital transformation affects innovation output from the perspectives of internal transaction costs and external transaction costs. The results show that digital transformation promotes overall improvement in innovation output of manufacturing companies and leads to improvements in both the quality and quantity of innovation output. Furthermore, the study finds that the effect of digital transformation on innovation output has a nonlinear characteristic under different levels of market competitiveness and market freedom. The mediation analysis reveals that the influence of digital transformation on innovation output can be attributed to the reduction of internal transaction costs and the enhancement of external transaction efficiency. In terms of digital policy formulation, it is necessary to coordinate the development of diverse and innovative digital infrastructure at the macro level with the micro-level ecosystems of enterprises, in order to reduce transaction costs within and outside innovative entities. Ultimately, it is essential for the government to foster a conducive free market environment that enhances transaction efficiency and timely regulates the excessive competition resulting from oligopolistic monopolies, thus maximizing the potential of digital transformation in promoting innovation output.</div