130 research outputs found
Mitigating Off-Policy Bias in Actor-Critic Methods with One-Step Q-learning: A Novel Correction Approach
Compared to on-policy counterparts, off-policy model-free deep reinforcement
learning can improve data efficiency by repeatedly using the previously
gathered data. However, off-policy learning becomes challenging when the
discrepancy between the underlying distributions of the agent's policy and
collected data increases. Although the well-studied importance sampling and
off-policy policy gradient techniques were proposed to compensate for this
discrepancy, they usually require a collection of long trajectories and induce
additional problems such as vanishing/exploding gradients or discarding many
useful experiences, which eventually increases the computational complexity.
Moreover, their generalization to either continuous action domains or policies
approximated by deterministic deep neural networks is strictly limited. To
overcome these limitations, we introduce a novel policy similarity measure to
mitigate the effects of such discrepancy in continuous control. Our method
offers an adequate single-step off-policy correction that is applicable to
deterministic policy networks. Theoretical and empirical studies demonstrate
that it can achieve a "safe" off-policy learning and substantially improve the
state-of-the-art by attaining higher returns in fewer steps than the competing
methods through an effective schedule of the learning rate in Q-learning and
policy optimization
Are treatment guides and rational drug use policies adequately exploited in combating respiratory system diseases?
SummaryThe aim of the present study was to increase awareness regarding the rational use of medicines. The data were obtained via the Material Resources Management System Module of the Ministry of Health. For the appropriateness of treatments, the Global Initiative for Asthma, the Global Initiative for Chronic Obstructive Lung Disease, and the guidelines for the rational use of medicines were used. We also investigated whether any de-escalation method or physical exercise was performed. Statistical analyses were performed using descriptive statistics to determine the mean, standard deviation, and frequency. The results showed that healthcare providers ignored potential drug reactions or adverse interactions, and reflecting the lack of adherence to the current treatment guides, 35.8% irrational use of medicines was recorded. Thus, de-escalation methods should be used to decrease costs or narrow the antibiotic spectrum, antibiotic selection should consider the resistance patterns, culturing methods should be analyzed, and monotherapy should be preferred over combination treatments
Improving Phase Change Memory Performance with Data Content Aware Access
A prominent characteristic of write operation in Phase-Change Memory (PCM) is
that its latency and energy are sensitive to the data to be written as well as
the content that is overwritten. We observe that overwriting unknown memory
content can incur significantly higher latency and energy compared to
overwriting known all-zeros or all-ones content. This is because all-zeros or
all-ones content is overwritten by programming the PCM cells only in one
direction, i.e., using either SET or RESET operations, not both. In this paper,
we propose data content aware PCM writes (DATACON), a new mechanism that
reduces the latency and energy of PCM writes by redirecting these requests to
overwrite memory locations containing all-zeros or all-ones. DATACON operates
in three steps. First, it estimates how much a PCM write access would benefit
from overwriting known content (e.g., all-zeros, or all-ones) by
comprehensively considering the number of set bits in the data to be written,
and the energy-latency trade-offs for SET and RESET operations in PCM. Second,
it translates the write address to a physical address within memory that
contains the best type of content to overwrite, and records this translation in
a table for future accesses. We exploit data access locality in workloads to
minimize the address translation overhead. Third, it re-initializes unused
memory locations with known all-zeros or all-ones content in a manner that does
not interfere with regular read and write accesses. DATACON overwrites unknown
content only when it is absolutely necessary to do so. We evaluate DATACON with
workloads from state-of-the-art machine learning applications, SPEC CPU2017,
and NAS Parallel Benchmarks. Results demonstrate that DATACON significantly
improves system performance and memory system energy consumption compared to
the best of performance-oriented state-of-the-art techniques.Comment: 18 pages, 21 figures, accepted at ACM SIGPLAN International Symposium
on Memory Management (ISMM
The miR-644a/CTBP1/p53 axis suppresses drug resistance by simultaneous inhibition of cell survival and epithelialmesenchymal transition in breast cancer
Tumor cells develop drug resistance which leads to recurrence and distant metastasis. MicroRNAs are key regulators of tumor pathogenesis; however, little is known whether they can sensitize cells and block metastasis simultaneously. Here, we report miR-644a as a novel inhibitor of both cell survival and EMT whereby acting as pleiotropic therapy-sensitizer in breast cancer. We showed that both miR-644a expression and its gene signature are associated with tumor progression and distant metastasis-free survival. Mechanistically, miR-644a directly targets the transcriptional co-repressor C-Terminal Binding Protein 1 (CTBP1) whose knock-outs by the CRISPRCas9 system inhibit tumor growth, metastasis, and drug resistance, mimicking the phenotypes induced by miR-644a. Furthermore, downregulation of CTBP1 by miR-644a upregulates wild type- or mutant-p53 which acts as a 'molecular switch' between G1-arrest and apoptosis by inducing cyclin-dependent kinase inhibitor 1 (p21, CDKN1A, CIP1) or pro-apoptotic phorbol-12-myristate-13-acetate-induced protein 1 (Noxa, PMAIP1), respectively. Interestingly, an increase in mutant-p53 by either overexpression of miR-644a or downregulation of CTBP1 was enough to shift this balance in favor of apoptosis through upregulation of Noxa. Notably, p53- mutant patients, but not p53-wild type ones, with high CTBP1 have a shorter survival suggesting that CTBP1 could be a potential prognostic factor for breast cancer patients with p53 mutations. Overall, re-activation of the miR-644a/CTBP1/p53 axis may represent a new strategy for overcoming both therapy resistance and metastasis
Predictive performance of CT for adverse outcomes among COVID-19 suspected patients: a two-center retrospective study
The aim of the study was to compare the performance of various computed tomography (CT) reporting tools, including zonal CT visual score (ZCVS), the number of involved lobes, and Radiological Society of North America (RSNA) categorization in predicting adverse outcomes among patients hospitalized due to the lower respiratory symptoms during the coronavirus disease 2019 (COVID-19) pandemic. A total of 405 patients admitted with severe respiratory symptoms who underwent a chest CT were enrolled. The primary adverse outcome was intensive care unit (ICU) admission of patients. Predictive performances of reporting tools were compared using the area under the receiver operating characteristic curves (AUC ROC). Among the 405 patients, 39 (9.63%) required ICU support during their hospital stay. At least two or more observers reported a typical and indeterminate COVID-19 pneumonia CT pattern according to RSNA categorization in 70% (285/405) of patients. Among these, 63% (179/285) had a positive polymerase chain reaction (PCR test for the SARS-CoV-2 virus. The median number of lobes involved according to CT was higher in patients who required ICU support (median interquartile range [IQR], 5[3; 5] vs. 3[0; 5]). The median ZCVS score was higher among the patients that subsequently required ICU support (median [IQR], 4[0; 12] vs. 13[5.75; 24]). The bootstrap comparisons of AUC ROC showed significant differences between reporting tools, and the ZCVS was found to be superior (AUC ROC, 71-75%). The ZCVS score at the first admission showed a linear and significant association with adverse outcomes among patients with the lower respiratory tract symptoms during the COVID-19 pandemic
Growth, tolerance and safety outcomes with use of an extensively hydrolyzed casein-based formula in infants with cow’s milk protein allergy
ObjectiveTo evaluate growth, tolerance and safety outcomes with use of an extensively hydrolyzed casein-based formula (eHCF) in infants with cow’s milk protein allergy (CMPA).MethodsA total of 226 infants (mean ± SD age: 106.5 ± 39.5 days, 52.7% were girls) with CMPA who received eHCF comprising at least half of the daily dietary intake were included. Data on anthropometrics [weight for age (WFA), length for age (LFA) and weight for length (WFL) z-scores] were recorded at baseline (visit 1), while data on infant feeding and stool records, anthropometrics and Infant Feeding and Stool Patterns and Formula Satisfaction Questionnaires were recorded at visit 2 (on Days 15 ± 5) and visit 3 (on Days 30 ± 5).ResultsFrom baseline to visit 2 and visit 3, WFA z-scores (from −0.60 ± 1.13 to −0.54 ± 1.09 at visit 2, and to −0.44 ± 1.05 at visit 3, p < 0.001) and WFL z-scores (from −0.80 ± 1.30 to −0.71 ± 1.22 at visit 2, and to −0.64 ± 1.13 at visit 3, p = 0.002) were significantly increased. At least half of infants never experienced irritability or feeding refusal (55.7%) and spit-up after feeding (50.2%). The majority of mothers were satisfied with the study formula (93.2%), and wished to continue using it (92.2%).ConclusionsIn conclusion, eHCF was well-accepted and tolerated by an intended use population of infants ≤ 6 months of age with CMPA and enabled adequate volume consumption and improved growth indices within 30 days of utilization alongside a favorable gastrointestinal tolerance and a high level of parental satisfaction
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
Dynamic Pricing and Learning: Historical Origins, Current Research, and New Directions
Reducing the environmental impact of surgery on a global scale: systematic review and co-prioritization with healthcare workers in 132 countries
Abstract
Background
Healthcare cannot achieve net-zero carbon without addressing operating theatres. The aim of this study was to prioritize feasible interventions to reduce the environmental impact of operating theatres.
Methods
This study adopted a four-phase Delphi consensus co-prioritization methodology. In phase 1, a systematic review of published interventions and global consultation of perioperative healthcare professionals were used to longlist interventions. In phase 2, iterative thematic analysis consolidated comparable interventions into a shortlist. In phase 3, the shortlist was co-prioritized based on patient and clinician views on acceptability, feasibility, and safety. In phase 4, ranked lists of interventions were presented by their relevance to high-income countries and low–middle-income countries.
Results
In phase 1, 43 interventions were identified, which had low uptake in practice according to 3042 professionals globally. In phase 2, a shortlist of 15 intervention domains was generated. In phase 3, interventions were deemed acceptable for more than 90 per cent of patients except for reducing general anaesthesia (84 per cent) and re-sterilization of ‘single-use’ consumables (86 per cent). In phase 4, the top three shortlisted interventions for high-income countries were: introducing recycling; reducing use of anaesthetic gases; and appropriate clinical waste processing. In phase 4, the top three shortlisted interventions for low–middle-income countries were: introducing reusable surgical devices; reducing use of consumables; and reducing the use of general anaesthesia.
Conclusion
This is a step toward environmentally sustainable operating environments with actionable interventions applicable to both high– and low–middle–income countries
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