Forward and backward state abstractions for off-policy evaluation

Off-policy evaluation (OPE) is crucial for evaluating a target policy’s impact offline before its deployment. However, achieving accurate OPE in large state spaces remains challenging. This paper studies state abstractions – originally designed for policy learning – in the context of OPE. Our contributions are three-fold: (i) We define a set of irrelevance conditions central to learning state abstractions for OPE. (ii) We derive sufficient conditions for achieving irrelevance in Q-functions and marginalized importance sampling ratios, the latter obtained by constructing a time-reversed Markov decision process (MDP) based on the observed MDP. (iii) We propose a novel two-step procedure that sequentially projects the original state space into a smaller space, which substantially simplify the sample complexity of OPE arising from high cardinality

Risk factors on healthcare-associated infections among tuberculosis hospitalized patients in China from 2001 to 2020: A systematic review and meta-analysis

Background: China has been still suffering from high burden attributable to tuberculosis (TB) and healthcare-associated infections (HAIs). TB patients are at high risk to get HAIs. Evidence-based guidelines or regulations to constrain the rising HAIs among TB hospitalized patients are needed in China. The aim of this systematic review and meta-analysis is to investigate the risk factors associated with HAIs among TB hospitalized patients in Chinese hospitals. Methods: Medline, EMBASE and Chinese Journals Online databases were searched. The search was limited to studies published from January 1st 2001 to December 31st 2020. Meta-analyses of ORs of the risk factors between patients with HAIs and patients without HAIs among TB hospitalized patients were estimated. Heterogeneity among studies was assessed based on the τ ^ 2 and I2 statistics to select the meta-analysis model. Review Manager 5.3 was employed and P < 0.05 was considered as statistical significance. Results: 851 records were filtered from the databases, of which 11 studies were included in the quantitative meta-analysis. A total of 11,922TB patients were included in the systematic review and meta-analysis, of which 1133 were diagnosed as having HAIs. Age older than 60 years (OR: 2.89 [2.01–4.15]), complications (OR: 3.28 [2.10–5.13]), diabetes mellitus (OR: 1.63 [1.22–2.19]), invasive procedure (OR: 3.80 [2.25–6.42]), longer than 15 hospitalization days (OR: 2.09 [1.64–2.64]), secondary tuberculosis (OR: 2.25 [1.48–3.42]), smoking (OR: 1.40[1.02–1.93]), underlying disease (OR: 2.66 [1.53–4.62]), and use of antibiotics (OR: 2.77 [2.35–3.27]) were the main risk factors associated with HAIs among TB hospitalized patients with a statistical significance (P < 0.05). Conclusions: Age older than 60 years, presence of complications, presence of diabetes mellitus, invasive procedure, longer than 15 hospitalization days, secondary tuberculosis, smoking, presence of underlying disease, and use of antibiotics were the main risk factors which had a negative impact on HAIs among TB hospitalized patients in Chinese hospitals. These findings provided evidence for policy makers and hospital managers to make effective infection prevention and control measures to constrain the rising HAIs. It is also required that more cost-effective infection prevention and control measures should be widely applied in routinely medical treatment and clinical management to reduce the occurrence of HAIs among TB hospitalized patients

Identification and discovery of imaging genetic patterns using fusion self-expressive network in major depressive disorder

IntroductionMajor depressive disorder (MDD) is a prevalent mental illness, with severe symptoms that can significantly impair daily routines, social interactions, and professional pursuits. Recently, imaging genetics has received considerable attention for understanding the pathogenesis of human brain disorders. However, identifying and discovering the imaging genetic patterns between genetic variations, such as single nucleotide polymorphisms (SNPs), and brain imaging data still present an arduous challenge. Most of the existing MDD research focuses on single-modality brain imaging data and neglects the complex structure of brain imaging data.MethodsIn this study, we present a novel association analysis model based on a self-expressive network to identify and discover imaging genetics patterns between SNPs and multi-modality imaging data. Specifically, we first build the multi-modality phenotype network, which comprises voxel node features and connectivity edge features from structural magnetic resonance imaging (sMRI) and resting-state functional magnetic resonance imaging (rs-fMRI), respectively. Then, we apply intra-class similarity information to construct self-expressive networks of multi-modality phenotype features via sparse representation. Subsequently, we design a fusion method guided by diagnosis information, which iteratively fuses the self-expressive networks of multi-modality phenotype features into a single new network. Finally, we propose an association analysis between MDD risk SNPs and the multi-modality phenotype network based on a fusion self-expressive network.ResultsExperimental results show that our method not only enhances the association between MDD risk SNP rs1799913 and the multi-modality phenotype network but also identifies some consistent and stable regions of interest (ROIs) multi-modality biological markers to guide the interpretation of MDD pathogenesis. Moreover, 15 new potential risk SNPs highly associated with MDD are discovered, which can further help interpret the MDD genetic mechanism.DiscussionIn this study, we discussed the discriminant and convergence performance of the fusion self-expressive network, parameters, and atlas selection

The impact of COVID-19 on global health journals: An analysis of impact factor and publication trends

Background COVID-19 has affected research productivity across all areas of knowledge. Current evidence suggests that COVID-19 has had a blockbuster effect on journal impact factors (JIFs) and publication trends, while little is known on global health journals. Methods Twenty global health journals were included to analyse the impact of COVID-19 on their JIFs and publication trends. Indicator data, including numbers of publications, citations, articles with different types, etc, were extracted from journal websites and Web of Science Core Collection database. The JIFs from 2019 to 2021 were simulated for longitudinal and cross-sectional analyses. Interrupted time-series analysis and non-parametric tests were applied to assess whether COVID-19 had decreased non-COVID-19 publications from January 2018 to June 2022. Results In 2020, 615 out of 3223 publications were COVID-19 related, accounting for 19.08%. The simulated JIFs of 17 out of 20 journals in 2021 were higher than those in 2019 and 2020. Notably, 18 out of 20 journals had a decrease in their simulated JIFs after excluding COVID-19-related publications. Moreover, 10 out of 20 journals decreased their monthly numbers of non-COVID-19 publications after the COVID-19 outbreak. For all the 20 journals as a whole, after the COVID-19 outbreak in February 2020, the total number of non-COVID-19 publications significantly decreased by 14.2 compared with the previous month (p=0.013), and since then, on average, the publications had decreased by 0.6 per month until June 2022 (p<0.001). Conclusions COVID-19 has impacted the structure of COVID-19-related publications, the JIFs of global health journals and their numbers of non-COVID-19 publications. Although journals may benefit from increased JIFs, global health journals should avoid relying on a single metric. More follow-up studies including more years of data with a combination of metrics should be conducted to generate more robust evidence

Network Pharmacology Based Research on the Combination Mechanism Between Escin and Low Dose Glucocorticoids in Anti-rheumatoid Arthritis

Rheumatoid arthritis (RA) is characterized by chronic progressive symmetrical synovitis and destruction of multiple joints. Glucocorticoids (GCs) are widely used in the treatment of RA. However, their adverse effects can be serious. Escin, which is isolated from Aesculus hippocastanum L., has been reported to have anti-inflammatory effects. We investigated the anti-RA effect of Escin combined with low dose GCs (dexamethasone, Dex) and the underlying mechanism. Adjuvant-induced RA rats and lipopolysaccharides (LPS)-injured RAW264.7 cells were used to investigate the anti-RA effects of Escin combined with low dose Dex in vivo and in vitro. The results showed that Escin combined with low-dose Dex significantly decreased arthritic index, serum IL-6 and TNF-α levels, reduced paw swelling, and ameliorated the joint pathology and immune organ pathology. Gene chip results revealed that Nr3c1 (GR) expression was significantly altered, and that GR was activated by Escin and low dose Dex in vivo and in vitro. Additionally, Escin combined with low dose Dex also significantly increased GR mRNA expression. However, when GR expression was suppressed by its specific inhibitor, the anti-RA effect of Escin combined with low-dose Dex was abolished. The data in this study demonstrated that Escin combined with Dex reduced the dose of Dex, and exerted significant anti-RA effects, which could also reduce the adverse effects of Dex. This combination might result from GR activation. This study might provide a new combination of drugs for the treatment of RA

Efficient and bright warm-white electroluminescence from lead-free metal halides.

Solution-processed metal-halide perovskites are emerging as one of the most promising materials for displays, lighting and energy generation. Currently, the best-performing perovskite optoelectronic devices are based on lead halides and the lead toxicity severely restricts their practical applications. Moreover, efficient white electroluminescence from broadband-emission metal halides remains a challenge. Here we demonstrate efficient and bright lead-free LEDs based on cesium copper halides enabled by introducing an organic additive (Tween, polyethylene glycol sorbitan monooleate) into the precursor solutions. We find the additive can reduce the trap states, enhancing the photoluminescence quantum efficiency of the metal halide films, and increase the surface potential, facilitating the hole injection and transport in the LEDs. Consequently, we achieve warm-white LEDs reaching an external quantum efficiency of 3.1% and a luminance of 1570 cd m-2 at a low voltage of 5.4 V, showing great promise of lead-free metal halides for solution-processed white LED applications

Machine learning reveals neutrophil-to-lymphocyte ratio as a crucial prognostic indicator in severe Japanese encephalitis patients

Japanese encephalitis (JE) is a severe infectious disease affecting the central nervous system (CNS). However, limited risk factors have been identified for predicting poor prognosis (PP) in adults with severe JE. In this study, we analyzed clinical data from thirty-eight severe adult JE patients and compared them to thirty-three patients without organic CNS disease. Machine learning techniques employing branch-and-bound algorithms were used to identify clinical risk factors. Based on clinical outcomes, patients were categorized into two groups: the PP group (mRs ≥ 3) and the good prognosis (GP) group (mRs ≤ 2) at three months post-discharge. We found that the neutrophil-to-lymphocyte ratio (NLR) and the percentage of neutrophilic count (N%) were significantly higher in the PP group compared to the GP group. Conversely, the percentage of lymphocyte count (L%) was significantly lower in the PP group. Additionally, elevated levels of aspartate aminotransferase (AST) and blood glucose were observed in the PP group compared to the GP group. The clinical parameters most strongly correlated with prognosis, as indicated by Pearson correlation coefficient (PCC), were NLR (PCC 0.45) and blood glucose (PCC 0.45). In summary, our findings indicate that increased serum NLR, N%, decreased L%, abnormal glucose metabolism, and liver function impairment are risk factors associated with poor prognosis in severe adult JE patients

Circulating tumor DNA clearance predicts prognosis across treatment regimen in a large real-world longitudinally monitored advanced non-small cell lung cancer cohort

Background: Although growth advantage of certain clones would ultimately translate into a clinically visible disease progression, radiological imaging does not reflect clonal evolution at molecular level. Circulating tumor DNA (ctDNA), validated as a tool for mutation detection in lung cancer, could reflect dynamic molecular changes. We evaluated the utility of ctDNA as a predictive and a prognostic marker in disease monitoring of advanced non-small cell lung cancer (NSCLC) patients.Methods: This is a multicenter prospective cohort study. We performed capture-based ultra-deep sequencing on longitudinal plasma samples utilizing a panel consisting of 168 NSCLC-related genes on 949 advanced NSCLC patients with driver mutations to monitor treatment responses and disease progression. The correlations between ctDNA and progression-free survival (PFS)/overall survival (OS) were performed on 248 patients undergoing various treatments with the minimum of 2 ctDNA tests.Results: The results of this study revealed that higher ctDNA abundance (P=0.012) and mutation count (P=8.5x10(-4)) at baseline are associated with shorter OS. We also found that patients with ctDNA clearance, not just driver mutation clearance, at any point during the course of treatment were associated with longer PFS (P=2.2x10(-1)6, HR 0.28) and OS (P=4.5x10(-6), HR 0.19) regardless of type of treatment and evaluation schedule.Conclusions: This prospective real-world study shows that ctDNA clearance during treatment may serve as predictive and prognostic marker across a wide spectrum of treatment regimens

The Sihailongwan Maar Lake, northeastern China as a candidate Global Boundary Stratotype Section and Point for the Anthropocene Series

Sihailongwan Maar Lake, located in Northeast China, is a candidate Global boundary Stratotype Section and Point (GSSP) for demarcation of the Anthropocene. The lake’s varved sediments are formed by alternating allogenic atmospheric inputs and authigenic lake processes and store a record of environmental and human impacts at a continental-global scale. Varve counting and radiometric dating provided a precise annual-resolution sediment chronology for the site. Time series records of radioactive (239,240Pu, 129I and soot 14C), chemical (spheroidal carbonaceous particles, polycyclic aromatic hydrocarbons, soot, heavy metals, δ13C, etc), physical (magnetic susceptibility and grayscale) and biological (environmental DNA) indicators all show rapid changes in the mid-20th century, coincident with clear lithological changes of the sediments. Statistical analyses of these proxies show a tipping point in 1954 CE. 239,240Pu activities follow a typical unimodal globally-distributed profile, and are proposed as the primary marker for the Anthropocene. A rapid increase in 239,240Pu activities at 88 mm depth in core SHLW21-Fr-13 (1953 CE) is synchronous with rapid changes of other anthropogenic proxies and the Great Acceleration, marking the onset of the Anthropocene. The results indicate that Sihailongwan Maar Lake is an ideal site for the Anthropocene GSSP