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 Watson–Crick base‐pairing interactions between only four types of nucleotides, well‐designed DNA self‐assembly can be programmable and predictable. Stem‐loops, sticky ends, Holliday junctions, DNA tiles, and lattices are typical motifs for forming DNA‐based structures. The oligonucleotides experience thermal annealing in a near‐neutral 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 DNA‐based 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 DNA‐based 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 DNA‐based 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

Loss of Angiopoietin-like 7 diminishes the regeneration capacity of hematopoietic stem and progenitor cells

© 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

Stereoselective property of 20(S)-protopanaxadiol ocotillol type epimers affects its absorption and also the inhibition of P-glycoprotein.

Stereoselectivity has been proved to be tightly related to drug action including pharmacodynamics and pharmacokinetics. (20S,24R)-epoxy-dammarane-3,12,25-triol (24R-epimer) and (20S,24S)-epoxy-dammarane-3,12,25-triol (24S-epimer), a pair of 20(S)-protopanaxadiol (PPD) ocotillol type epimers, were the main metabolites of PPD. Previous studies have shown that 24R-epimer and 24S-epimer had stereoselectivity in pharmacological action and pharmacokinetics. In the present study, the aim was to further study the pharmacokinetic characteristics of both epimers, investigate their absorption mechanism and analyze the selectivity effects of ocotillol type side chain and C24 stereo-configuration on P-glycoprotein (P-gp) in vivo and in vitro. Results showed that the absolute bioavailability of 24R-epimer was about 14-fold higher than that of 24S-epimer, and a linear kinetic characteristic was acquired in doses of 5-20 mg/kg for both epimers after oral administration. Furthermore, the apparent permeability coefficients of 24R-epimer were 5-7 folds higher than that of 24S-epimer having lower efflux ratios in Caco-2 cell models. Moreover, both 24R-epimer and 24S-epimer had similar inhibitory effects on P-gp by increasing cellular retention of rhodamine 123 in Caco-2 cells and decreasing efflux of digoxin across Caco-2 cell monolayers. In situ in vivo experiments showed that the inhibition of 24R-epimer on P-gp was stronger than that of 24S-epimer by single-pass intestinal perfusion of rhodamine 123 in rats. Western blot analyses demonstrated that both epimers had no action on P-gp expression in Caco-2 cells. In conclusion, with respect to the stereoselectivity, C24 S-configuration of the ocotillol type epimers processed a poor transmembrane permeability and could be distinguished by P-gp. Sharing a dammarane skeleton, both 24R-epimer and 24S-epimer were potent inhibitors of P-gp. This study provides a new case of stereoselective pharmacokinetics of chiral compounds which contributes to know the chiral characteristics of P-gp and structure-action relationship of PPD type and ocotillol type ginsenosides as a P-gp inhibitor

Sensor Response Mechanism And Characterization Of Co-Based Phosphate Nanosensors

Phosphate is widely used as a nutrient in fertilizers for maximizing crop yield, which can unintentionally cause serious water contamination problems. Thus, accurate and robust phosphate sensors applicable to complex water body conditions are in great demand. In order to better understand the sensor behavior and improve the performance, we analyzed the cobalt\u27s interaction with phosphate in aqueous solution and proposed a sensor response mechanism of Co-based nanostructured electrochemical sensors for phosphate measurements. The increased surface area obtained from the nanoscale features could contribute to the improved detection range (10-6 to 10-3 M) when operated in a stable measurement condition, which was identified by the proposed mechanism. It was also observed that the inclusion of nanoscale features could enhance a specific charge transfer process more prominent over the others