Diverse Target and Contribution Scheduling for Domain Generalization

Generalization under the distribution shift has been a great challenge in computer vision. The prevailing practice of directly employing the one-hot labels as the training targets in domain generalization~(DG) can lead to gradient conflicts, making it insufficient for capturing the intrinsic class characteristics and hard to increase the intra-class variation. Besides, existing methods in DG mostly overlook the distinct contributions of source (seen) domains, resulting in uneven learning from these domains. To address these issues, we firstly present a theoretical and empirical analysis of the existence of gradient conflicts in DG, unveiling the previously unexplored relationship between distribution shifts and gradient conflicts during the optimization process. In this paper, we present a novel perspective of DG from the empirical source domain's risk and propose a new paradigm for DG called Diverse Target and Contribution Scheduling (DTCS). DTCS comprises two innovative modules: Diverse Target Supervision (DTS) and Diverse Contribution Balance (DCB), with the aim of addressing the limitations associated with the common utilization of one-hot labels and equal contributions for source domains in DG. In specific, DTS employs distinct soft labels as training targets to account for various feature distributions across domains and thereby mitigates the gradient conflicts, and DCB dynamically balances the contributions of source domains by ensuring a fair decline in losses of different source domains. Extensive experiments with analysis on four benchmark datasets show that the proposed method achieves a competitive performance in comparison with the state-of-the-art approaches, demonstrating the effectiveness and advantages of the proposed DTCS

Rethinking Domain Generalization: Discriminability and Generalizability

Domain generalization (DG) endeavors to develop robust models that possess strong generalizability while preserving excellent discriminability. Nonetheless, pivotal DG techniques tend to improve the feature generalizability by learning domain-invariant representations, inadvertently overlooking the feature discriminability. On the one hand, the simultaneous attainment of generalizability and discriminability of features presents a complex challenge, often entailing inherent contradictions. This challenge becomes particularly pronounced when domain-invariant features manifest reduced discriminability owing to the inclusion of unstable factors, \emph{i.e.,} spurious correlations. On the other hand, prevailing domain-invariant methods can be categorized as category-level alignment, susceptible to discarding indispensable features possessing substantial generalizability and narrowing intra-class variations. To surmount these obstacles, we rethink DG from a new perspective that concurrently imbues features with formidable discriminability and robust generalizability, and present a novel framework, namely, Discriminative Microscopic Distribution Alignment (DMDA). DMDA incorporates two core components: Selective Channel Pruning~(SCP) and Micro-level Distribution Alignment (MDA). Concretely, SCP attempts to curtail redundancy within neural networks, prioritizing stable attributes conducive to accurate classification. This approach alleviates the adverse effect of spurious domain invariance and amplifies the feature discriminability. Besides, MDA accentuates micro-level alignment within each class, going beyond mere category-level alignment. This strategy accommodates sufficient generalizable features and facilitates within-class variations. Extensive experiments on four benchmark datasets corroborate the efficacy of our method

Fine-Grained Scene Graph Generation with Data Transfer

Scene graph generation (SGG) is designed to extract (subject, predicate, object) triplets in images. Recent works have made a steady progress on SGG, and provide useful tools for high-level vision and language understanding. However, due to the data distribution problems including long-tail distribution and semantic ambiguity, the predictions of current SGG models tend to collapse to several frequent but uninformative predicates (e.g., on, at), which limits practical application of these models in downstream tasks. To deal with the problems above, we propose a novel Internal and External Data Transfer (IETrans) method, which can be applied in a plug-and-play fashion and expanded to large SGG with 1,807 predicate classes. Our IETrans tries to relieve the data distribution problem by automatically creating an enhanced dataset that provides more sufficient and coherent annotations for all predicates. By training on the enhanced dataset, a Neural Motif model doubles the macro performance while maintaining competitive micro performance. The code and data are publicly available at https://github.com/waxnkw/IETrans-SGG.pytorch.Comment: ECCV 2022 (Oral

Investigation of glucose-modified liposomes using polyethylene glycols with different chain lengths as the linkers for brain targeting

Background: An intimidating challenge to transporting drugs into the brain parenchyma is the presence of the blood-brain barrier (BBB). Glucose is an essential nutritional substance for brain function sustenance, which cannot be synthesized by the brain. Its transport primarily depends on the glucose transporters on the brain capillary endothelial cells. In this paper, the brain-targeted properties of glucose-modified liposomes using polyethylene glycols with different chain lengths as the linkers were compared and evaluated to establish an optimized drug-delivery system. Methods: Coumarin 6-loaded liposomes (GLU200-LIP, GLU400-LIP, GLU1000-LIP, and GLU2000-LIP) composed of phospholipids and glucose-derived cholesterols were prepared by thin-film dispersion-ultrasound method. The BBB model in vitro was developed to evaluate the transendothelial ability of the different liposomes crossing the BBB. The biodistribution of liposomes in the mice brains was identified by in vivo and ex vivo nearinfrared fluorescence imaging and confocal laser scanning microscopy and further analyzed quantitatively by high-performance liquid chromatography. Results: Glucose-derived cholesterols were synthesized and identified, and coumarin 6-loaded liposomes were prepared successfully. The particle sizes of the four types of glucose-modified liposomes were around or smaller than 100 nm with a polydispersity index less than 0.300. GLU400-LIP, GLU1000-LIP, and GLU2000-LIP achieved higher cumulative cleared volumes on BBB model in vitro after 6 hours compared with GLU200-LIP (P < 0.05) and were significantly higher than that of the conventional liposome (P < 0.001). The qualitative and quantitative biodistribution results in the mice showed that the accumulation of GLU1000-LIP in the brain was the highest among all the groups (P < 0.01 versus LIP). Conclusion: The data indicated that GLU400-LIP, GLU1000-LIP, and GLU2000-LIP all possess the potential of brain targeting, among which GLU1000-LIP, as a promising drug-delivery system, exhibited the strongest brain delivery capacity.Nanoscience & NanotechnologyPharmacology & PharmacySCI(E)0ARTICLE163-175

A multi-omic study reveals BTG2 as a reliable prognostic marker for early-stage non-small cell lung cancer

B-cell translocation gene 2 (BTG2) is a tumour suppressor protein known to be downregulated in several types of cancer. In this study, we investigated a potential role for BTG2 in early-stage non-small cell lung cancer (NSCLC) survival. We analysed BTG2 methylation data from 1230 early-stage NSCLC patients from five international cohorts, as well as gene expression data from 3038 lung cancer cases from multiple cohorts. Three CpG probes (cg01798157, cg06373167, cg23371584) that detected BTG2 hypermethylation in tumour tissues were associated with lower overall survival. The prognostic model based on methylation could distinguish patient survival in the four cohorts [hazard ratio (HR) range, 1.51-2.21] and the independent validation set (HR=1.85). In the expression analysis, BTG2 expression was positively correlated with survival in each cohort (HR range, 0.28-0.68), which we confirmed with meta-analysis (HR=0.61, 95% CI 0.54-0.68). The three CpG probes were all negatively correlated with BTG2 expression. Importantly, an integrative model of BTG2 methylation, expression and clinical information showed better predictive ability in the training set and validation set. In conclusion, the methylation and integrated prognostic signatures based on BTG2 are stable and reliable biomarkers for early-stage NSCLC. They may have new applications for appropriate clinical adjuvant trials and personalized treatments in the future

Expression and Role of the Calcium-Sensing Receptor in Rat Peripheral Blood Polymorphonuclear Neutrophils

The calcium-sensing receptors (CaSRs) play an important role in many tissues and organs that are involved in inflammatory reactions. Peripheral blood polymorphonuclear neutrophils (PMNs) are important inflammatory cells. However, the expression and functions of CaSR in peripheral blood PMNs are still not reported. In this study, we collected rat peripheral blood PMNs to observe the relationship between CaSR and PMNs. From the results, we found first that the CaSR protein was expressed in PMNs, and it increased after PMNs were activated with fMLP. In addition, CaSR activator cincalcet promoted the expression of CaSR and P-p65 (NF-κB signaling pathway protein) and Bcl-xl (antiapoptosis protein), and it increased the secretion of interleukin-6 (IL-6) and myeloperoxidase (MPO); meanwhile, it decreased proapoptosis protein Bax expression and the production of IL-10 and reactive oxygen species (ROS). At the same time, cincalcet also decreased the PMN apoptosis rate analyzed by flow cytometry. However, CaSR inhibitor NPS-2143 and NF-κB signaling pathway inhibitor PDTC reverse the results cited earlier. All of these results indicated that CaSR can regulate PMN functions and status to play a role in inflammation, which is probably through the NF-κB signaling pathway

CYP2A6 activity and cigarette consumption interact in smoking-related lung cancer susceptibility.

Cigarette smoke, containing both nicotine and carcinogens, causes lung cancer. However, not all smokers develop lung cancer, highlighting the importance of the interaction between host susceptibility and environmental exposure in tumorigenesis. Here, we aimed to delineate the interaction between metabolizing ability of tobacco carcinogens and smoking intensity in mediating genetic susceptibility to smoking-related lung tumorigenesis. Single-variant and gene-based associations of 43 tobacco carcinogen-metabolizing genes with lung cancer were analyzed using summary statistics and individual-level genetic data, followed by causal inference of Mendelian randomization, mediation analysis, and structural equation modeling. Cigarette smoke-exposed cell models were used to detect gene expression patterns in relation to specific alleles. Data from the International Lung Cancer Consortium (29,266 cases and 56,450 controls) and UK Biobank (2,155 cases and 376,329 controls) indicated that the genetic variant rs56113850 C>T located in intron 4 of CYP2A6 was significantly associated with decreased lung cancer risk among smokers [odds ratio (OR) = 0.88, 95% confidence interval = 0.85-0.91, P = 2.18×10-16], which might interact (Pinteraction = 0.028) with and partially be mediated (ORindirect = 0.987) by smoking status. Smoking intensity accounted for 82.3% of the effect of CYP2A6 activity on lung cancer risk but entirely mediated the genetic effect of rs56113850. Mechanistically, the rs56113850 T allele rescued the downregulation of CYP2A6 caused by cigarette smoke exposure, potentially through preferential recruitment of transcription factor HLTF. Together, this study provides additional insights into the interplay between host susceptibility and carcinogen exposure in smoking-related lung tumorigenesis

Fibronectin 1: A Potential Biomarker for Ovarian Cancer

Object. Ovarian cancer is one of the most common cancers among females with high mortality rate, due to most patients diagnosed at the advanced stage of the disease. Seeking new biomarkers for ovarian cancer detection and progress indication is really important for the patients. Methods. OVCAR3 and A2780 are the two common cell lines that are used for ovarian cancer studies. The different invasion and migration abilities were observed by scratch tests and transwell experiments in our preliminary study. Gene chip was used to screen the expression gene in these two different cell lines, and then, the differentially expressed genes (at least 2-fold difference, P value < 0.05) were analyzed using KEGG. Result. Fibronectin 1 (FN1) was found to be the most strongly correlated with the invasion and migration abilities of the OVCAR3 cells. Real-time PCR and FN1 knockout cell line was conducted and confirmed this finding. Based on the Oncomine database analysis, comparing with normal people, ovarian cancer patients exhibited high levels of FN1 expression. Additionally, higher FN1 expression was found in patients with higher FIGO stages of cancer. Conclusion. FN1 could be a new biomarker for ovarian cancer detection and progress indicator

Sustainable development pathways for Chinese cities: An assessment of the advanced inclusive wealth index

The inclusive wealth index (IWI) is a stock metric proposed internationally in recent years to measure a region’s sustainable development potential. To explore sustainability more comprehensively in this context, this paper improves the inclusive wealth (IW) system proposed by the United Nations Environment Programme by extending the definition of intangible capital and refining the classification of different types of capital. We then used the advanced IWI to investigate the changes in per capita IW and its capital composition in China’s 10 National Sustainable Development Agenda Innovation Demonstration Zones (NIDZs) from 2010 to 2019, and proposed sustainable development pathways for Chinese cities. Our results underline the fact that IW and capital structure across different types of cities is highly variable. The growth of IW per capita in Shenzhen, an international metropolis, mainly depends on advanced produced capital and intangible capital. For Ordos, however — a resource-rich ‘energy city’ — the per capita IW is driven by ordinary produced capital and restricted by non-renewable natural capital, thus showing a low level of sustainability. Through its consideration of four kinds of capital, this study also points out the inequality of human capital between urban-rural and male-female groups, and demonstrates how increasing educational attainment helps to promote the transfer of human capital between regions and sectors. In general, a strong potential for sustainable development is linked to the promotion of highly educated human capital, advanced produced capital and intangible capital, while the ecosystem service value of natural capital is also key

Timespan-aware dynamic knowledge graph embedding by incorporating temporal evolution

Recently, Knowledge Graph Embedding (KGE) has attracted considerable research efforts, since it simplifies the manipulation while preserving the inherent structure of the KG. However to some extent, most existing KGE approaches ignore the historical changes of structural information involved in dynamic knowledge graphs (DKGs). To deal with this problem, this paper presents a Timespan-aware Dynamic knowledge Graph Embedding Evolution (TDG2E) method that considers temporal evolving process of DKGs. The major innovations of our paper are two-fold. Firstly, a Gated Recurrent Units (GRU) based model is utilized in TDG2E to deal with the dependency among sub-KGs that is inevitably involved in the learning process of the dynamic knowledge graph embedding. Furthermore, we incorporate an auxiliary loss to supervise the learning process of the next sub-KG by utilizing previous structural information (i.e., the hidden state of GRU). In contrast with existing approaches in the literature (e.g., HyTE and t-TransE), TDG2E preserves structural information of current sub-KG and the temporal evolving process of the DKG simultaneously. Secondly, to further deal with the time unbalance issue underlying the DKGs, a Timespan Gate is designed in GRU. It makes TDG2E possible to model the temporal evolving process of DKGs more effectively by incorporating the timespan between adjacent sub-KGs. Extensive experiments on two large temporal datasets (i.e., YAGO11k and Wikidata12k) extracted from real-world KGs validate that the proposed TDG2E significantly outperforms traditional KGE methods in terms of Mean Rank and Hit Rate.Published versio