207 research outputs found
Consumption and savings of migrants in China – social cohesion perspective
Boosting domestic demand is the task of China’s sustainable economic development, and in particular, China has become an
important global consumer market and the savings patterns
should be more cohesive and without discriminations. Using data
of China Migrants Dynamic Survey, the paper provides new evidence on internal migrants’ savings in China from the perspective
of homeownership and family migration. We find that migrants’
savings are 5.25–6.60 percentage points higher than hukou population even when controlling for working, social status, and social
insurance coverage which means the migrant will save
1019.88–1647.10 yuan in 2010 price more monthly. Furthermore,
we discover housing could partly explain the saving gap, while
when we take remittance and family migration into account, the
saving rate differences between migrants and hukou population
disappears, which means migrants may save to consume when
they go back to their hometown with their family members
instead of consuming later in the resident cities. The research is
carried out taking into account the objectives of social cohesion
policy identified at national and international level and their
involvement in consumption and saving processes. Our empirical
results reveal that homeownership, remittance motive and family
migration play important roles in shaping saving behaviour
of migrants
Double-layered hyaluronic acid/stearic acid-modified polyethyleneimine nanoparticles encapsulating (-)-gossypol: a nanocarrier for chiral anticancer drugs
This study aimed to enhance the water solubility and antitumor efficacy of (-)-gossypol. Polyethyleneimine conjugated with stearic acid (PgS) was used for loading and protecting (-)-gossypol through hydrogen bonding. Double-layered hyaluronic acid (HA)-modified PgS nanoparticles encapsulating (-)-gossypol [(-)-G-PgSHAs] were prepared through a two-step fabrication process. The nanoparticles possessed a uniform spherical shape with a dynamic size of 110.9 ± 2.4 nm, which was determined through transmission electron microscopy and dynamic light scattering analysis. The encapsulation efficiency and drug-loading capacity of (-)-G-PgSHAs were 72.6% ± 3.1% and 9.1% ± 0.42%, respectively. The IR spectra of the samples confirmed the protection effect of hydrogen bonding on the optical activity of the encapsulated (-)-gossypol. (-)-G-PgSHAs exhibited a controlled and tumor-specific release because of the high expression of HAase in the tumor region. The tumor-targeting feature of PgSHAs due to HA-receptor mediation was confirmed by in vitro cell uptake and in vivo near infrared fluorescence imaging. The in vitro test showed that the (-)-G-PgSHAs had similar cytotoxicity to free (-)-gossypol and was smaller than that of the encapsulated (±)-gossypol [(±)-G-PgSHAs]. The in vivo study of the anti-cancer effect of (-)-G-PgSHAs revealed that (-)-G-PgSHAs had a more enhanced tumor-suppression effect and reduced systemic toxicity compared with free (-)-gossypol and (±)-G-PgSHAs (P < 0.05). Therefore, PgSHA was a useful (-)-gossypol nanocarrier that exhibits high biocompatibility, tunable release of drug, and tumor-targeting characteristics for cancer treatment. In addition, this double-layered nanocarrier provided novel strategies for the encapsulation of other chiral drugs
Diagnosis-guided method for identifying multi-modality neuroimaging biomarkers associated with genetic risk factors in Alzheimer’s disease
Many recent imaging genetic studies focus on detecting the associations between genetic markers such as single nucleotide polymorphisms (SNPs) and quantitative traits (QTs). Although there exist a large number of generalized multivariate regression analysis methods, few of them have used diagnosis information in subjects to enhance the analysis performance. In addition, few of models have investigated the identification of multi-modality phenotypic patterns associated with interesting genotype groups in traditional methods. To reveal disease-relevant imaging genetic associations, we propose a novel diagnosis-guided multi-modality (DGMM) framework to discover multi-modality imaging QTs that are associated with both Alzheimer's disease (AD) and its top genetic risk factor (i.e., APOE SNP rs429358). The strength of our proposed method is that it explicitly models the priori diagnosis information among subjects in the objective function for selecting the disease-relevant and robust multi-modality QTs associated with the SNP. We evaluate our method on two modalities of imaging phenotypes, i.e., those extracted from structural magnetic resonance imaging (MRI) data and fluorodeoxyglucose positron emission tomography (FDG-PET) data in the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. The experimental results demonstrate that our proposed method not only achieves better performances under the metrics of root mean squared error and correlation coefficient but also can identify common informative regions of interests (ROIs) across multiple modalities to guide the disease-induced biological interpretation, compared with other reference methods
Identifying Multimodal Intermediate Phenotypes between Genetic Risk Factors and Disease Status in Alzheimer’s Disease
Neuroimaging genetics has attracted growing attention and interest, which
is thought to be a powerful strategy to examine the influence of genetic
variants (i.e., single nucleotide polymorphisms (SNPs)) on structures or
functions of human brain. In recent studies, univariate or multivariate
regression analysis methods are typically used to capture the effective
associations between genetic variants and quantitative traits (QTs) such as
brain imaging phenotypes. The identified imaging QTs, although associated with
certain genetic markers, may not be all disease specific. A useful, but
underexplored, scenario could be to discover only those QTs associated with both
genetic markers and disease status for revealing the chain from genotype to
phenotype to symptom. In addition, multimodal brain imaging phenotypes are
extracted from different perspectives and imaging markers consistently showing
up in multimodalities may provide more insights for mechanistic understanding of
diseases (i.e., Alzheimer’s disease (AD)). In this work, we propose a
general framework to exploit multi-modal brain imaging phenotypes as
intermediate traits that bridge genetic risk factors and multi-class disease
status. We applied our proposed method to explore the relation between the
well-known AD risk SNP APOE rs429358 and three baseline brain
imaging modalities (i.e., structural magnetic resonance imaging (MRI),
fluorodeoxyglucose positron emission tomography (FDG-PET) and F-18 florbetapir
PET scans amyloid imaging (AV45)) from the Alzheimer’s Disease
Neuroimaging Initiative (ADNI) database. The empirical results demonstrate that
our proposed method not only helps improve the performances of imaging genetic
associations, but also discovers robust and consistent regions of interests
(ROIs) across multi-modalities to guide the disease-induced interpretation
Identification of associations between genotypes and longitudinal phenotypes via temporally-constrained group sparse canonical correlation analysis
Motivation: Neuroimaging genetics identifies the relationships between genetic variants (i.e., the single nucleotide polymorphisms) and brain imaging data to reveal the associations from genotypes to phenotypes. So far, most existing machine-learning approaches are widely used to detect the effective associations between genetic variants and brain imaging data at one time-point. However, those associations are based on static phenotypes and ignore the temporal dynamics of the phenotypical changes. The phenotypes across multiple time-points may exhibit temporal patterns that can be used to facilitate the understanding of the degenerative process. In this article, we propose a novel temporally constrained group sparse canonical correlation analysis (TGSCCA) framework to identify genetic associations with longitudinal phenotypic markers.
Results: The proposed TGSCCA method is able to capture the temporal changes in brain from longitudinal phenotypes by incorporating the fused penalty, which requires that the differences between two consecutive canonical weight vectors from adjacent time-points should be small. A new efficient optimization algorithm is designed to solve the objective function. Furthermore, we demonstrate the effectiveness of our algorithm on both synthetic and real data (i.e., the Alzheimer’s Disease Neuroimaging Initiative cohort, including progressive mild cognitive impairment, stable MCI and Normal Control participants). In comparison with conventional SCCA, our proposed method can achieve strong associations and discover phenotypic biomarkers across multiple time-points to guide disease-progressive interpretation
Fatigue Detection for Ship OOWs Based on Input Data Features, from The Perspective of Comparison with Vehicle Drivers: A Review
Ninety percent of the world’s cargo is transported by sea, and the fatigue of ship officers of the watch (OOWs) contributes significantly to maritime accidents. The fatigue detection of ship OOWs is more difficult than that of vehicles drivers owing to an increase in the automation degree. In this study, research progress pertaining to fatigue detection in OOWs is comprehensively analysed based on a comparison with that in vehicle drivers. Fatigue detection techniques for OOWs are organised based on input sources, which include the physiological/behavioural features of OOWs, vehicle/ship features, and their comprehensive features. Prerequisites for detecting fatigue in OOWs are summarised. Subsequently, various input features applicable and existing applications to the fatigue detection of OOWs are proposed, and their limitations are analysed. The results show that the reliability of the acquired feature data is insufficient for detecting fatigue in OOWs, as well as a non-negligible invasive effect on OOWs. Hence, low-invasive physiological information pertaining to the OOWs, behaviour videos, and multisource feature data of ship characteristics should be used as inputs in future studies to realise quantitative, accurate, and real-time fatigue detections in OOWs on actual ships
BeamSearchQA: Large Language Models are Strong Zero-Shot QA Solver
Open-domain question answering is a crucial task that often requires
accessing external information. Existing methods typically adopt a single-turn
retrieve-then-read approach, where relevant documents are first retrieved, and
questions are then answered based on the retrieved information. However, there
are cases where answering a question requires implicit knowledge that is not
directly retrievable from the question itself. In this work, we propose a novel
question-answering pipeline called BeamSearchQA. Our approach leverages large
language models to iteratively generate new questions about the original
question, enabling an iterative reasoning process. By iteratively refining and
expanding the scope of the question, our method aims to capture and utilize
hidden knowledge that may not be directly obtainable through retrieval. We
evaluate our approach on the widely-used open-domain NQ and WebQ datasets. The
experimental results demonstrate that BeamSearchQA significantly outperforms
other zero-shot baselines, indicating its effectiveness in tackling the
challenges of open-domain question answering.Comment: Work in progres
Hormone Activity of Hydroxylated Polybrominated Diphenyl Ethers on Human Thyroid Receptor-β: In Vitro and In Silico Investigations
LEAD: Liberal Feature-based Distillation for Dense Retrieval
Knowledge distillation is often used to transfer knowledge from a strong
teacher model to a relatively weak student model. Traditional knowledge
distillation methods include response-based methods and feature-based methods.
Response-based methods are used the most widely but suffer from lower upper
limit of model performance, while feature-based methods have constraints on the
vocabularies and tokenizers. In this paper, we propose a tokenizer-free method
liberal feature-based distillation (LEAD). LEAD aligns the distribution between
teacher model and student model, which is effective, extendable, portable and
has no requirements on vocabularies, tokenizer, or model architecture.
Extensive experiments show the effectiveness of LEAD on several widely-used
benchmarks, including MS MARCO Passage, TREC Passage 19, TREC Passage 20, MS
MARCO Document, TREC Document 19 and TREC Document 20.Comment: Work in progres
Mining Outcome-relevant Brain Imaging Genetic Associations via Three-way Sparse Canonical Correlation Analysis in Alzheimer’s Disease
Neuroimaging genetics is an emerging field that aims to identify the associations between genetic variants (e.g., single nucleotide polymorphisms (SNPs)) and quantitative traits (QTs) such as brain imaging phenotypes. In recent studies, in order to detect complex multi-SNP-multi-QT associations, bi-multivariate techniques such as various structured sparse canonical correlation analysis (SCCA) algorithms have been proposed and used in imaging genetics studies. However, associations between genetic markers and imaging QTs identified by existing bi-multivariate methods may not be all disease specific. To bridge this gap, we propose an analytical framework, based on three-way sparse canonical correlation analysis (T-SCCA), to explore the intrinsic associations among genetic markers, imaging QTs, and clinical scores of interest. We perform an empirical study using the Alzheimer’s Disease Neuroimaging Initiative (ADNI) cohort to discover the relationships among SNPs from AD risk gene APOE, imaging QTs extracted from structural magnetic resonance imaging scans, and cognitive and diagnostic outcomes. The proposed T-SCCA model not only outperforms the traditional SCCA method in terms of identifying strong associations, but also discovers robust outcome-relevant imaging genetic patterns, demonstrating its promise for improving disease-related mechanistic understanding
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