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Background The digital transformation of medical data enables health systems to leverage real‐world data from electronic health records to gain actionable insights for improving hypertension care. Methods and Results We performed a serial cross‐sectional analysis of outpatients of a large regional health system from 2010 to 2021. Hypertension was defined by systolic blood pressure ≥140 mm Hg, diastolic blood pressure ≥90 mm Hg, or recorded treatment with antihypertension medications. We evaluated 4 methods of using blood pressure measurements in the electronic health record to define hypertension. The primary outcomes were age‐adjusted prevalence rates and age‐adjusted control rates. Hypertension prevalence varied depending on the definition used, ranging from 36.5% to 50.9% initially and increasing over time by ≈5%, regardless of the definition used. Control rates ranged from 61.2% to 71.3% initially, increased during 2018 to 2019, and decreased during 2020 to 2021. The proportion of patients with a hypertension diagnosis ranged from 45.5% to 60.2% initially and improved during the study period. Non‐Hispanic Black patients represented 25% of our regional population and consistently had higher prevalence rates, higher mean systolic and diastolic blood pressure, and lower control rates compared with other racial and ethnic groups. Conclusions In a large regional health system, we leveraged the electronic health record to provide real‐world insights. The findings largely reflected national trends but showed distinctive regional demographics and findings, with prevalence increasing, one‐quarter of the patients not controlled, and marked disparities. This approach could be emulated by regional health systems seeking to improve hypertension care.

Objectives The extent to which care quality influenced outcomes for patients hospitalised with COVID-19 is unknown. Our objective was to determine if prepandemic hospital quality is associated with mortality among Medicare patients hospitalised with COVID-19.Design This is a retrospective observational study. We calculated hospital-level risk-standardised in-hospital and 30-day mortality rates (risk-standardised mortality rates, RSMRs) for patients hospitalised with COVID-19, and correlation coefficients between RSMRs and pre-COVID-19 hospital quality, overall and stratified by hospital characteristics.Setting Short-term acute care hospitals and critical access hospitals in the USA.Participants Hospitalised Medicare beneficiaries (Fee-For-Service and Medicare Advantage) age 65 and older hospitalised with COVID-19, discharged between 1 April 2020 and 30 September 2021.Intervention/exposure Pre-COVID-19 hospital quality.Outcomes Risk-standardised COVID-19 in-hospital and 30-day mortality rates (RSMRs).Results In-hospital (n=4256) RSMRs for Medicare patients hospitalised with COVID-19 (April 2020–September 2021) ranged from 4.5% to 59.9% (median 18.2%; IQR 14.7%–23.7%); 30-day RSMRs ranged from 12.9% to 56.2% (IQR 24.6%–30.6%). COVID-19 RSMRs were negatively correlated with star rating summary scores (in-hospital correlation coefficient −0.41, p

IntroductionData on ethnic and racial differences in symptoms and health-related impacts following SARS-CoV-2 infection are limited. We aimed to estimate the ethnic and racial differences in symptoms and health-related impacts 3 and 6 months after the first SARS-CoV-2 infection.MethodsParticipants included adults with SARS-CoV-2 infection enrolled in a prospective multicenter US study between 12/11/2020 and 7/4/2022 as the primary cohort of interest, as well as a SARS-CoV-2-negative cohort to account for non-SARS-CoV-2-infection impacts, who completed enrollment and 3-month surveys (N = 3,161; 2,402 SARS-CoV-2-positive, 759 SARS-CoV-2-negative). Marginal odds ratios were estimated using GEE logistic regression for individual symptoms, health status, activity level, and missed work 3 and 6 months after COVID-19 illness, comparing each ethnicity or race to the referent group (non-Hispanic or white), adjusting for demographic factors, social determinants of health, substance use, pre-existing health conditions, SARS-CoV-2 infection status, COVID-19 vaccination status, and survey time point, with interactions between ethnicity or race and time point, ethnicity or race and SARS-CoV-2 infection status, and SARS-CoV-2 infection status and time point.ResultsFollowing SARS-CoV-2 infection, the majority of symptoms were similar over time between ethnic and racial groups. At 3 months, Hispanic participants were more likely than non-Hispanic participants to report fair/poor health (OR: 1.94; 95%CI: 1.36–2.78) and reduced activity (somewhat less, OR: 1.47; 95%CI: 1.06–2.02; much less, OR: 2.23; 95%CI: 1.38–3.61). At 6 months, differences by ethnicity were not present. At 3 months, Other/Multiple race participants were more likely than white participants to report fair/poor health (OR: 1.90; 95% CI: 1.25–2.88), reduced activity (somewhat less, OR: 1.72; 95%CI: 1.21–2.46; much less, OR: 2.08; 95%CI: 1.18–3.65). At 6 months, Asian participants were more likely than white participants to report fair/poor health (OR: 1.88; 95%CI: 1.13–3.12); Black participants reported more missed work (OR, 2.83; 95%CI: 1.60–5.00); and Other/Multiple race participants reported more fair/poor health (OR: 1.83; 95%CI: 1.10–3.05), reduced activity (somewhat less, OR: 1.60; 95%CI: 1.02–2.51; much less, OR: 2.49; 95%CI: 1.40–4.44), and more missed work (OR: 2.25; 95%CI: 1.27–3.98).DiscussionAwareness of ethnic and racial differences in outcomes following SARS-CoV-2 infection may inform clinical and public health efforts to advance health equity in long-term outcomes.

Objective The aim of the present study was to detect the association between platelet glycoprotein-specific autoantibodies and the patient response to short-term high-dose dexamethasone (HD-DXM) + prednisone maintenance treatment. Methods The data from 112 adult patients newly diagnosed with ITP who were administered first-line HD-DXM + prednisone maintenance therapy between January 2016 and January 2021 were retrospectively analyzed. Results A total of 72 patients positive for platelet glycoprotein-specific antibodies were enrolled in the antibody-positive group, and 40 patients not positive for platelet glycoprotein-specific antibodies were enrolled in the antibody-negative group. In the antibody-positive group, six platelet glycoprotein-specific antibody types were found: 41.67% of the patients were anti-GP IIb/IIIa-positive only, 5.56% were anti-GP Ib/IX-positive only, 5.56% were anti-P-selectin-positive only, 19.44% were anti-GP IIb/IIIa- and anti-GP Ib/IX-positive, 16.67% were anti-GP Ib/IX- and P-selectin-positive and 11.11% were positive for all three antibodies. There was no significant difference in the overall response rate between the antibody-positive group and the antibody-negative group (94.44 versus 80.00%, p = .221). However, the CR rate was significantly higher in the antibody-positive group than in the antibody-negative group (69.44% versus 40.00%, p = .032). The logistic regression analysis revealed that platelet glycoprotein-specific antibody positivity and age were two factors that could affect patient response. Conclusions The present study discovered that adult patients newly diagnosed with ITP who had positive platelet glycoprotein-specific antibody test results were likely to achieve a better response after treatment with HD-DXM + prednisone maintenance.

Cassava is an important tropical cash crop. Severe drought stresses affect cassava productivity and quality, and cause great economic losses in agricultural production. Enhancing the drought tolerance of cassava can effectively improve its yield. Long non-coding RNAs (lncRNAs) are present in a wide variety of eukaryotes. Recently, increasing evidence has shown that lncRNAs play a critical role in the responses to abiotic stresses. However, the function of cassava lncRNAs in the drought response remains largely unknown. In this study, we identified a novel lncRNA, DROUGHT-INDUCED INTERGENIC lncRNA (DIR). Gene expression analysis showed that DIR was significantly induced by drought stress treatment, but did not respond to abscisic acid (ABA) or jasmonic acid (JA) treatments. In addition, overexpression of the DIR gene enhanced proline accumulation and drought tolerance in transgenic cassava. RNA-seq analysis revealed that DIR preferentially affected drought-related genes that were linked to transcription and metabolism. Moreover, RNA pull-down mass spectrometry analysis showed that DIR interacted with 325 proteins. A protein–protein interaction (PPI) analysis found a marked enrichment in proteins associated with the mRNA export and protein quality control pathways. Collectively, these results suggest that DIR and its interacting proteins that regulate mRNA or protein metabolism are involved in mediating the drought stress response. Thus, regulating DIR expression has potential for improving cassava yield under drought conditions.

Abstract Background To estimate, prior to finalization of claims, the national monthly numbers of admissions and rates of 30-day readmissions and post-discharge observation-stays for Medicare fee-for-service beneficiaries hospitalized with acute myocardial infarction (AMI), heart failure (HF), or pneumonia. Methods The centers for Medicare & Medicaid Services (CMS) Integrated Data Repository, including the Medicare beneficiary enrollment database, was accessed in June 2015, February 2017, and February 2018. We evaluated patterns of delay in Medicare claims accrual, and used incomplete, non-final claims data to develop and validate models for real-time estimation of admissions, readmissions, and observation stays. Results These real-time reporting models accurately estimate, within 2 months from admission, the monthly numbers of admissions, 30-day readmission and observation-stay rates for patients with AMI, HF, or pneumonia. Conclusions This work will allow CMS to track the impact of policy decisions in real time and enable hospitals to better monitor their performance nationally.

Abstract Background Efforts to decrease hospitalization costs could increase post-acute care costs. This effect could undermine initiatives to reduce overall episode costs and have implications for the design of health care under alternative payment models. Methods Among Medicare fee-for-service beneficiaries aged ≥65 years hospitalized with acute myocardial infarction (AMI) between July 2010 and June 2013 in the Premier Healthcare Database, we studied the association of in-hospital and post-acute care resource utilization and outcomes by in-hospital cost tertiles. Results Among patients with AMI at 326 hospitals, the median (range) of each hospital’s mean per-patient in-hospital risk-standardized cost (RSC) for the low, medium, and high cost tertiles were $16,257 ($13,097–$17,648), $18,544 ($17,663–$19,875), and $21,831 ($19,923–$31,296), respectively. There was no difference in the median (IQR) of risk-standardized post-acute payments across cost-tertiles: $5014 (4295-6051), $4980 (4349-5931) and $4922 (4056-5457) for the low (n = 90), medium (n = 98), and high (n = 86) in-hospital RSC tertiles (p = 0.21), respectively. In-hospital and 30-day mortality rates did not differ significantly across the in-hospital RSC tertiles; however, 30-day readmission rates were higher at hospitals with higher in-hospital RSCs: median = 17.5, 17.8, and 18.0% at low, medium, and high in-hospital RSC tertiles, respectively (p = 0.005 for test of trend across tertiles). Conclusions In our study of patients hospitalized with AMI, greater resource utilization during the hospitalization was not associated with meaningful differences in costs or mortality during the post-acute period. These findings suggest that it may be possible for higher cost hospitals to improve efficiency in care without increasing post-acute care utilization or worsening outcomes.

Background: Despite the rapid growth in the incidence of acute myocardial infarction (AMI) in China, there is limited information about patients′ experiences after AMI hospitalization, especially on long-term adverse events and patient-reported outcomes (PROs). Methods: The China Patient-centered Evaluative Assessment of Cardiac Events (PEACE)-Prospective AMI Study will enroll 4000 consecutive AMI patients from 53 diverse hospitals across China and follow them longitudinally for 12 months to document their treatment, recovery, and outcomes. Details of patients′ medical history, treatment, and in-hospital outcomes are abstracted from medical charts. Comprehensive baseline interviews are being conducted to characterize patient demographics, risk factors, presentation, and healthcare utilization. As part of these interviews, validated instruments are administered to measure PROs, including quality of life, symptoms, mood, cognition, and sexual activity. Follow-up interviews, measuring PROs, medication adherence, risk factor control, and collecting hospitalization events are conducted at 1, 6, and 12 months after discharge. Supporting documents for potential outcomes are collected for adjudication by clinicians at the National Coordinating Center. Blood and urine samples are also obtained at baseline, 1- and 12-month follow-up. In addition, we are conducting a survey of participating hospitals to characterize their organizational characteristics. Conclusion: The China PEACE-Prospective AMI study will be uniquely positioned to generate new information regarding patient′s experiences and outcomes after AMI in China and serve as a foundation for quality improvement activities.

BACKGROUND:The current acute kidney injury (AKI) risk prediction model for patients undergoing percutaneous coronary intervention (PCI) from the American College of Cardiology (ACC) National Cardiovascular Data Registry (NCDR) employed regression techniques. This study aimed to evaluate whether models using machine learning techniques could significantly improve AKI risk prediction after PCI. METHODS AND FINDINGS:We used the same cohort and candidate variables used to develop the current NCDR CathPCI Registry AKI model, including 947,091 patients who underwent PCI procedures between June 1, 2009, and June 30, 2011. The mean age of these patients was 64.8 years, and 32.8% were women, with a total of 69,826 (7.4%) AKI events. We replicated the current AKI model as the baseline model and compared it with a series of new models. Temporal validation was performed using data from 970,869 patients undergoing PCIs between July 1, 2016, and March 31, 2017, with a mean age of 65.7 years; 31.9% were women, and 72,954 (7.5%) had AKI events. Each model was derived by implementing one of two strategies for preprocessing candidate variables (preselecting and transforming candidate variables or using all candidate variables in their original forms), one of three variable-selection methods (stepwise backward selection, lasso regularization, or permutation-based selection), and one of two methods to model the relationship between variables and outcome (logistic regression or gradient descent boosting). The cohort was divided into different training (70%) and test (30%) sets using 100 different random splits, and the performance of the models was evaluated internally in the test sets. The best model, according to the internal evaluation, was derived by using all available candidate variables in their original form, permutation-based variable selection, and gradient descent boosting. Compared with the baseline model that uses 11 variables, the best model used 13 variables and achieved a significantly better area under the receiver operating characteristic curve (AUC) of 0.752 (95% confidence interval [CI] 0.749-0.754) versus 0.711 (95% CI 0.708-0.714), a significantly better Brier score of 0.0617 (95% CI 0.0615-0.0618) versus 0.0636 (95% CI 0.0634-0.0638), and a better calibration slope of observed versus predicted rate of 1.008 (95% CI 0.988-1.028) versus 1.036 (95% CI 1.015-1.056). The best model also had a significantly wider predictive range (25.3% versus 21.6%, p < 0.001) and was more accurate in stratifying AKI risk for patients. Evaluated on a more contemporary CathPCI cohort (July 1, 2015-March 31, 2017), the best model consistently achieved significantly better performance than the baseline model in AUC (0.785 versus 0.753), Brier score (0.0610 versus 0.0627), calibration slope (1.003 versus 1.062), and predictive range (29.4% versus 26.2%). The current study does not address implementation for risk calculation at the point of care, and potential challenges include the availability and accessibility of the predictors. CONCLUSIONS:Machine learning techniques and data-driven approaches resulted in improved prediction of AKI risk after PCI. The results support the potential of these techniques for improving risk prediction models and identification of patients who may benefit from risk-mitigation strategies.

BackgroundSPRINT (Systolic Blood Pressure Intervention Trial) and the ACCORD (Action to Control Cardiovascular Risk in Diabetes) blood pressure trial used similar interventions but produced discordant results. We investigated whether differences in systolic blood pressure (SBP) response contributed to the discordant trial results. Methods and ResultsWe evaluated the distributions of SBP response during the first year for the intensive and standard treatment groups of SPRINT and ACCORD using growth mixture models. We assessed whether significant differences existed between trials in the distributions of SBP achieved at 1 year and the treatment‐independent relationships of achieved SBP with risks of primary outcomes defined in each trial, heart failure, stroke, and all‐cause death. We examined whether visit‐to‐visit variability was associated with heterogeneous treatment effects. Among the included 9027 SPRINT and 4575 ACCORD participants, the difference in mean SBP achieved between treatment groups was 15.7 mm Hg in SPRINT and 14.2 mm Hg in ACCORD, but SPRINT had significantly less between‐group overlap in the achieved SBP (standard deviations of intensive and standard groups, respectively: 6.7 and 5.9 mm Hg in SPRINT versus 8.8 and 8.2 mm Hg in ACCORD; P

Identifying temporal variation in hospitalization rates may provide insights about disease patterns and thereby inform research, policy, and clinical care. However, the majority of medical conditions have not been studied for their potential seasonal variation. The objective of this study was to apply a data-driven approach to characterize temporal variation in condition-specific hospitalizations. Using a dataset of 34 million inpatient discharges gathered from hospitals in New York State from 2008-2011, we grouped all discharges into 263 clinical conditions based on the principal discharge diagnosis using Clinical Classification Software in order to mitigate the limitation that administrative claims data reflect clinical conditions to varying specificity. After applying Seasonal-Trend Decomposition by LOESS, we estimated the periodicity of the seasonal component using spectral analysis and applied harmonic regression to calculate the amplitude and phase of the condition's seasonal utilization pattern. We also introduced four new indices of temporal variation: mean oscillation width, seasonal coefficient, trend coefficient, and linearity of the trend. Finally, K-means clustering was used to group conditions across these four indices to identify common temporal variation patterns. Of all 263 clinical conditions considered, 164 demonstrated statistically significant seasonality. Notably, we identified conditions for which seasonal variation has not been previously described such as ovarian cancer, tuberculosis, and schizophrenia. Clustering analysis yielded three distinct groups of conditions based on multiple measures of seasonal variation. Our study was limited to New York State and results may not directly apply to other regions with distinct climates and health burden. A substantial proportion of medical conditions, larger than previously described, exhibit seasonal variation in hospital utilization. Moreover, the application of clustering tools yields groups of clinically heterogeneous conditions with similar seasonal phenotypes. Further investigation is necessary to uncover common etiologies underlying these shared seasonal phenotypes.

Background The availability of hospital cardiac services may vary between hospitals and influence care processes and outcomes. However, data on available cardiac services are restricted to a limited number of services collected by the American Hospital Association (AHA) annual survey. We developed an alternative method to identify hospital services using individual patient discharge data for acute myocardial infarction (AMI) in the Premier Healthcare Database. Methods and Results Thirty‐five inpatient cardiac services relevant for AMI care were identified using American Heart Association/American College of Cardiology guidelines. Thirty‐one of these services could be defined using patient‐level administrative data codes, such as International Classification of Diseases, Ninth Revision, Clinical Modification and Current Procedural Terminology codes. A hospital was classified as providing a service if it had ≥5 instances for the service in the Premier database from 2009 to 2011. Using this system, the availability of these services among 432 Premier hospitals ranged from 100% (services such as chest X‐ray) to 1.2% (heart transplant service). To measure the accuracy of this method using administrative data, we calculated agreement between the AHA survey and Premier for a subset of 16 services defined by both sources. There was a high percentage of agreement (≥80%) for 11 of 16 (68.8%) services, moderate agreement for 3 of 16 (18.8%) services, and low agreement (≤50%) for 2 of 16 services (12.5%). Conclusions The availability of cardiac services for AMI care varies widely among hospitals. Using individual patient discharge data is a feasible method to identify these cardiac services, particularly for those services pertaining to inpatient care.

For meeting the forecasting target of key technology indicators in the flotation process, a BP neural network soft-sensor model based on features extraction of flotation froth images and optimized by shuffled cuckoo search algorithm is proposed. Based on the digital image processing technique, the color features in HSI color space, the visual features based on the gray level cooccurrence matrix, and the shape characteristics based on the geometric theory of flotation froth images are extracted, respectively, as the input variables of the proposed soft-sensor model. Then the isometric mapping method is used to reduce the input dimension, the network size, and learning time of BP neural network. Finally, a shuffled cuckoo search algorithm is adopted to optimize the BP neural network soft-sensor model. Simulation results show that the model has better generalization results and prediction accuracy.

For meeting the real-time fault diagnosis and the optimization monitoring requirements of the polymerization kettle in the polyvinyl chloride resin (PVC) production process, a fault diagnosis strategy based on the self-organizing map (SOM) neural network is proposed. Firstly, a mapping between the polymerization process data and the fault pattern is established by analyzing the production technology of polymerization kettle equipment. The particle swarm optimization (PSO) algorithm with a new dynamical adjustment method of inertial weights is adopted to optimize the structural parameters of SOM neural network. The fault pattern classification of the polymerization kettle equipment is to realize the nonlinear mapping from symptom set to fault set according to the given symptom set. Finally, the simulation experiments of fault diagnosis are conducted by combining with the industrial on-site historical data of the polymerization kettle and the simulation results show that the proposed PSO-SOM fault diagnosis strategy is effective.

Heart failure as recognized and treated in typical practice may represent a complex condition that defies discrete categorizations. To illuminate this complexity, we examined treatment strategies for patients hospitalized and treated for decompensated heart failure. We focused on the receipt of medications appropriate for other acute conditions associated with shortness of breath including acute asthma, pneumonia, and exacerbated chronic obstructive pulmonary disease.Using Premier Perspective(®), we studied adults hospitalized with a principal discharge diagnosis of heart failure and evidence of acute heart failure treatment from 2009-2010 at 370 US hospitals. We determined treatment with acute respiratory therapies during the initial 2 days of hospitalization and daily during hospital days 3-5. We also calculated adjusted odds of in-hospital death, admission to the intensive care unit, and late intubation (intubation after hospital day 2). Among 164,494 heart failure hospitalizations, 53% received acute respiratory therapies during the first 2 hospital days: 37% received short-acting inhaled bronchodilators, 33% received antibiotics, and 10% received high-dose corticosteroids. Of these 87,319 hospitalizations, over 60% continued receiving respiratory therapies after hospital day 2. Respiratory treatment was more frequent among the 60,690 hospitalizations with chronic lung disease. Treatment with acute respiratory therapy during the first 2 hospital days was associated with higher adjusted odds of all adverse outcomes.Acute respiratory therapy is administered to more than half of patients hospitalized with and treated for decompensated heart failure. Heart failure is therefore regularly treated as a broader cardiopulmonary syndrome rather than as a singular cardiac condition.

Background: New methods such as machine learning techniques have been increasingly used to enhance the performance of risk predictions for clinical decision-making. However, commonly reported performance metrics may not be sufficient to capture the advantages of these newly proposed models for their adoption by health care professionals to improve care. Machine learning models often improve risk estimation for certain subpopulations that may be missed by these metrics.Methods and Results: This article addresses the limitations of commonly reported metrics for performance comparison and proposes additional metrics. Our discussions cover metrics related to overall performance, discrimination, calibration, resolution, reclassification, and model implementation. Models for predicting acute kidney injury after percutaneous coronary intervention are used to illustrate the use of these metrics.Conclusions: We demonstrate that commonly reported metrics may not have sufficient sensitivity to identify improvement of machine learning models and propose the use of a comprehensive list of performance metrics for reporting and comparing clinical risk prediction models. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic has disproportionately affected Hispanic or Latino, non-Hispanic Black (Black), non-Hispanic American Indian or Alaska Native (AI/AN), and non-Hispanic Native Hawaiian or Other Pacific Islander (NH/PI) populations in the United States. These populations have experienced higher rates of infection and mortality compared with the non-Hispanic White (White) population (1-5) and greater excess mortality (i.e., the percentage increase in the number of persons who have died relative to the expected number of deaths for a given place and time) (6). A limitation of existing research on excess mortality among racial/ethnic minority groups has been the lack of adjustment for age and population change over time. This study assessed excess mortality incidence rates (IRs) (e.g., the number of excess deaths per 100,000 person-years) in the United States during December 29, 2019-January 2, 2021, by race/ethnicity and age group using data from the National Vital Statistics System. Among all assessed racial/ethnic groups (non-Hispanic Asian [Asian], AI/AN, Black, Hispanic, NH/PI, and White populations), excess mortality IRs were higher among persons aged ≥65 years (426.4 to 1033.5 excess deaths per 100,000 person-years) than among those aged 25-64 years (30.2 to 221.1) and those aged <25 years (-2.9 to 14.1). Among persons aged <65 years, Black and AI/AN populations had the highest excess mortality IRs. Among adults aged ≥65 years, Black and Hispanic persons experienced the highest excess mortality IRs of >1,000 excess deaths per 100,000 person-years. These findings could help guide more tailored public health messaging and mitigation efforts to reduce disparities in mortality associated with the COVID-19 pandemic in the United States,* by identifying the racial/ethnic groups and age groups with the highest excess mortality rates. [ABSTRACT FROM AUTHOR]

Maritime transportation is a crucial component of international cargo transport, offering several advantages, such as route flexibility, large capacity, and cost-effectiveness. The competition and collaboration among the node enterprises in the sea-cargo supply chain system (SCSCS) directly impact the overall structure and efficiency of the supply chain system, introducing complexity in analysis. This research focuses on a two-level SCSCS comprising one shipping company and two competing freight forwarders, considering their altruistic preferences manifested through contributing to the shipping company’s brand building. Employing a Stackelberg game model, this study examines the effects of the shipping company’s brand investment willingness and the freight forwarders’ altruistic preferences on the decision making and profits of all stakeholders. The findings reveal that a higher willingness of the shipping company to invest in its brand building leads to increased profits for all parties involved. However, while the altruistic behaviors of the freight forwarders can enhance the shipping company’s profits, their own profits may not necessarily see the same impact. Furthermore, moderate competition between the freight forwarders can enhance the profits for all members. This research identifies the circumstances in which the freight forwarders’ altruistic preferences can lead to increased profits for themselves, achieving both altruistic and self-interested outcomes.

It is of great significance to exert the synergistic effect between single atom and support. Here, the authors prepare polyoxometalates-stabilized single-atom site catalysts in confined space with enhanced activity for alkynes diboration.

Among eukaryotic transcription factors, the basic leucine zipper (bZIP) transcription factor is the most widely distributed and the most conserved protein. Alfalfa (Medicago sativa) is the largest forage crop in the world. However, few studies have evaluated the bZIP family in Alfalfa. In this study, bZIP genes in Alfalfa were identified, with 57 MsbZIPs being distributed into 11 sub-families in the phylogenetic tree. Motif and gene structure analysis revealed that the eleven subfamilies had similar motifs and gene structures, 15 cis-acting elements in MsbZIPs, including eight elements in hormones and four in plant stress resistance; 57 MsbZIPs were localized on 8 chromosomes, among which chr3 and chr4 had more collinearities, and MsbZIPs had more collinearity with the soybean. Tissue specific expression revealed that MsbZIPs were highly expressed in leaves and roots. These findings formed a foundation for further studies on functional characteristics, evolution and biological functions of bZIP transcription factors in alfalfa.

Background: Machine learning methods may complement traditional analytic methods for medical device surveillance. Methods and results: Using data from the National Cardiovascular Data Registry for implantable cardioverter-defibrillators (ICDs) linked to Medicare administrative claims for longitudinal follow-up, we applied three statistical approaches to safety-signal detection for commonly used dual-chamber ICDs that used two propensity score (PS) models: one specified by subject-matter experts (PS-SME), and the other one by machine learning-based selection (PS-ML). The first approach used PS-SME and cumulative incidence (time-to-event), the second approach used PS-SME and cumulative risk (Data Extraction and Longitudinal Trend Analysis [DELTA]), and the third approach used PS-ML and cumulative risk (embedded feature selection). Safety-signal surveillance was conducted for eleven dual-chamber ICD models implanted at least 2,000 times over 3 years. Between 2006 and 2010, there were 71,948 Medicare fee-for-service beneficiaries who received dual-chamber ICDs. Cumulative device-specific unadjusted 3-year event rates varied for three surveyed safety signals: death from any cause, 12.8%-20.9%; nonfatal ICDrelated adverse events, 19.3%-26.3%; and death from any cause or nonfatal ICD-related adverse event, 27.1%-37.6%. Agreement among safety signals detected/not detected between the time-to-event and DELTA approaches was 90.9% (360 of 396, k=0.068), between the time-to-event and embedded feature-selection approaches was 91.7% (363 of 396, k=-0.028), and between the DELTA and embedded feature selection approaches was 88.1% (349 of 396, k=-0.042). Conclusion: Three statistical approaches, including one machine learning method, identified important safety signals, but without exact agreement. Ensemble methods may be needed to detect all safety signals for further evaluation during medical device surveillance. [ABSTRACT FROM AUTHOR]

Randomized trials of hypertension have seldom examined heterogeneity in response to treatments over time and the implications for cardiovascular outcomes. Understanding this heterogeneity, however, is a necessary step toward personalizing antihypertensive therapy. We applied trajectory-based modeling to data on 39 763 study participants of the ALLHAT (Antihypertensive and Lipid-Lowering Treatment to Prevent Heart Attack Trial) to identify distinct patterns of systolic blood pressure (SBP) response to randomized medications during the first 6 months of the trial. Two trajectory patterns were identified: immediate responders (85.5%), on average, had a decreasing SBP, whereas nonimmediate responders (14.5%), on average, had an initially increasing SBP followed by a decrease. Compared with those randomized to chlorthalidone, participants randomized to amlodipine (odds ratio, 1.20; 95% confidence interval [CI], 1.10-1.31), lisinopril (odds ratio, 1.88; 95% CI, 1.73-2.03), and doxazosin (odds ratio, 1.65; 95% CI, 1.52-1.78) had higher adjusted odds ratios associated with being a nonimmediate responder (versus immediate responder). After multivariable adjustment, nonimmediate responders had a higher hazard ratio of stroke (hazard ratio, 1.49; 95% CI, 1.21-1.84), combined cardiovascular disease (hazard ratio, 1.21; 95% CI, 1.11-1.31), and heart failure (hazard ratio, 1.48; 95% CI, 1.24-1.78) during follow-up between 6 months and 2 years. The SBP response trajectories provided superior discrimination for predicting downstream adverse cardiovascular events than classification based on difference in SBP between the first 2 measurements, SBP at 6 months, and average SBP during the first 6 months. Our findings demonstrate heterogeneity in response to antihypertensive therapies and show that chlorthalidone is associated with more favorable initial response than the other medications. [ABSTRACT FROM AUTHOR]

With the globalization of supply chains, intermodal operations have become a key player in transportation. In real life, the consumer's demands are often uncertain. The recent literature is rich of papers that either emphasizes on uncertain customer demands or intermodal transportation planning. In this paper, we combine these two fields in order to identify new potentials for saving cost and improving the efficiency of intermodal logistics network. A two stage stochastic programming model was proposed to decide the terminal location and transportation routes under uncertain customer demands. And the AP data set is used to test the presented model. The result is compared with the traditional counterparts, which shows superiority in solution quantity under uncertain environment. [ABSTRACT FROM AUTHOR]

Mathematical model of electroslag remelting (ESR) process is established based on its technique features and dynamic characteristics. A new multivariable self-tuning PID controller tuned optimally by an improved cuckoo search algorithm is proposed to control the two-input-two output (TITO) ESR process. In order to improve the convergence velocity and optimization accuracy of cuckoo search (CS) algorithm, a new searching mechanism with a learning-evolving searching guider is proposed by combining the learning-evolving thought with Gaussian distribution. Then the new searching mechanism is combined with the Levy Flight searching mechanism according to a selection probability so as to form a new searching mechanism cuckoo search (MCS) algorithm. For solving the problem that the search space size and the scope cannot be decided exactly, a self-tuning dynamic searching space strategy is put forward. The search space is adjusted dynamically by following the best bird's nest location. Finally, the new searching mechanism cuckoo search algorithm with self-tuning dynamic search space (DMCS) is applied to the multivariable self-tuning PID decoupling controller. The simulation results show that the proposed control strategy can overcome dynamic conditions and the coupling problem of the system within a wide range, and it has an excellent control quality, stronger robustness and good working adaptability. [ABSTRACT FROM AUTHOR]

Cuckoo search (CS) algorithm is a new biological heuristic algorithm and simulates the cuckoo's seeking nest and spawning behavior and introduces levy flight mechanism. In order to improve the convergence velocity and optimization accuracy of cuckoo search algorithm, by combining the learning-evolving thought with Gaussian distribution, a new searching mechanism that with a learning-evolving searching guider is proposed. Then combining the new searching mechanism proposed with the Levy Flight searching mechanism according to a selection probability, a new searching mechanism cuckoo search (MCS) algorithm is formed. The result of functions testing under different dimensions proves the superiority of new proposed MCS algorithm. [ABSTRACT FROM AUTHOR]

AIM: To evaluate whether recombinant complement factor B (CFB) short hairpin RNA (shRNA) reduces laser-induced choroidal neovascularization (CNV) in rats. METHODS: Laser-induced rat CNV model was established, and then the animals underwent fundus fluorescence angiography (FFA) and hematoxylin and eosin (HE) staining. On day 3 and 7 after photocoagulation, the expression of CFB and membrane attack complex (MAC) was detected by immunhischemistry. A recombinant CFB-shRNA plasmid was constructed. CFB and scrambled shRNA plasmids were intravenous injected into rats via the tail vein on the day of laser treatment, respectively. On day 7, the incidence of CNV was determined by FFA, and the expression of CFB and vascular endothelial growth factor (VEGF) in retinal pigment epithelium (RPE)/choroidal tissues was detected by immunhischemistry, Western blot and/or semi-quantitative reverse transcription-polymerase chain reaction (RT-PCR) in CFB and scrambled shRNA groups. The possible adverse effects of CFB-shRNA injection were assessed by transmission electron microscopy and electroretinography. RESULTS: FFA and HE results indicated that a laser-induced rat CNV model was successfully established on day 7 after photocoagulation. The expression of CFB and MAC was extremely weak in normal retina and choroid, and increased on day 3 after photocoagulation. However, it started to reduce on day 7. CFB shRNA plasmid was successfully constructed and induced CFB knockdown in the retinal and choroidal tissues. FFA showed CFB knockdown significantly inhibited incidence of CNV in rats. Moreover, CFB knockdown significantly inhibited the expression of VEGF in RPE/choroidal tissues. CFB shRNA caused no obvious side effects in eyes. CONCLUSION: CFB knockdown significantly inhibits the formation and development of CNV in vivo through reducing the expression of VEGF, which is a potential therapy target. The alternative pathway of complement activation plays an important role in CNV formation.

Background: The current ability to predict readmissions in patients with heart failure is modest at best. It is unclear whether machine learning techniques that address higher dimensional, nonlinear relationships among variables would enhance prediction. We sought to compare the effectiveness of several machine learning algorithms for predicting readmissions.Methods and Results: Using data from the Telemonitoring to Improve Heart Failure Outcomes trial, we compared the effectiveness of random forests, boosting, random forests combined hierarchically with support vector machines or logistic regression (LR), and Poisson regression against traditional LR to predict 30- and 180-day all-cause readmissions and readmissions because of heart failure. We randomly selected 50% of patients for a derivation set, and a validation set comprised the remaining patients, validated using 100 bootstrapped iterations. We compared C statistics for discrimination and distributions of observed outcomes in risk deciles for predictive range. In 30-day all-cause readmission prediction, the best performing machine learning model, random forests, provided a 17.8% improvement over LR (mean C statistics, 0.628 and 0.533, respectively). For readmissions because of heart failure, boosting improved the C statistic by 24.9% over LR (mean C statistic 0.678 and 0.543, respectively). For 30-day all-cause readmission, the observed readmission rates in the lowest and highest deciles of predicted risk with random forests (7.8% and 26.2%, respectively) showed a much wider separation than LR (14.2% and 16.4%, respectively).Conclusions: Machine learning methods improved the prediction of readmission after hospitalization for heart failure compared with LR and provided the greatest predictive range in observed readmission rates. [ABSTRACT FROM AUTHOR]

Abstract Background Accumulating evidence shows that circular RNAs (circRNAs) plays vital roles in tumor progression. However, the biological functions of circRNAs in laryngeal squamous cell carcinoma (LSCC) metastasis is still unclear. Methods qRT-PCR was used to detect circFLNA, miRNAs and FLNA mRNA expression. Transwell assay and western blot were performed to evaluate cell migration ability and to detect FLNA, MMP2 and MLK1 protein expression, respectively. RNA pull-down analysis was used to find the binding-miRNAs of circFLNA. Luciferase reporter assay was used to examine the effect of circFLNA on miRNAs and miR-486-3p on FLNA expression. Results In this study, we confirmed that a Filamin A (FLNA)-derived hsa_circ_0092012 known as circFLNA, was upregulated in LSCC, and the higher expression of circFLNA was correlated with LSCC lymph node metastasis. Increased circFLNA facilitates LSCC cell migration ability through upregulating FLNA and MMP2 protein expression. Mechanistically, we find that circFLNA sponges miR-486-3p in LSCC cells, relieving miR-486-3p-induced repression of FLNA which promotes LSCC cell migration. Accordingly, FLNA mRNA is overexpressed in LSCC tissues and a higher FLNA level is correlated with poor survival. Dysregulation of the circFLNA/miR-486-3p/FLNA regulatory pathway contributes to LSCC migration. Conclusions In summary, our study sheds light on the regulatory mechanism of circFLNA in LSCC migration via sponging miR‐486-3p, which downregulates the FLNA protein expression. Targeting circFLNA/miR-486-3p/FLAN axis provides a potential therapeutic target for aggressive LSCC.