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Purpose This paper aims to test the effects of social presence (SP) and other related factors, including trust, self-construal and brand familiarity, in affecting consumers’ purchase intention of live-streaming workout courses. Design/methodology/approach Three between-subjects, Web-based experimental studies were conducted. There were separately 108, 208 and 284 valid questionnaires collected in the three studies. Moderation and mediation analyses were performed to test the research hypotheses. Findings Users’ sense of SP when watching live-streaming courses positively affects their willingness to purchase courses, and users’ sense of trust plays a mediating role in the influence of users’ SP on their purchase intention. In addition, when users belong to dependent self-construal rather than independent self-construal, their trust in live-streaming courses plays a stronger mediating role in the influence of users’ SP on their purchase intentions. What is more, when users have high brand familiarity rather than low brand familiarity, their trust in online live-streaming plays a stronger mediating role in the influence of users’ SP on their purchase intentions. Originality/value This research delineates the effects of SP on the consumption of live-streaming courses, thus further adding to the understanding of the role of real-time interaction in determining consumer behavior. It also highlights the roles of self-construal and brand familiarity as mediating influences on the relationship between SP and consumer trust.

High-temperature laser bed powder fusion (HT-LPBF) technology is an ideal method for processing poly-ether-ether-ketone (PEEK) implants with personalized bionic structures, but the biological inertia of PEEK limits its medical applications. In this study, we evaluated the mechanical and biological properties of a novel akermanite (AKM)/PEEK composite for HT-LPBF. The results showed that tiny AKM particles are evenly attached to the surface of the PEEK particle. The delayed peak crystallization temperature and stable sintering window ensure the processing feasibility of the AKM/PEEK composites. The tensile strength and Young’s modulus are in the range of 30.83–98.73 MPa and 2.27–3.71 GPa, respectively, which can match the properties of cancellous bones and meet their implanting requirement. The CCK-8 experiments demonstrated the biocompatibility of the composites and the good proliferation of bone marrow stromal cells. The dense hydroxyapatite network layer and petal-like hydroxyapatite demonstrates biological activity, indicating that the composite has a good potential in the orthopedics fields.

人结直肠癌细胞不同处理下差异表达的基因

β-Glucuronidase (GUS) plays a key role in tumor initiation, metastasis, and progression, and thus, has been proposed as a promising cancer biomarker. In this study, we designed an enzyme-activatable near-infrared (NIR) fluorescent probe (DCM-βGlcA) for the rapid and accurate detection of GUS activity in vitro, in vivo and ex vivo. The DCM-βGlcA was prepared by linking a glucuronic acid residue to dicyanomethylene-4H-pyran (DCM). This probe exhibited significant light-up NIR fluorescent signals at 680 nm after reacting with GUS and the Stokes shift could reach 150 nm. The DCM-βGlcA showed a high sensitivity toward GUS and an excellent linear relationship at concentrations ranging between 0 and 4 U L−1 (R2 = 0.9974) with the limit of detection as low as 0.19 U L−1. We used the DCM-βGlcA to identify GUS serum levels in both cancer patients and healthy individuals with a similar accuracy as that of an enzyme-linked immunosorbent assay (ELISA) while being easier and faster to perform. Moreover, the DCM-βGlcA was used for tracking endogenous GUS in living cells, thereby discriminating GUS-overexpressed liver cancer from normal cells. Additionally, the DCM-βGlcA was able to detect and image endogenous GUS in liver cancer tissue and tumor-bearing mouse models. These findings demonstrate the potential of the DCM-βGlcA as a promising tool for detecting and monitoring GUS activity in preclinical applications.

Nitrophenols are identified as the priority organic pollutants due to the chemical stability, water solubility, persistence, and toxicity to human health and the environment. Hence, removal of nitrophenols from waste water is vitally essential. In this study, amino-rich coordination polymer Cu

Anaerobic digestion (AD) is a promising technology for recovering value-added resources from organic waste, thus achieving sustainable waste management. The performance of AD is dictated by a variety of factors including system design and operating conditions. This necessitates developing suitable modelling and optimization tools to quantify its off-design performance, where the application of machine learning (ML) and soft computing approaches have received increasing attention. Here, we succinctly reviewed the latest progress in black-box ML approaches for AD modelling with a thrust on global and local model interpretability metrics (e.g., Shapley values, partial dependence analysis, permutation feature importance). Categorical applications of the ML and soft computing approaches such as what-if scenario analysis, fault detection in AD systems, long-term operation prediction, and integration of ML with life cycle assessment are discussed. Finally, the research gaps and scopes for future work are summarized.

The authors propose a rearranging-atom algorithm with near-fewest sorting-atom moves for large-scale atom assemblers and apply to the preparation of defect-free atom arrays in compact configurations experimentally.

Optically trapped mixed-species single atom arrays with arbitrary geometries are an attractive and promising platform for various applications, because tunable quantum systems with multiple components provide extra degrees of freedom for experimental control. Here, we report the first demonstration of two-dimensional $6\times4$ dual-species atom assembly with a filling fraction of 0.88 (0.89) for $^{85}$Rb ($^{87}$Rb) atoms. This mixed-species atomic synthetic is achieved via rearranging initially randomly distributed atoms using a sorting algorithm (heuristic heteronuclear algorithm) which is proposed for bottom-up atom assembly with both user-defined geometries and two-species atom number ratios. Our fully tunable hybrid-atom system of scalable advantages is a good starting point for high-fidelity quantum logic, many-body quantum simulation and forming defect-free single molecule arrays.

Background Anti-inflammatory drug use, particularly ibuprofen, has been associated with a lower risk of Parkinson's disease. Microglial activation and inflammatory cytokine expression have been shown to be pathological features of progressive supranuclear palsy. We examined the association between NSAID use and risk of PSP, disease severity and age at onset. Methods The ENGENE-PSP multicenter case-control study recruited incident PSP cases who met the NINDS-PSP Society diagnostic criteria and age-, sex- and race-matched controls primarily from the same geographical areas. All subjects underwent standardized interviews to obtain data on demographics, residential history, medication history and lifetime occupational history. NSAID use was specifically queried by telephone interview using a standardized questionnaire. Results Information was obtained on anti-inflammatory drug exposure in 276 cases and 278 controls. No association was found between NSAID exposure and risk of PSP, age at onset or rate of change of UPDRS motor subscale, PSP Rating Scale or Mattis Dementia Rating Scale scores. This lack of association persisted when NSAID exposure was measured considering any NSAIDs, ibuprofen only, ASA only or non-ibuprofen, non-aspirin NSAIDs. Conclusions These results do not suggest an important association between NSAID use and PSP occurrence or expression. Despite the large size of our study, confidence intervals were wide. To rule out small associations, very large sample sizes will be required.

There is high demand for forensic pedigree searches with Y-chromosome short tandem repeat (Y-STR) profiling in large-scale crime investigations. However, when two Y-STR haplotypes have a few mismatched loci, it is difficult to determine if they are from the same male lineage because of the high mutation rate of Y-STRs. Here we design a new strategy to handle cases in which none of pedigree samples shares identical Y-STR haplotype. We combine next generation sequencing (NGS), capillary electrophoresis and pyrosequencing under the term ‘NGS+’ for typing Y-STRs and Y-chromosomal single nucleotide polymorphisms (Y-SNPs). The high-resolution Y-SNP haplogroup and Y-STR haplotype can be obtained with NGS+. We further developed a new data-driven decision rule, FSindex, for estimating the likelihood for each retrieved pedigree. Our approach enables positive identification of pedigree from mismatched Y-STR haplotypes. It is envisaged that NGS+ will revolutionize forensic pedigree searches, especially when the person of interest was not recorded in forensic DNA database.

The ability to identify a specific cancer using minimally invasive biopsy holds great promise for improving the diagnosis, treatment selection, and prediction of prognosis in cancer. Using whole-genome methylation data from The Cancer Genome Atlas (TCGA) and machine learning methods, we evaluated the utility of DNA methylation for differentiating tumor tissue and normal tissue for four common cancers (breast, colon, liver, and lung). We identified cancer markers in a training cohort of 1,619 tumor samples and 173 matched adjacent normal tissue samples. We replicated our findings in a separate TCGA cohort of 791 tumor samples and 93 matched adjacent normal tissue samples, as well as an independent Chinese cohort of 394 tumor samples and 324 matched adjacent normal tissue samples. The DNA methylation analysis could predict cancer versus normal tissue with more than 95% accuracy in these three cohorts, demonstrating accuracy comparable to typical diagnostic methods. This analysis also correctly identified 29 of 30 colorectal cancer metastases to the liver and 32 of 34 colorectal cancer metastases to the lung. We also found that methylation patterns can predict prognosis and survival. We correlated differential methylation of CpG sites predictive of cancer with expression of associated genes known to be important in cancer biology, showing decreased expression with increased methylation, as expected. We verified gene expression profiles in a mouse model of hepatocellular carcinoma. Taken together, these findings demonstrate the utility of methylation biomarkers for the molecular characterization of cancer, with implications for diagnosis and prognosis.

Various methods have been performed to predict an unknown individual's age from biological traces in forensic investigations. A considerably accurate age prediction for the semen donor can help narrow down the search in a sexual assault case. The aim of this study was to develop an assay for age prediction from seminal stains in Han Chinese males. We built a sperm-specific linear regression model using bisulfite pyrosequencing. Validations were conducted with a Mean Absolute Deviation from the chronological age (MAD) of 4.219 years in liquid semen, 4.158 years in fresh seminal stains, 4.393 years in aged seminal stains, and 3.880 years in mixed stains, respectively. Furthermore, our strategy enables accurate age prediction using a forensic casework sample. The strategy indicated that we produced an accurate and reliable age prediction tool for the semen donors in Han Chinese males for forensic purposes.

OBJECTIVE:This study evaluates the impact of individual surgeons and institutions on the use of mastectomy or breast conserving surgery (BCS) among elderly women with breast cancer. SUMMARY OF BACKGROUND DATA:Current literature characterizes patient clinical and demographic factors that increase likelihood of mastectomy use. However, the impact of the individual provider or institution is not well understood, and could provide key insights to biases in the decision-making process. METHODS:A retrospective cohort study of 29,358 women 65 years or older derived from the SEER-Medicare linked database with localized breast cancer diagnosed from 2000 to 2009. Multilevel, multivariable logistic models were employed, with odds ratios (ORs) used to describe the impact of demographic or clinical covariates, and the median OR (MOR) used to describe the relative impact of the surgeon and institution. RESULTS:Six thousand five hundred ninety-four women (22.4%) underwent mastectomy. Unadjusted rates of mastectomy ranged from 0% in the bottom quintile of surgeons to 58.0% in the top quintile. On multivariable analysis, the individual surgeon (MOR 1.97) had a greater impact on mastectomy than did the institution (MOR 1.71) or all other clinical and demographic variables except tumor size (OR 3.06) and nodal status (OR 2.95). Surgeons with more years in practice, or those with a lower case volume were more likely to perform mastectomy (P < 0.05). CONCLUSION:The individual surgeon influences the likelihood of mastectomy for the treatment of localized breast cancer. Further research should focus on physician-related biases that influence this decision to ensure patient autonomy.

PURPOSE Treatment decisions about localized prostate cancer depend on accurate estimation of the patient’s life expectancy. Current cancer and noncancer survival models use a limited number of predefined variables, which could restrict their predictive capability. We explored a technique to create more comprehensive survival prediction models using insurance claims data from a large administrative data set. These data contain substantial information about medical diagnoses and procedures, and thus may provide a broader reflection of each patient’s health. METHODS We identified 57,011 Medicare beneficiaries with localized prostate cancer diagnosed between 2004 and 2009. We constructed separate cancer survival and noncancer survival prediction models using a training data set and assessed performance on a test data set. Potential model inputs included clinical and demographic covariates, and 8,971 distinct insurance claim codes describing comorbid diseases, procedures, surgeries, and diagnostic tests. We used a least absolute shrinkage and selection operator technique to identify predictive variables in the final survival models. Each model’s predictive capacity was compared with existing survival models with a metric of explained randomness (ρ2) ranging from 0 to 1, with 1 indicating an ideal prediction. RESULTS Our noncancer survival model included 143 covariates and had improved survival prediction (ρ2 = 0.60) compared with the Charlson comorbidity index (ρ2 = 0.26) and Elixhauser comorbidity index (ρ2 = 0.26). Our cancer-specific survival model included nine covariates, and had similar survival predictions (ρ2 = 0.71) to the Memorial Sloan Kettering prediction model (ρ2 = 0.68). CONCLUSION Survival prediction models using high-dimensional variable selection techniques applied to claims data show promise, particularly with noncancer survival prediction. After further validation, these analyses could inform clinical decisions for men with prostate cancer.

Large container ship is a common transport ship. Nowadays, modern container ships are developing in the direction of large-scale, high-speed and multi-purpose. Their hydrodynamic performance has a great impact on the safety of the work. In this paper, using the SESAM software system of DNV classification society, taking a large container ship as an example, the hydrodynamic characteristics analysis and wave load prediction of the whole ship were carried out by establishing the finite element structural model of the whole ship. The results were compared with the results of the ship model experiment. The calculation shows that the SESAM software can relatively accurately predict the motion response of large container ships and calculate the load of waves acting on the hull, which provides a basis for ship wave resistance assessment and structural design.

Buoys traditional uses battery for energy supplement, which may have shortcomings, such as short working life, inconvenient battery replacement, etc. Therefore, renewable energy analysis becomes a hot topic. In this regard, this research proposed an energy capture device located in the buoy. It collects the kinetic energy of the buoy and stores it in an accumulator when the buoy floats on the sea surface. The hydrodynamic characteristics of the device under the fourth-order sea conditions are study by using CFD numerical simulation. It is evident that the proposed device has energy capture function.

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Background In low and middle-income countries, reliable data on the epidemiology of childhood acute kidney injury (AKI) is lacking. The Global Snapshot, conducted by the ISN “0by25” AKI initiative, was a world-wide cross-sectional, observational study to evaluate AKI in hospitalized patients. Here we report the pediatric results of this study. Patients and methods We prospectively collected data on children who met the Kidney Disease Improving Global Outcomes AKI criteria during a 10-week window in late 2014. AKI risk factors, etiological factors, management and outcomes were recorded using standardized forms and protocols. Countries were classified according to their 2014 gross national income (GNI) per person into high-income countries (HIC), upper-middle income countries (UMIC) and low and low-middle income countries (LLMIC). Need for renal replacement therapy, mortality, and renal recovery were assessed 7 days after AKI diagnosis or at hospital discharge, whichever came first. Results 92 centers from 41 countries collected data on 354 pediatric AKI patients; 53% of the children developed AKI while hospitalized and 47% in the community. The most common etiological factors for AKI differed across GNI categories as well as between patients with community-acquired vs. hospital-acquired AKI. Children from HIC were younger, and larger proportion of AKI in this group were due to post-surgical complications vs. other etiologies when compared to other income categories. In patients with hypotension as the cause of AKI, the adjusted risk of death was almost 10-fold higher compared to patients without hypotension as an etiological factor for AKI development. Mortality was similar within AKI stages in HIC and UMIC. In LLMIC, patients with the highest AKI level of severity had higher mortality than patients in higher income categories. Patients from LLMIC and UMIC had a 57-fold and 11 fold higher adjusted risk of death, respectively, compared to patients from HIC. Conclusion In resource-limited countries, pediatric AKI-associated mortality is disproportionately higher when compared to high-resource areas, especially among patients with more severe AKI.

Competing risk analysis considers event times due to multiple causes, or of more than one event types. Commonly used regression models for such data include (1) cause-specific hazards model, which focuses on modeling one type of event while acknowledging other event types simultaneously; and (2) subdistribution hazards model, which links the covariate effects directly to the cumulative incidence function. Their use and in particular statistical properties in the presence of high-dimensional predictors are largely unexplored. We study the accuracy of prediction and variable selection of existing statistical learning methods under both models using extensive simulation experiments, including different approaches to choosing penalty parameters in each method.

Competing risk analysis considers event times due to multiple causes or of more than one event types. Commonly used regression models for such data include (1) cause-specific hazards model, which focuses on modeling one type of event while acknowledging other event types simultaneously, and (2) subdistribution hazards model, which links the covariate effects directly to the cumulative incidence function. Their use in the presence of high-dimensional predictors are largely unexplored. Motivated by an analysis using the linked SEER-Medicare database for the purposes of predicting cancer versus noncancer mortality for patients with prostate cancer, we study the accuracy of prediction and variable selection of existing machine learning methods under both models using extensive simulation experiments, including different approaches to choosing penalty parameters in each method. We then apply the optimal approaches to the analysis of the SEER-Medicare data.

Purpose Despite multiple randomized trials showing the efficacy of hypofractionated radiation therapy in early-stage breast cancer, the United States has been slow to adopt this treatment. The goal of this study was to evaluate the impact of individual radiation oncologists on the early adoption of hypofractionated radiation therapy for early-stage breast cancer. Methods We identified 22,233 Medicare beneficiaries with localized breast cancer that was diagnosed from 2004 to 2011 who underwent breast-conserving surgery with adjuvant radiation. Multilevel, multivariable logistic models clustered by radiation oncologist and geographic practice area were used to determine the impact of the provider and geographic region on the likelihood of receiving hypofractionated compared with standard fractionated radiation therapy while controlling for a patient's clinical and demographic covariates. Odds ratios (OR) describe the impact of demographic or clinical covariates, and the median OR (MOR) describes the relative impact of the individual radiation oncologist and geographic region on the likelihood of undergoing hypofractionated radiation therapy. Results Among the entire cohort, 2333 women (10.4%) were treated with hypofractionated radiation therapy, with unadjusted rates ranging from 0.0% in the bottom quintile of radiation oncologists to 30.4% in the top quintile. Multivariable analysis found that the individual radiation oncologist (MOR 3.08) had a greater impact on the use of hypofractionation than did geographic region (MOR 2.10) or clinical and demographic variables. The impact of the provider increased from the year 2004 to 2005 (MOR 2.82) to the year 2010 to 2011 (MOR 3.16) despite the publication of long-term randomized trial results in early 2010. Male physician and radiation oncologists treating the highest volume of breast cancer patients were less likely to perform hypofractionation ( P Conclusions The individual radiation oncologist strongly influenced the likelihood of a patient's receiving hypofractionated radiation therapy, and this trend increased despite the publication of long-term data showing equivalence to standard fractionation. Future research should focus on physician-related factors that influence this decision.

Myelodysplastic syndromes (MDS) are heterogeneous myeloid disorders with prevalent mutations in several splicing factors, but the splicing programs linked to specific mutations or MDS in general remain to be systematically defined. We applied RASL-seq, a sensitive and cost-effective platform, to interrogate 5502 annotated splicing events in 169 samples from MDS patients or healthy individuals. We found that splicing signatures associated with normal hematopoietic lineages are largely related to cell signaling and differentiation programs, whereas MDS-linked signatures are primarily involved in cell cycle control and DNA damage responses. Despite the shared roles of affected splicing factors in the 3′ splice site definition, mutations in U2AF1, SRSF2, and SF3B1 affect divergent splicing programs, and interestingly, the affected genes fall into converging cancer-related pathways. A risk score derived from 11 splicing events appears to be independently associated with an MDS prognosis and AML transformation, suggesting potential clinical relevance of altered splicing patterns in MDS.

BACKGROUND & AIMS: Focal zone 1 steatosis, although rare in adults with nonalcoholic fatty liver disease (NAFLD), does occur in children with NAFLD. We investigated whether focal zone 1 steatosis and focal zone 3 steatosis are distinct subphenotypes of pediatric NAFLD. We aimed to determine associations between the zonality of steatosis and demographic, clinical, and histologic features in children with NAFLD. METHODS: We performed a cross-sectional study of baseline data from 813 children (age less than 18 years; mean age, 12.8±2.7 years). The subjects had biopsy-proven NAFLD and were enrolled in the NASH Clinical Research Network. Liver histology was reviewed using the Nonalcoholic Steatohepatitis Clinical Research Network scoring system. RESULTS: Zone 1 steatosis was present in 18% of children with NAFLD (n=146) and zone 3 steatosis was present in 32% (n=244). Children with zone 1 steatosis were significantly younger (10 vs 14 years; P

ObjectiveNecrotising enterocolitis (NEC) is one of the most common and often fatal intestinal disorders in preterm infants. Markers to identify at-risk infants as well as therapies to prevent and treat NEC are limited and urgently needed. NEC incidence is significantly lower in breast-fed compared with formula-fed infants. Infant formula lacks human milk oligosaccharides (HMO), such as disialyllacto-N-tetraose (DSLNT), which prevents NEC in neonatal rats. However, it is unknown if DSLNT also protects human preterm infants.DesignWe conducted a multicentre clinical cohort study and recruited 200 mothers and their very low birthweight infants that were predominantly human milk-fed. We analysed HMO composition in breast milk fed to infants over the first 28 days post partum, matched each NEC case with five controls and used logistic regression and generalised estimating equation to test the hypothesis that infants who develop NEC receive milk with less DSLNT than infants who do not develop NEC.ResultsEight infants in the cohort developed NEC (Bell stage 2 or 3). DSLNT concentrations were significantly lower in almost all milk samples in NEC cases compared with controls, and its abundance could identify NEC cases prior to onset. Aggregate assessment of DSLNT over multiple days enhanced the separation of NEC cases and control subjects.ConclusionsDSLNT content in breast milk is a potential non-invasive marker to identify infants at risk of developing NEC, and screen high-risk donor milk. In addition, DSLNT could serve as a natural template to develop novel therapeutics against this devastating disorder.