90 research outputs found
Video Compressive Sensing for Dynamic MRI
We present a video compressive sensing framework, termed kt-CSLDS, to
accelerate the image acquisition process of dynamic magnetic resonance imaging
(MRI). We are inspired by a state-of-the-art model for video compressive
sensing that utilizes a linear dynamical system (LDS) to model the motion
manifold. Given compressive measurements, the state sequence of an LDS can be
first estimated using system identification techniques. We then reconstruct the
observation matrix using a joint structured sparsity assumption. In particular,
we minimize an objective function with a mixture of wavelet sparsity and joint
sparsity within the observation matrix. We derive an efficient convex
optimization algorithm through alternating direction method of multipliers
(ADMM), and provide a theoretical guarantee for global convergence. We
demonstrate the performance of our approach for video compressive sensing, in
terms of reconstruction accuracy. We also investigate the impact of various
sampling strategies. We apply this framework to accelerate the acquisition
process of dynamic MRI and show it achieves the best reconstruction accuracy
with the least computational time compared with existing algorithms in the
literature.Comment: 30 pages, 9 figure
Sleep When Everything Looks Fine: Self-Triggered Monitoring for Signal Temporal Logic Tasks
Online monitoring is a widely used technique in assessing if the performance
of the system satisfies some desired requirements during run-time operation.
Existing works on online monitoring usually assume that the monitor can acquire
system information periodically at each time instant. However, such a periodic
mechanism may be unnecessarily energy-consuming as it essentially requires to
turn on sensors consistently. In this paper, we proposed a novel self-triggered
mechanism for model-based online monitoring of discrete-time dynamical system
under specifications described by signal temporal logic (STL) formulae.
Specifically, instead of sampling the system state at each time instant, a
self-triggered monitor can actively determine when the next system state is
sampled in addition to its monitoring decision regarding the satisfaction of
the task. We propose an effective algorithm for synthesizing such a
self-triggered monitor that can correctly evaluate a given STL formula
on-the-fly while maximizing the time interval between two observations. We show
that, compared with the standard online monitor with periodic information, the
proposed self-triggered monitor can significantly reduce observation burden
while ensuring that no information of the STL formula is lost. Case studies are
provided to illustrate the proposed monitoring mechanism
Rethinking Human-AI Collaboration in Complex Medical Decision Making: A Case Study in Sepsis Diagnosis
Today's AI systems for medical decision support often succeed on benchmark
datasets in research papers but fail in real-world deployment. This work
focuses on the decision making of sepsis, an acute life-threatening systematic
infection that requires an early diagnosis with high uncertainty from the
clinician. Our aim is to explore the design requirements for AI systems that
can support clinical experts in making better decisions for the early diagnosis
of sepsis. The study begins with a formative study investigating why clinical
experts abandon an existing AI-powered Sepsis predictive module in their
electrical health record (EHR) system. We argue that a human-centered AI system
needs to support human experts in the intermediate stages of a medical
decision-making process (e.g., generating hypotheses or gathering data),
instead of focusing only on the final decision. Therefore, we build SepsisLab
based on a state-of-the-art AI algorithm and extend it to predict the future
projection of sepsis development, visualize the prediction uncertainty, and
propose actionable suggestions (i.e., which additional laboratory tests can be
collected) to reduce such uncertainty. Through heuristic evaluation with six
clinicians using our prototype system, we demonstrate that SepsisLab enables a
promising human-AI collaboration paradigm for the future of AI-assisted sepsis
diagnosis and other high-stakes medical decision making.Comment: Under submission to CHI202
The Genomes of Oryza sativa: A History of Duplications
We report improved whole-genome shotgun sequences for the genomes of indica and japonica rice, both with multimegabase contiguity, or almost 1,000-fold improvement over the drafts of 2002. Tested against a nonredundant collection of 19,079 full-length cDNAs, 97.7% of the genes are aligned, without fragmentation, to the mapped super-scaffolds of one or the other genome. We introduce a gene identification procedure for plants that does not rely on similarity to known genes to remove erroneous predictions resulting from transposable elements. Using the available EST data to adjust for residual errors in the predictions, the estimated gene count is at least 38,000–40,000. Only 2%–3% of the genes are unique to any one subspecies, comparable to the amount of sequence that might still be missing. Despite this lack of variation in gene content, there is enormous variation in the intergenic regions. At least a quarter of the two sequences could not be aligned, and where they could be aligned, single nucleotide polymorphism (SNP) rates varied from as little as 3.0 SNP/kb in the coding regions to 27.6 SNP/kb in the transposable elements. A more inclusive new approach for analyzing duplication history is introduced here. It reveals an ancient whole-genome duplication, a recent segmental duplication on Chromosomes 11 and 12, and massive ongoing individual gene duplications. We find 18 distinct pairs of duplicated segments that cover 65.7% of the genome; 17 of these pairs date back to a common time before the divergence of the grasses. More important, ongoing individual gene duplications provide a never-ending source of raw material for gene genesis and are major contributors to the differences between members of the grass family
Impact of opioid-free analgesia on pain severity and patient satisfaction after discharge from surgery: multispecialty, prospective cohort study in 25 countries
Background: Balancing opioid stewardship and the need for adequate analgesia following discharge after surgery is challenging. This study aimed to compare the outcomes for patients discharged with opioid versus opioid-free analgesia after common surgical procedures.Methods: This international, multicentre, prospective cohort study collected data from patients undergoing common acute and elective general surgical, urological, gynaecological, and orthopaedic procedures. The primary outcomes were patient-reported time in severe pain measured on a numerical analogue scale from 0 to 100% and patient-reported satisfaction with pain relief during the first week following discharge. Data were collected by in-hospital chart review and patient telephone interview 1 week after discharge.Results: The study recruited 4273 patients from 144 centres in 25 countries; 1311 patients (30.7%) were prescribed opioid analgesia at discharge. Patients reported being in severe pain for 10 (i.q.r. 1-30)% of the first week after discharge and rated satisfaction with analgesia as 90 (i.q.r. 80-100) of 100. After adjustment for confounders, opioid analgesia on discharge was independently associated with increased pain severity (risk ratio 1.52, 95% c.i. 1.31 to 1.76; P < 0.001) and re-presentation to healthcare providers owing to side-effects of medication (OR 2.38, 95% c.i. 1.36 to 4.17; P = 0.004), but not with satisfaction with analgesia (beta coefficient 0.92, 95% c.i. -1.52 to 3.36; P = 0.468) compared with opioid-free analgesia. Although opioid prescribing varied greatly between high-income and low- and middle-income countries, patient-reported outcomes did not.Conclusion: Opioid analgesia prescription on surgical discharge is associated with a higher risk of re-presentation owing to side-effects of medication and increased patient-reported pain, but not with changes in patient-reported satisfaction. Opioid-free discharge analgesia should be adopted routinely
Finishing the euchromatic sequence of the human genome
The sequence of the human genome encodes the genetic instructions for human physiology, as well as rich information about human evolution. In 2001, the International Human Genome Sequencing Consortium reported a draft sequence of the euchromatic portion of the human genome. Since then, the international collaboration has worked to convert this draft into a genome sequence with high accuracy and nearly complete coverage. Here, we report the result of this finishing process. The current genome sequence (Build 35) contains 2.85 billion nucleotides interrupted by only 341 gaps. It covers ∼99% of the euchromatic genome and is accurate to an error rate of ∼1 event per 100,000 bases. Many of the remaining euchromatic gaps are associated with segmental duplications and will require focused work with new methods. The near-complete sequence, the first for a vertebrate, greatly improves the precision of biological analyses of the human genome including studies of gene number, birth and death. Notably, the human enome seems to encode only 20,000-25,000 protein-coding genes. The genome sequence reported here should serve as a firm foundation for biomedical research in the decades ahead
Research on the Direction of Ion Channel Related to Epileptic Seizures
Epilepsy is a group of chronic brain diseases characterized by transient central nervous system dysfunction caused by repeated abnormal synchronization of neuronal discharges in the brain, with sudden onset and repeated seizures. Epilepsy has been listed as one of the five major neuropsychiatric diseases of the World Health Organization (WHO). Hereditary epilepsy refers to epilepsy syndromes previously classified as idiopathic generalized epilepsies (IGEs), which encompasses several different epilepsy syndromes ranging in clinical severity from relatively benign febrile convulsions (FS) and childhood absence epilepsy (CAE) to the more severe juvenile myoclonic epilepsy (JME) and generalized epileptic seizures with febrile convulsions (GEFS+). This article analyzes the direction of ion channel related to epileptic seizures. It will look forward to the future research direction of some of the ion channels related to epileptogenesis
A Unified Framework for Verification of Observational Properties for Partially-Observed Discrete-Event Systems
In this paper, we investigate property verification problems in
partially-observed discrete-event systems (DES). Particularly, we are
interested in verifying observational properties that are related to the
information-flow of the system. Observational properties considered here
include diagnosability, predictability, detectability and opacity, which have
drawn considerable attentions in the literature. However, in contrast to
existing results, where different verification procedures are developed for
different properties case-by-case, in this work, we provide a unified framework
for verifying all these properties by reducing each of them as an instance of
HyperLTL model checking. Our approach is based on the construction of a Kripke
structure that effectively captures the issue of unobservability as well as the
finite string semantics in partially-observed DES so that HyperLTL model
checking techniques can be suitably applied. Then for each observational
property considered, we explicitly provide the HyperLTL formula to be checked
over the Kripke structure for the purpose of verification. Our approach is
uniform in the sense that all different properties can be verified with the
same model checking engine. Furthermore, our unified framework also brings new
insights for classifying observational properties for partially-observed DES in
terms of their verification complexity
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