Structural water as an essential comonomer in supramolecular polymerization

Water is an essential comonomer in a supramolecular polymer that is used as a recyclable, water-activated glue.</jats:p

Genetic advances in systemic lupus erythematosus:an update

PURPOSE OF REVIEW: More than 80 susceptibility loci are now reported to show robust genetic association with systemic lupus erythematosus (SLE). The differential functional effects of the risk alleles for the majority of these loci remain to be defined. Here, we review current SLE association findings and the recent progress in the annotation of noncoding regions of the human genome as well as the new technologies and statistical methods that can be applied to further the understanding of SLE genetics.RECENT FINDINGS: Genome-wide association studies (GWAS) have markedly expanded the catalogue of genetic signals contributing to SLE development; we can now explain more than 50% of the disease's heritability. Expression quantitative trait loci mapping with colocalization analysis of GWAS results help to identify the underlying causal genes. The Encyclopedia of DNA elements, Roadmap Epigenome, and the Blueprint Epigenome projects have jointly annotated more than 80% of the noncoding genome, providing a wealth of information (from healthy individuals) to define the functional elements within the risk loci. Technologies, such as next-generation sequencing, chromatin structure determination, and genome editing, will help elucidate the actual mechanisms that underpin SLE risk alleles.SUMMARY: Gene expression and epigenetic databases provide a valuable resource to interpret genetic association in SLE. Expansion of such resources to include disease status and multiple ancestries will further aid the exploration of the biology underlying the genetics.</p

Anxiety of Family Caregivers over Future Work and the Double-Care Period in Urban Areas in China : A Case Study of Dalian

departmental bulletin pape

Breast cancer metastasis to thyroid: a retrospective analysis

Background: Breast cancers metastasizing to thyroid gland are relatively uncommon in clinical practice.Objective: Retrospective analysis of data from breast cancer patients with thyroid metastasis (TM).Methods: The US suspected, fine-needle aspiration cytology (FNAC) confirmed TM in breast cancer patients, treated between 2005 and 2015 at our hospital, was retrospectively analyzed. The data were re-evaluated by the pathologist and radiologist who were blinded to the patients’ data.Results: FNAC and immunohistochemistry confirmed the ultrasonography (US) suspected TM in eight breast cancer patients. Clinically both unilateral and bilateral TM was seen, which were symptomless and metachronously (6-121 months) metastasized. Six of eight cases exhibited recurrence/distant metastasis and were treated with chemotherapy/ thyroidectomy of which two cases passed away. The remaining two patients had no recurrences/distant metastases and were treated with partial/total thyroidectomy. Post-chemotherapy US showed more homogenous thyroid parenchyma with gathering of calcification that reduced in size, revealing the sensitiveness of TM to chemotherapy.Conclusion: US was useful in screening TM in breast cancer patients. Both partial and total thyroidectomy was effective in disease free survival of isolated TM cases, with controlled primary condition. TM responded well to chemotherapy in most of the recurrent breast cancer cases with or without distant metastasis.Keywords: Thyroid, ultrasonography, breast cancer, metastasis

Pre-train, Adapt and Detect: Multi-Task Adapter Tuning for Camouflaged Object Detection

Camouflaged object detection (COD), aiming to segment camouflaged objects which exhibit similar patterns with the background, is a challenging task. Most existing works are dedicated to establishing specialized modules to identify camouflaged objects with complete and fine details, while the boundary can not be well located for the lack of object-related semantics. In this paper, we propose a novel ``pre-train, adapt and detect" paradigm to detect camouflaged objects. By introducing a large pre-trained model, abundant knowledge learned from massive multi-modal data can be directly transferred to COD. A lightweight parallel adapter is inserted to adjust the features suitable for the downstream COD task. Extensive experiments on four challenging benchmark datasets demonstrate that our method outperforms existing state-of-the-art COD models by large margins. Moreover, we design a multi-task learning scheme for tuning the adapter to exploit the shareable knowledge across different semantic classes. Comprehensive experimental results showed that the generalization ability of our model can be substantially improved with multi-task adapter initialization on source tasks and multi-task adaptation on target tasks

Unsupervised Cross-Database Micro-Expression Recognition Using Target-Adapted Least-Squares Regression

AbstractOver the past several years, the research of micro-expression recognition (MER) has become an active topic in affective computing and computer vision because of its potential value in many application fields, e.g., lie detection. However, most previous works assumed an ideal scenario that both training and testing samples belong to the same micro-expression database, which is easily broken in practice. In this letter, we hence consider a more challenging scenario that the training and testing samples come from different micro-expression databases and investigated unsupervised cross-database MER in which the source database is labeled while the label information of target database is entirely unseen. To solve this interesting problem, we propose an effective method called target-adapted least-squares regression (TALSR). The basic idea of TALSR is to learn a regression coefficient matrix based on the source samples and their provided label information and also enable this learned regression coefficient matrix to suit the target micro-expression database. We are thus able to use the learned regression coefficient matrix to predict the micro-expression categories of the target micro-expression samples. Extensive experiments on CASME II and SMIC micro-expression databases are conducted to evaluate the proposed TALSR. The experimental results show that our TALSR has better performance than lots of recent well-performing domain adaptation methods in dealing with unsupervised cross-database MER tasks.Abstract Over the past several years, the research of micro-expression recognition (MER) has become an active topic in affective computing and computer vision because of its potential value in many application fields, e.g., lie detection. However, most previous works assumed an ideal scenario that both training and testing samples belong to the same micro-expression database, which is easily broken in practice. In this letter, we hence consider a more challenging scenario that the training and testing samples come from different micro-expression databases and investigated unsupervised cross-database MER in which the source database is labeled while the label information of target database is entirely unseen. To solve this interesting problem, we propose an effective method called target-adapted least-squares regression (TALSR). The basic idea of TALSR is to learn a regression coefficient matrix based on the source samples and their provided label information and also enable this learned regression coefficient matrix to suit the target micro-expression database. We are thus able to use the learned regression coefficient matrix to predict the micro-expression categories of the target micro-expression samples. Extensive experiments on CASME II and SMIC micro-expression databases are conducted to evaluate the proposed TALSR. The experimental results show that our TALSR has better performance than lots of recent well-performing domain adaptation methods in dealing with unsupervised cross-database MER tasks