68 research outputs found
Reinforced Path Reasoning for Counterfactual Explainable Recommendation
Counterfactual explanations interpret the recommendation mechanism via
exploring how minimal alterations on items or users affect the recommendation
decisions. Existing counterfactual explainable approaches face huge search
space and their explanations are either action-based (e.g., user click) or
aspect-based (i.e., item description). We believe item attribute-based
explanations are more intuitive and persuadable for users since they explain by
fine-grained item demographic features (e.g., brand). Moreover, counterfactual
explanation could enhance recommendations by filtering out negative items.
In this work, we propose a novel Counterfactual Explainable Recommendation
(CERec) to generate item attribute-based counterfactual explanations meanwhile
to boost recommendation performance. Our CERec optimizes an explanation policy
upon uniformly searching candidate counterfactuals within a reinforcement
learning environment. We reduce the huge search space with an adaptive path
sampler by using rich context information of a given knowledge graph. We also
deploy the explanation policy to a recommendation model to enhance the
recommendation. Extensive explainability and recommendation evaluations
demonstrate CERec's ability to provide explanations consistent with user
preferences and maintain improved recommendations. We release our code at
https://github.com/Chrystalii/CERec
Counterfactual Explanation for Fairness in Recommendation
Fairness-aware recommendation eliminates discrimination issues to build
trustworthy recommendation systems.Explaining the causes of unfair
recommendations is critical, as it promotes fairness diagnostics, and thus
secures users' trust in recommendation models. Existing fairness explanation
methods suffer high computation burdens due to the large-scale search space and
the greedy nature of the explanation search process. Besides, they perform
score-based optimizations with continuous values, which are not applicable to
discrete attributes such as gender and race. In this work, we adopt the novel
paradigm of counterfactual explanation from causal inference to explore how
minimal alterations in explanations change model fairness, to abandon the
greedy search for explanations. We use real-world attributes from Heterogeneous
Information Networks (HINs) to empower counterfactual reasoning on discrete
attributes. We propose a novel Counterfactual Explanation for Fairness
(CFairER) that generates attribute-level counterfactual explanations from HINs
for recommendation fairness. Our CFairER conducts off-policy reinforcement
learning to seek high-quality counterfactual explanations, with an attentive
action pruning reducing the search space of candidate counterfactuals. The
counterfactual explanations help to provide rational and proximate explanations
for model fairness, while the attentive action pruning narrows the search space
of attributes. Extensive experiments demonstrate our proposed model can
generate faithful explanations while maintaining favorable recommendation
performance
Causal Neural Graph Collaborative Filtering
Graph collaborative filtering (GCF) has gained considerable attention in
recommendation systems by leveraging graph learning techniques to enhance
collaborative filtering (CF) models. One classical approach in GCF is to learn
user and item embeddings by modeling complex graph relations and utilizing
these embeddings for CF models. However, the quality of the embeddings
significantly impacts the recommendation performance of GCF models. In this
paper, we argue that existing graph learning methods are insufficient in
generating satisfactory embeddings for CF models. This is because they
aggregate neighboring node messages directly, which can result in incorrect
estimations of user-item correlations. To overcome this limitation, we propose
a novel approach that incorporates causal modeling to explicitly encode the
causal effects of neighboring nodes on the target node. This approach enables
us to identify spurious correlations and uncover the root causes of user
preferences. We introduce Causal Neural Graph Collaborative Filtering (CNGCF),
the first causality-aware graph learning framework for CF. CNGCF integrates
causal modeling into the graph representation learning process, explicitly
coupling causal effects between node pairs into the core message-passing
process of graph learning. As a result, CNGCF yields causality-aware embeddings
that promote robust recommendations. Our extensive experiments demonstrate that
CNGCF provides precise recommendations that align with user preferences.
Therefore, our proposed framework can address the limitations of existing GCF
models and offer a more effective solution for recommendation systems
A novel three-dimensional template combined with MR-guided(125)I brachytherapy for recurrent glioblastoma
Background: At present, the treatment of recurrent glioblastoma is extremely challenging. In this study, we used a novel three-dimensional non-coplanar template (3DNPT) combined with open MR to guide(125)I seed implantation for recurrent glioblastoma. The aim of this study was to evaluate the feasibility, accuracy, and effectiveness of this technique. Methods: Twenty-four patients of recurrent glioblastoma underwent 3DNPT with open MR-guided(125)I brachytherapy from August 2017 to January 2019. Preoperative treatment plan and 3DNPT were made according to enhanced isovoxel T1-weighted MR images. I-125 seeds were implanted using 3DNPT and 1.0-T open MR imaging guidance. Dosimetry verification was performed after brachytherapy based on postoperative CT/MR fusion images. Preoperative and postoperative dosimetry parameters of D90, V100, V200, conformity index (CI), external index (EI) were compared. The objective response rate (ORR) at 6 months and 1-year survival rate were calculated. Median overall survival (OS) measured from the date of brachytherapy was estimated by Kaplan-Meier method. Results: There were no significant differences between preoperative and postoperative dosimetry parameters of D90, V100, V200, CI, EI (P > 0.05). The ORR at 6 months was 75.0%. The 1-year survival rate was 58.3%. Median OS was 12.9 months. One case of small amount of epidural hemorrhage occurred during the procedure. There were 3 cases of symptomatic brain edema after brachytherapy treatment, including grade three toxicity in 1 case and grade two toxicity in 2 cases. The three patients were treated with corticosteroid for 2 to 4 weeks. The clinical symptoms related to brain edema were significantly alleviated thereafter. Conclusions: 3DNPT combined with open MR-guided(125)I brachytherapy for circumscribed recurrent glioblastoma is feasible, effective, and with low risk of complications. Postoperative dosimetry matched the preoperative treatment plan. The described method can be used as a novel implantation technique for(125)I brachytherapy in the treatment of recurrent gliomas.</div
Programmable and Multifunctional DNA-Based Materials for Biomedical Applications
DNA encodes the genetic information; recently, it has also become a key player in material science. Given the specific WatsonâCrick baseâpairing interactions between only four types of nucleotides, wellâdesigned DNA selfâassembly can be programmable and predictable. Stemâloops, sticky ends, Holliday junctions, DNA tiles, and lattices are typical motifs for forming DNAâbased structures. The oligonucleotides experience thermal annealing in a nearâneutral buffer containing a divalent cation (usually Mg2+) to produce a variety of DNA nanostructures. These structures not only show beautiful landscape, but can also be endowed with multifaceted functionalities. This Review begins with the fundamental characterization and evolutionary trajectory of DNAâbased artificial structures, but concentrates on their biomedical applications. The coverage spans from controlled drug delivery to high therapeutic profile and accurate diagnosis. A variety of DNAâbased materials, including aptamers, hydrogels, origamis, and tetrahedrons, are widely utilized in different biomedical fields. In addition, to achieve better performance and functionality, material hybridization is widely witnessed, and DNA nanostructure modification is also discussed. Although there are impressive advances and high expectations, the development of DNAâbased structures/technologies is still hindered by several commonly recognized challenges, such as nuclease instability, lack of pharmacokinetics data, and relatively high synthesis cost. </p
2D nanomaterial sensing array using machine learning for differential profiling of pathogenic microbial taxonomic identification
An integrated custom cross-response sensing array has been developed combining the algorithm module's visible machine learning approach for rapid and accurate pathogenic microbial taxonomic identification. The diversified cross-response sensing array consists of two-dimensional nanomaterial (2D-n) with fluorescently labeled single-stranded DNA (ssDNA) as sensing elements to extract a set of differential response profiles for each pathogenic microorganism. By altering the 2D-n and different ssDNA with different sequences, we can form multiple sensing elements. While interacting with microorganisms, the competition between ssDNA and 2D-n leads to the release of ssDNA from 2D-n. The signals are generated from binding force driven by the exfoliation of either ssDNA or 2D-n from the microorganisms. Thus, the signal is distinguished from different ssDNA and 2D-n combinations, differentiating the extracted information and visualizing the recognition process. Fluorescent signals collected from each sensing element at the wavelength around 520 nm are applied to generate a fingerprint. As a proof of concept, we demonstrate that a six-sensing array enables rapid and accurate pathogenic microbial taxonomic identification, including the drug-resistant microorganisms, under a data size of n=288. We precisely identify microbial with an overall accuracy of 97.9%, which overcomes the big data dependence for identifying recurrent patterns in conventional methods. For each microorganism, the detection concentration is 10(5) similar to 10(8) CFU/mL for Escherichia coli, 10(2) similar to 10(7) CFU/mL for E. coli beta, 10(3) similar to 10(8) CFU/mL for Staphylococcus aureus, 10(3) similar to 10(7) CFU/mL for MRSA, 10(2) similar to 10(8) CFU/ mL for Pseudomonas aeruginosa, 10(3) similar to 10(8) CFU/mL for Enterococcus faecalis, 10(2) similar to 10(8) CFU/mL for Klebsiella pneumoniae, and 10(3) similar to 10(8) CFU/mL for Candida albicans. Combining the visible machine learning approach, this sensing array provides strategies for precision pathogenic microbial taxonomic identification
Combined inhibition of MDM2 and BCR-ABL1 tyrosine kinase targets chronic myeloid leukemia stem/progenitor cells in a murine model
Although highly effective, BCR-ABL1 tyrosine kinase inhibitors do not target chronic myeloid leukemia (CML) stem cells. Most patients relapse upon tyrosine kinase inhibitor therapy cessation. We reported previously that combined BCR-ABL1 and BCL-2 inhibition synergistically targets CML stem/progenitor cells. p53 induces apoptosis mainly by modulating BCL-2 family proteins. Although infrequently mutated in CML, p53 is antagonized by MDM2, which is regulated by BCR-ABL1 signaling. We hypothesized that MDM2 inhibition could sensitize CML cells to tyrosine kinase inhibitors. Using an inducible transgenic Scl-tTa-BCR-ABL1 murine CML model, we found, by RT-PCR and CyTOF proteomics increased p53 signaling in CML bone marrow (BM) cells compared with controls in CD45+ and linage-SCA-1+C-KIT+ populations. CML BM cells were more sensitive to exogenous BH3 peptides than controls. Combined inhibition of BCR-ABL1 with imatinib and MDM2 with DS-5272 increased NOXA level, markedly reduced leukemic linage-SCA-1+C-KIT+ cells and hematopoiesis, decreased leukemia burden, significantly prolonged the survival of mice engrafted with BM cells from Scl-tTa-BCR-ABL1 mice, and significantly decreased CML stem cell frequency in secondary transplantations. Our results suggest that CML stem/progenitor cells have increased p53 signaling and a propensity for apoptosis. Combined MDM2 and BCR-ABL1 inhibition targets CML stem/progenitor cells and has the potential to improve cure rates for CML
Loss of Angiopoietin-like 7 diminishes the regeneration capacity of hematopoietic stem and progenitor cells
Ă© 2015 Xiao et al.; licensee Biomed Central. Successful expansion of hematopoietic stem cells (HSCs) would benefit the use of HSC transplants in the clinic. Angiopoietin-like 7 promotes the expansion of hematopoietic stem and progenitor cells (HSPC) in vitro and ex vivo. However, the impact of loss of Angptl7 on HSPCs in vivo has not been characterized. Here, we generated Angptl7-deficient mice by TALEN-mediated gene targeting and found that HSC compartments in Angptl7-null mice were compromised. In addition, wild type (WT) HSPCs failed to repopulate in the BM of Angptl7-null mice after serial transplantations while the engraftment of Angptl7-deficient HSPCs in WT mice was not impaired. These results suggest that Angptl7 is required for HSPCs repopulation in a non-cell autonomous manner.Link_to_subscribed_fulltex
Multi-tissue integrative analysis of personal epigenomes
Evaluating the impact of genetic variants on transcriptional regulation is a central goal in biological science that has been constrained by reliance on a single reference genome. To address this, we constructed phased, diploid genomes for four cadaveric donors (using long-read sequencing) and systematically charted noncoding regulatory elements and transcriptional activity across more than 25 tissues from these donors. Integrative analysis revealed over a million variants with allele-specific activity, coordinated, locus-scale allelic imbalances, and structural variants impacting proximal chromatin structure. We relate the personal genome analysis to the ENCODE encyclopedia, annotating allele- and tissue-specific elements that are strongly enriched for variants impacting expression and disease phenotypes. These experimental and statistical approaches, and the corresponding EN-TEx resource, provide a framework for personalized functional genomics
Global burden of atrial fibrillation attributable to high body mass index from 1990 to 2021: findings from the Global Burden of Disease Study 2021
Abstract Objectives To assess the global burden of atrial fibrillation (AF) attributable to high body mass index (BMI) from 1990 to 2021 and analyze its spatiotemporal distribution characteristics. Study design An observational study based on GBD 2021 data. Methods Data on AF burden due to high BMI were obtained from the Global Burden of Disease Study (GBD) 2021. Estimated annual percentage change (EAPC) was calculated to evaluate temporal trends in age-standardized rates of deaths and disability-adjusted life years (DALYs) over 30 years. Results In 2021, high BMI-related AF caused 27,000 deaths and 725,000 DALYs globally, a 376% increase since 1990. Females and the elderly (aged 70+) bore a higher burden. Upper-middle-income regions surpassed high-income regions in AF burden. Australasia had the highest age-standardized rates, while High-income Asia Pacific and South Asia had the lowest. South Asia showed rapid growth in age-standardized death rates. Conclusion The global burden of high BMI-related AF varies across regions and time, threatening global health, especially for females and the elderly. Targeted strategies are needed to reduce AF and obesity
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