150 research outputs found
Towards Learning Instantiated Logical Rules from Knowledge Graphs
Efficiently inducing high-level interpretable regularities from knowledge
graphs (KGs) is an essential yet challenging task that benefits many downstream
applications. In this work, we present GPFL, a probabilistic rule learner
optimized to mine instantiated first-order logic rules from KGs. Instantiated
rules contain constants extracted from KGs. Compared to abstract rules that
contain no constants, instantiated rules are capable of explaining and
expressing concepts in more details. GPFL utilizes a novel two-stage rule
generation mechanism that first generalizes extracted paths into templates that
are acyclic abstract rules until a certain degree of template saturation is
achieved, then specializes the generated templates into instantiated rules.
Unlike existing works that ground every mined instantiated rule for evaluation,
GPFL shares groundings between structurally similar rules for collective
evaluation. Moreover, we reveal the presence of overfitting rules, their impact
on the predictive performance, and the effectiveness of a simple validation
method filtering out overfitting rules. Through extensive experiments on public
benchmark datasets, we show that GPFL 1.) significantly reduces the runtime on
evaluating instantiated rules; 2.) discovers much more quality instantiated
rules than existing works; 3.) improves the predictive performance of learned
rules by removing overfitting rules via validation; 4.) is competitive on
knowledge graph completion task compared to state-of-the-art baselines
The Use of Chinese-language Internet Information about Cancer by Chinese Health Consumers
We investigated the use of Chinese-language Internet information about cancer by Chinese health consumers, and its impact on their cancer care. We applied a grounded theory approach and undertook semi-structured interviews with 20 participants in China to learn their experience of using the Internet for cancer information as a patient or a family member. Thematic analysis of the interview data identified three key themes: (1) information needs evolve during the treatment journey; (2) Traditional Chinese Medicine (TCM) and adverse effects of treatment are the topics of greatest interest; and (3) most participants have encountered Internet health information with questionable quality. These findings suggest that although Internet has great potential to empower Chinese cancer patients and their family through cancer care journey, the information quality issues, cultural considerations and current health care paradigm constrain this potential. Further research is needed to address these issues in improving cancer care in China
Building Rule Hierarchies for Efficient Logical Rule Learning from Knowledge Graphs
Many systems have been developed in recent years to mine logical rules from
large-scale Knowledge Graphs (KGs), on the grounds that representing
regularities as rules enables both the interpretable inference of new facts,
and the explanation of known facts. Among these systems, the walk-based methods
that generate the instantiated rules containing constants by abstracting
sampled paths in KGs demonstrate strong predictive performance and
expressivity. However, due to the large volume of possible rules, these systems
do not scale well where computational resources are often wasted on generating
and evaluating unpromising rules. In this work, we address such scalability
issues by proposing new methods for pruning unpromising rules using rule
hierarchies. The approach consists of two phases. Firstly, since rule
hierarchies are not readily available in walk-based methods, we have built a
Rule Hierarchy Framework (RHF), which leverages a collection of subsumption
frameworks to build a proper rule hierarchy from a set of learned rules. And
secondly, we adapt RHF to an existing rule learner where we design and
implement two methods for Hierarchical Pruning (HPMs), which utilize the
generated hierarchies to remove irrelevant and redundant rules. Through
experiments over four public benchmark datasets, we show that the application
of HPMs is effective in removing unpromising rules, which leads to significant
reductions in the runtime as well as in the number of learned rules, without
compromising the predictive performance
Learning Logical Rules from Knowledge Graphs
Ph.D. (Integrated) ThesisExpressing and extracting regularities in multi-relational data, where data points are interrelated
and heterogeneous, requires well-designed knowledge representation. Knowledge Graphs (KGs),
as a graph-based representation of multi-relational data, have seen a rapidly growing presence in
industry and academia, where many real-world applications and academic research are either
enabled or augmented through the incorporation of KGs. However, due to the way KGs are
constructed, they are inherently noisy and incomplete. In this thesis, we focus on developing
logic-based graph reasoning systems that utilize logical rules to infer missing facts for the
completion of KGs. Unlike most rule learners that primarily mine abstract rules that contain
no constants, we are particularly interested in learning instantiated rules that contain constants
due to their ability to represent meaningful patterns and correlations that can not be expressed
by abstract rules. The inclusion of instantiated rules often leads to exponential growth in the
search space. Therefore, it is necessary to develop optimization strategies to balance between
scalability and expressivity. To such an end, we propose GPFL, a probabilistic rule learning
system optimized to mine instantiated rules through the implementation of a novel two-stage
rule generation mechanism. Through experiments, we demonstrate that GPFL not only performs
competitively on knowledge graph completion but is also much more efficient then existing
methods at mining instantiated rules. With GPFL, we also reveal overfitting instantiated rules
and provide detailed analyses about their impact on system performance. Then, we propose RHF,
a generic framework for constructing rule hierarchies from a given set of rules. We demonstrate
through experiments that with RHF and the hierarchical pruning techniques enabled by it,
significant reductions in runtime and rule size are observed due to the pruning of unpromising
rules. Eventually, to test the practicability of rule learning systems, we develop Ranta, a novel
drug repurposing system that relies on logical rules as features to make interpretable inferences.
Ranta outperforms existing methods by a large margin in predictive performance and can make
reasonable repurposing suggestions with interpretable evidence
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The archaeal ATPase PINA interacts with the helicase Hjm via its carboxyl terminal KH domain remodeling and processing replication fork and Holliday junction.
PINA is a novel ATPase and DNA helicase highly conserved in Archaea, the third domain of life. The PINA from Sulfolobus islandicus (SisPINA) forms a hexameric ring in crystal and solution. The protein is able to promote Holliday junction (HJ) migration and physically and functionally interacts with Hjc, the HJ specific endonuclease. Here, we show that SisPINA has direct physical interaction with Hjm (Hel308a), a helicase presumably targeting replication forks. In vitro biochemical analysis revealed that Hjm, Hjc, and SisPINA are able to coordinate HJ migration and cleavage in a concerted way. Deletion of the carboxyl 13 amino acid residues impaired the interaction between SisPINA and Hjm. Crystal structure analysis showed that the carboxyl 70 amino acid residues fold into a type II KH domain which, in other proteins, functions in binding RNA or ssDNA. The KH domain not only mediates the interactions of PINA with Hjm and Hjc but also regulates the hexameric assembly of PINA. Our results collectively suggest that SisPINA, Hjm and Hjc work together to function in replication fork regression, HJ formation and HJ cleavage
Merger-induced star formation in low-metallicity dwarf galaxy NGC 4809/4810
The physical mechanisms driving starbursts in dwarf galaxies are unclear, and
the effects of mergers on star formation in these galaxies are still uncertain.
We explore how the merger process affects star formation in metal-poor dwarf
galaxies by analyzing high-spatial-resolution ( 70 pc) integral field
spectrograph observations of ionized gas. We use archival data from the Very
Large Telescope/Multi Unit Spectroscopic Explorer to map the spatial
distribution of strong emission lines (e.g., , , , , etc) in the nearby merging
star-forming dwarf galaxy system NGC 4809/4810. We identify approximately 112
star-forming knots scattered among the two galaxies, where the gas-phase
metallicity distribution is inhomogeneous and mixing with metal-poor and
metal-rich ionized gas. Star-forming knots at the interacting region show lower
metallicity, the highest star formation rates (SFRs) and SFR to resolved
main-sequence-relation (rMSR) ratios. Ionized gas exhibits an obvious
northeast-southwest velocity gradient in NGC 4809, while seemingly mixed in NGC
4810. High virial parameters and the stellar mass-size relation of HII regions
indicate that these regions are dominated by direct radiation pressure from
massive stars/clusters and persistently expanding. We find two different
stellar mass surface density-stellar age relations in NGC 4809 and NGC 4810,
and the stellar ages of NGC 4810 are systematically younger than in NGC 4809.
Our study suggests that the merging stage of two dwarf galaxies can induce
starburst activities at the interaction areas, despite the metal-deficient
environment. Considering the high specific SFRs and different stellar ages, we
propose that the interaction initially triggered star formation in NGC 4809 and
then drove star formation in NGC 4810.Comment: 13 pages, 12 figures; accepted for publication in A&
Online dosimetric evaluation of larynx SBRT: A pilot study to assess the necessity of adaptive replanning
PURPOSE: We have initiated a multi-institutional phase I trial of 5-fraction stereotactic body radiotherapy (SBRT) for Stage III-IVa laryngeal cancer. We conducted this pilot dosimetric study to confirm potential utility of online adaptive replanning to preserve treatment quality.
METHODS: We evaluated ten cases: five patients enrolled onto the current trial and five patients enrolled onto a separate phase I SBRT trial for early-stage glottic larynx cancer. Baseline SBRT treatment plans were generated per protocol. Daily cone-beam CT (CBCT) or diagnostic CT images were acquired prior to each treatment fraction. Simulation CT images and target volumes were deformably registered to daily volumetric images, the original SBRT plan was copied to the deformed images and contours, delivered dose distributions were re-calculated on the deformed CT images. All of these were performed on a commercial treatment planning system. In-house software was developed to propagate the delivered dose distribution back to reference CT images using the deformation information exported from the treatment planning system. Dosimetric differences were evaluated via dose-volume histograms.
RESULTS: We could evaluate dose within 10 minutes in all cases. Prescribed coverage to gross tumor volume (GTV) and clinical target volume (CTV) was uniformly preserved; however, intended prescription dose coverage of planning treatment volume (PTV) was lost in 53% of daily treatments (mean: 93.9%, range: 83.9-97.9%). Maximum bystander point dose limits to arytenoids, parotids, and spinal cord remained respected in all cases, although variances in carotid artery doses were observed in a minority of cases.
CONCLUSIONS: Although GTV and CTV SBRT dose coverage is preserved with in-room three-dimensional image guidance, PTV coverage can vary significantly from intended plans and dose to critical structures may exceed tolerances. Online adaptive treatment re-planning is potentially necessary and clinically applicable to fully preserve treatment quality. Confirmatory trial accrual and analysis remains ongoing
Development of an evidence-based mHealth weight management program using a formative research process
BACKGROUND: There is a critical need for weight management programs that are effective, cost efficient, accessible, and acceptable to adults from diverse ethnic and socioeconomic backgrounds. mHealth (delivered via mobile phone and Internet) weight management programs have potential to address this need. To maximize the success and cost-effectiveness of such an mHealth approach it is vital to develop program content based on effective behavior change techniques, proven weight management programs, and closely aligned with participants’ needs. OBJECTIVE: This study aims to develop an evidence-based mHealth weight management program (Horizon) using formative research and a structured content development process. METHODS: The Horizon mHealth weight management program involved the modification of the group-based UK Weight Action Program (WAP) for delivery via short message service (SMS) and the Internet. We used an iterative development process with mixed methods entailing two phases: (1) expert input on evidence of effective programs and behavior change theory; and (2) target population input via focus group (n=20 participants), one-on-one phone interviews (n=5), and a quantitative online survey (n=120). RESULTS: Expert review determined that core components of a successful program should include: (1) self-monitoring of behavior; (2) prompting intention formation; (3) promoting specific goal setting; (4) providing feedback on performance; and (5) promoting review of behavioral goals. Subsequent target group input confirmed that participants liked the concept of an mHealth weight management program and expressed preferences for the program to be personalized, with immediate (prompt) and informative text messages, practical and localized physical activity and dietary information, culturally appropriate language and messages, offer social support (group activities or blogs) and weight tracking functions. Most target users expressed a preference for at least one text message per day. We present the prototype mHealth weight management program (Horizon) that aligns with those inputs. CONCLUSIONS: The Horizon prototype described in this paper could be used as a basis for other mHealth weight management programs. The next priority will be to further develop the program and conduct a full randomized controlled trial of effectiveness
Neural Interactive Collaborative Filtering
In this paper, we study collaborative filtering in an interactive setting, in
which the recommender agents iterate between making recommendations and
updating the user profile based on the interactive feedback. The most
challenging problem in this scenario is how to suggest items when the user
profile has not been well established, i.e., recommend for cold-start users or
warm-start users with taste drifting. Existing approaches either rely on overly
pessimistic linear exploration strategy or adopt meta-learning based algorithms
in a full exploitation way. In this work, to quickly catch up with the user's
interests, we propose to represent the exploration policy with a neural network
and directly learn it from the feedback data. Specifically, the exploration
policy is encoded in the weights of multi-channel stacked self-attention neural
networks and trained with efficient Q-learning by maximizing users' overall
satisfaction in the recommender systems. The key insight is that the satisfied
recommendations triggered by the exploration recommendation can be viewed as
the exploration bonus (delayed reward) for its contribution on improving the
quality of the user profile. Therefore, the proposed exploration policy, to
balance between learning the user profile and making accurate recommendations,
can be directly optimized by maximizing users' long-term satisfaction with
reinforcement learning. Extensive experiments and analysis conducted on three
benchmark collaborative filtering datasets have demonstrated the advantage of
our method over state-of-the-art methods
Rapid detection of multiple resistance genes to last-resort antibiotics in Enterobacteriaceae pathogens by recombinase polymerase amplification combined with lateral flow dipstick
The worrying emergence of multiple resistance genes to last-resort antibiotics in food animals and human populations throughout the food chain and relevant environments has been increasingly reported worldwide. Enterobacteriaceae pathogens are considered the most common reservoirs of such antibiotic resistance genes (ARGs). Thus, a rapid, efficient and accurate detection method to simultaneously screen and monitor such ARGs in Enterobacteriaceae pathogens has become an urgent need. Our study developed a recombinase polymerase amplification (RPA) assay combined with a lateral flow dipstick (LFD) for simultaneously detecting predominant resistance genes to last-resort antibiotics of Enterobacteriaceae pathogens, including mcr-1, blaNDM-1 and tet(X4). It is allowed to complete the entire process, including crude DNA extraction, amplification as well as reading, within 40 min at 37°C, and the detection limit is 101 copies/μl for mcr-1, blaNDM-1 and tet(X4). Sensitivity analysis showed obvious association of color signals with the template concentrations of mcr-1, blaNDM-1 and tet(X4) genes in Enterobacteriaceae pathogens using a test strip reader (R2 = 0.9881, R2 = 0.9745, and R2 = 0.9807, respectively), allowing for quantitative detection using multiplex RPA-LFD assays. Therefore, the RPA-LFD assay can suitably help to detect multiple resistance genes to last-resort antibiotics in foodborne pathogens and has potential applications in the field
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