To send content items to your account,
please confirm that you agree to abide by our usage policies.
If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account.
Find out more about sending content to .
To send content items to your Kindle, first ensure email@example.com
is added to your Approved Personal Document E-mail List under your Personal Document Settings
on the Manage Your Content and Devices page of your Amazon account. Then enter the ‘name’ part
of your Kindle email address below.
Find out more about sending to your Kindle.
Note you can select to send to either the @free.kindle.com or @kindle.com variations.
‘@free.kindle.com’ emails are free but can only be sent to your device when it is connected to wi-fi.
‘@kindle.com’ emails can be delivered even when you are not connected to wi-fi, but note that service fees apply.
Maternal supraphysiological estradiol (E2) environment during pregnancy leads to adverse perinatal outcomes. However, the influence of oocyte exposure to high E2 levels on perinatal outcomes remains unknown. Thus, a retrospective cohort study was conducted to explore the effect of high E2 level induced by controlled ovarian stimulation (COH) on further outcomes after frozen embryo transfer (FET). The study included all FET cycles (n = 10,581) between 2014 and 2017. All cycles were categorized into three groups according to the E2 level on the day of the human Chorionic Gonadotropin trigger. Odds ratios (ORs) and their confidence intervals (CIs) were calculated to evaluate the association between E2 level during COH and pregnancy outcomes and subsequent neonatal outcomes. From our findings, higher E2 level was associated with lower percentage of chemical pregnancy, clinical pregnancy, ongoing pregnancy, and live birth as well as increased frequency of early miscarriage. Preterm births were more common among singletons in women with higher E2 level during COH (aOR1 = 1.93, 95% CI: 1.22–3.06; aOR2 = 2.05, 95% CI: 1.33–3.06). Incidence of small for gestational age (SGA) was more common in both singletons (aOR1 = 2.01, 95% CI: 1.30–3.11; aOR2 = 2.51, 95% CI: 1.69–3.74) and multiples (aOR1 = 1.58, 95% CI: 1.03–2.45; aOR2 = 1.99, 95% CI: 1.05–3.84) among women with relatively higher E2 level. No association was found between high E2 level during COH and the percentage of macrosomia or large for gestational age. In summary, oocyte exposure to high E2 level during COH should be brought to our attention, since the pregnancy rate decreasing and the risk of preterm birth and SGA increasing following FET.
Previous studies have shown that the Dietary Approaches to Stop Hypertension (DASH) diet might contribute to managing risk factors of non-alcoholic fatty liver disease (NAFLD), but evidence is limited. We examined the association of DASH diet score (DASH-DS) with NAFLD, as well as the intermediary effects of serum retinol-binding protein-4 (RBP4), serum high-sensitivity C-reactive protein (hs-CRP), serum TAG, homeostasis model assessment of insulin resistance (HOMA-IR) and BMI.
We performed a cross-sectional analysis of a population-based cohort study. Dietary data and lifestyle factors were assessed by face-to-face interviews and the DASH-DS was then calculated. We assessed serum RBP4, hs-CRP and TAG and calculated HOMA-IR. The presence and degree of NAFLD were determined by abdominal sonography.
Guangzhou Nutrition and Health Study participants, aged 40–75 years at baseline (n 3051).
After adjusting for potential covariates, we found an inverse association between DASH-DS and the presence of NAFLD (Ptrend = 0·009). The OR (95 % CI) of NAFLD for quintiles 2–5 were 0·78 (0·62, 0·98), 0·74 (0·59, 0·94), 0·69 (0·55, 0·86) and 0·77 (0·61, 0·97), respectively. Path analyses indicated that a higher DASH-DS was associated with lower serum RBP4, hs-CRP, TAG, HOMA-IR and BMI, which were positively associated with the degree of NAFLD.
Adherence to the DASH diet was independently associated with a marked lower prevalence of NAFLD in Chinese adults, especially in women and those without abdominal obesity, and might be mediated by reducing RBP4, hs-CRP, TAG, HOMA-IR and BMI.
Discovering knowledge from data is a quantum jump from quantity to quality, which is the characteristic and the spirit of the development of science. Symbolic regression (SR) is playing a greater role in the discovery of knowledge from data, specifically in this era of exponential data growth, because SRs are able to discover mathematical formulas from data. These formulas may provide scientifically meaningful models, especially when combined with domain knowledge. This article provides an overview of SR applications in the field of materials science and engineering. Integrating domain knowledge with SR is the key and a crucial approach, which allows gaining knowledge from data quickly, accurately, and scientifically. In the data-driven paradigm, SR allows for uncovering the underlying mechanisms of materials behavior, properties, and functions, in a wide range of areas from basic academic research to industrial applications, including experiments and computations, by providing explicit interpretable models from data, in comparison with other machine-learning “black-box” models. SR will be a powerful tool for rational and automatic materials development.
Population-based colorectal cancer (CRC) screening programs that use a fecal immunochemical test (FIT) are often faced with a noncompliance issue and its subsequent waiting time (WT) for those FIT positives complying with confirmatory diagnosis. We aimed to identify factors associated with both of the correlated problems in the same model.
A total of 294,469 subjects, either with positive FIT test results or having a family history, collected from 2004 to 2013 were enrolled for analysis. We applied a hurdle Poisson regression model to accommodate the hurdle of compliance and also its related WT for undergoing colonoscopy while assessing factors responsible for the mixture of the two outcomes.
The effect on compliance and WT varied with contextual factors, such as geographic areas, type of screening units, and level of urbanization. The hurdle score, representing the risk score in association with noncompliance, and the WT score, reflecting the rate of taking colonoscopy, were used to classify subjects into each of three groups representing the degree of compliance and the level of health awareness.
Our model was not only successfully applied to evaluating factors associated with the compliance and the WT distribution, but also developed into a useful assessment model for stratifying the risk and predicting whether and when screenees comply with the procedure of receiving confirmatory diagnosis given contextual factors and individual characteristics.
Composite measures and indices are used in medical research to represent certain concepts that cannot be measured with one variable. They can be used to predict outcomes or serve as outcomes in trials. The creation of innovative indices is important to increase publications and secure research funding. However, some assumptions and problems are prevalent among indices. We aim to develop a reporting guide and an appraisal tool for indices based on the issues we identified.
We reproduced the three frailty indices from a previous publication and 134,689 principal component-based indices. We reviewed the index assumptions, bias introduced by data processing, relationships between input variables. We interpreted the indices with input variables.
We identified four major issues to be addressed in a reporting guide: constraints imposed by index creation on the input variables; data processing without evidence base; indices poorly linked to input variables; and, relatively inferior predictive power. We demonstrated a flow diagram and a checklist to report and review these four issues related to innovative indices.
A reporting and critical appraisal tool for innovative indices is lacking and needed. These four issues that need to be explicitly considered are previously neglected. This guide is the first attempt to improve the quality and generalizability of innovative indices. This guide can be used to lead further discussion with other experts and review committees.
Principal component analysis (PCA) is important to summarize data or reduce dimensionality. However, one disadvantage of using PCA is the interpretability of the principal components (PCs), especially in a high-dimensional database. This study aims to analyze the patterns of variance accumulation according to PCA loadings and to approximate PCs with input variables from sample data sets.
There were three data sets of various sizes used to understand the performance of PC approximation: Hitters; SF-12v2 subset of the 2004 to 2011 Medical Expenditure Panel Survey (MEPS); and, the full set of 1996 to 2011 MEPS data. The variables in three data sets were first centered and scaled before PCA. PCs approximation was studied with two approaches. First, the PC loadings were squared to estimate the variance contribution by variables to PCs. The other method was to use forward-stepwise regression to approximate PCs with all input variables.
The first few PCs represented large portions of total variances in each data set. Approximating PCs using stepwise regression could more efficiently identify the input variables that explain large portions of PC variances than approximating according to PCA loadings in three data sets. It required few numbers of variables to explain more than eighty percent of the PC variances.
Approximating and interpreting PCs with stepwise regression is highly feasible. Approximating PCs can help i) interpret PCs with input variables, ii) understand the major sources of variances in data sets, iii) select unique sources of information and iv) search and rank input variables according to the proportions of PC variance explained. This is an approach to systematically understand databases and search for variables that are highly representative of databases.
Principal component analysis (PCA) is used for dimension reduction and data summary. However, principal components (PCs) cannot be easily interpreted. To interpret PCs, this study compares two methods to approximate PCs. One uses the PCA loadings to understand how input variables are projected to PCs. The other uses forward-stepwise regression to determine the proportions of PC variances explained by input variables.
Two data sets derived from the Canadian Health Measures Survey (CHMS) were used to test the concept of PC approximation: a spirometry subset with the measures from the first trial of spirometry; and, full data set that contained representative variables. Variables were centered and scaled. PCA were conducted with 282 and twenty-three variables respectively. PCs were approximated with two methods.
The first PC (PC1) could explain 12.1 percent and 50.3 percent of total variances in respective data sets. The leading variables explained 89.6 percent and 79.0 percent of the variances of PC1 in respective data sets. It required one and two variables to explain more than 80 percent of the variances of PC1, respectively. Measures related to physical development were the leading variables to approximate PC1 and lung function variables were leading to approximate PC2 in the full data set. The leading variable to approximate PC1 of the spirometry subset were forced expiratory volume (FEV) 0.5/forced vital capacity (FVC) (percent) and FEV1/FVC (percent).
Approximating PCs with input variables were highly feasible and helpful for the interpretation of PCs, especially for the first PCs. This method is also useful to identify major or unique sources of variances in data sets. The variables related to physical development are the variables related to the most variations in the full data set. The leading variable in the spirometry subset, FEV0.5/FVC (percent), is not well studied for its application in clinical use.
Index mining is a new discipline that aims to search for the composite measures or indices most relevant to the contexts or outcomes. After reviewing three frailty indices and principal component (PC)-based indices, we hereby show certain occasions that can lead to ineffective indices, which consist of bias or fail to represent the theories.
We reproduced and reviewed the three frailty indices and the 134,689 PC (principal component) -based indices from previous publications. The impact of aggregating the input variables on the final indices was analyzed using forward stepwise regression.
Several methods to combine the input variables were related to ineffective projection of information onto the indices. The most common causes leading to ineffective summation of input variables were shown in three examples involving different types of input variables, which were positively or negatively correlated or uncorrelated to the outcome. Ineffective indices were created often because of the summation of redundant information or uncorrelated variables.
The creation of ineffective indices can be avoided if the relationships between input variables and outcomes are properly scrutinized. The creation of composite measures and indices is still a discipline under active development. The three examples we identified are the mistakes that may be repeated unintentionally and need to be addressed with explicit rules. A reporting guide for the creation of composite measures has been proposed. A proper review of index objectives, data characteristics, and data limitations before creating composite measures or indices is recommended.
For the first time, an experiment has been conducted to investigate synthetic jet laminar vortex rings impinging onto porous walls with different geometries by time-resolved particle image velocimetry. The geometry of the porous wall is changed by varying the hole diameter on the wall (from 1.0 mm to 3.0 mm) when surface porosity is kept constant (
). The finite-time Lyapunov exponent and phase-averaged vorticity field derived from particle image velocimetry data are presented to reveal the evolution of the vortical structures. A mechanism associated with vorticity cancellation is proposed to explain the formation of downstream transmitted vortex rings; and both the vortex ring trajectory and the time-mean flow feature are compared between different cases. It is found that the hole diameter significantly influences the evolution of the flow structures on both the upstream and downstream sides of the porous wall. In particular, for a porous wall with a small hole diameter (
, 0.10 and 0.133), the transmitted finger-type jets will reorganize into a well-formed transmitted vortex ring in the downstream flow. However, for the case of a large hole diameter of
, the transmitted vortex ring is not well formed because of insufficient vorticity cancellation. Additionally, the residual vorticity gradually evolves into discrete jet-like structures downstream, which further weaken the intensity of the transmitted vortex ring. Consequently, the transmitted flow structures for the
case would lose coherence more easily (or probably even transition to turbulence), resulting in a faster decay of the axial velocity and stronger entrainment of the transmitted jet. For all porous wall cases, the velocity profile of the transmitted jet exhibits self-similar behaviour in the far field (
), which agrees well with the velocity distribution of free synthetic jets. With the help of the control-volume approach, the time-mean drag of the porous wall is evaluated experimentally for the first time. It is shown that the porous wall drag increases with the decrease in the hole diameter. Moreover, for a porous wall with a small hole diameter (
, 0.10 and 0.133), it appears that the porous wall drag mainly derives from the viscous effect. However, as
increases to 0.20, the form drag associated with the porous wall geometry becomes significant.
Apathy is a condition characterized by a lack of motivation that manifests in emotional, behavioral, and cognitive domains. Although previous studies have indicated that apathy is associated with frontal lesions, few studies have focused on the different subdomains of apathy, and no in vivo human biochemical data have been obtained to examine the neurochemical changes related to apathy in patients with Alzheimer's disease (AD). Thus, we investigated the frontal neurochemical alterations related to apathy among patients with AD using proton magnetic resonance spectroscopy (1H MRS).
Apathy was assessed through the Apathy Evaluation Scale (AES). 1H MRS was performed to measure neurochemical metabolite levels in the anterior cingulate region and right orbitofrontal region. Associations between neurochemical metabolites and the total score and subscores of each domain of the AES were analyzed.
Altogether, 36 patients completed the study. Patients with lower N-acetylaspartate/creatine ratios (NAA/Cr) in the anterior cingulate region demonstrated higher total apathy scores (β = −0.56, p = 0.003) with adjustments for age, gender, educational level, dementia severity, and depression severity. In a further analysis, a lower NAA/Cr in the anterior cingulate region was associated with all subdomains of apathy, including cognition (β = −0.43, p = 0.028), behavior (β = −0.55, p = 0.002), and emotion (β = −0.50, p = 0.005). No statistically significant associations were discovered in the right orbitofrontal region.
Our results suggest that apathy, in each of its cognitive, behavioral, or emotional subdomains is associated with brain neurochemical alterations in the anterior cingulate region. Abnormal neuronal integrity over the anterior cingulate cortex may exhibit a central role in causing all aspects of apathy in patients with AD.
Remote-sensing and GIS techniques in conjunction with field investigations show how glacier mass loss has led to the rapid growth of Linggo Co, a glacier-fed lake on the central Tibetan Plateau, which has expanded by 21.3% in area between 1974 and 2010, with a lake-level rise of ˜11.2m. The lake volume of Linggo Co increased at a rate of 0.02 × 106, 42.67 × 106 and 65.8 × I06m3a-1 during the periods 1974-92, 1992-99 and 1999-2010, respectively. Other nonglacier-fed lakes in the vicinity (i.e. Longwei Co, Amur Co and Darngo Co Ngion) shrank considerably from the early 1970s to 1992 and then expanded from 1992 to 2010. Despite being in the same climate region, Linggo Co and the non-glacier-fed lakes have differed in response to climate change. The glaciers in the catchment of Linggo Co retreated by 2.4% in area between 1974 and 2007, and their mean thickness decreased by 6.19 ± 1.91 m between 1974 and 2000, with an associated glacier meltwater runoff of (7.52 ± 2.32) × 108 m3. The results indicate that glacier mass loss had a significant impact on the growth of Linggo Co over the past 40 years.
The predictability of modified constitutive model, based on Arrhenius type equation, for illustrating the flow behavior of Fe–36%Ni Invar alloy was investigated via isothermal hot compression tests. The hot deformation tests were carried out in a temperature range of 850–1100 °C and strain rates from 0.01 to 10 s−1. True stress-true strain curves exhibited the dependence of the flow stress on deformation temperatures and strain rates, which then described in Arrhenius-type equation by Zener–Holloman parameter. Moreover, the related material constants and hot deformation activation energy (Q) in the constitutive model were calculated by considering the effect of strain as independent function on them and employing sixth polynomial fitting. Subsequently, the performance of the modified constitutive equation was verified by correlation coefficient and average absolute relative error which were estimated in accordance with experimental and predicted data. The results showed that the modified constitutive equation possess reliable and stable ability to predict the hot flow behavior of studied material under different deformation conditions. Meanwhile, Zener–Holloman parameter map was established according to the modified constitutive equation and used to estimate the extent of dynamic recrystallization.
Recent advances in synchrotron based x-ray spectroscopy enable materials scientists to emanate fingerprints on important materials properties, e.g., electronic, optical, structural, and magnetic properties, in real-time and under nearly real-world conditions. This characterization in combination with optimized materials synthesis routes and tailored morphological properties could contribute greatly to the advances in solid-state electronics and renewable energy technologies. In connection to this, such perspective reflects the current materials research in the space of emerging energy technologies, namely photocatalysis, with a focus on transition metal oxides, mainly on the Fe2O3- and TiO2-based materials.
SiO2-MgF2/TiO2 double-layer films with antireflective, self-cleaning and adherent properties were prepared by spin-coating SiO2-MgF2 and TiO2 sol on glass substrate successively and subsequently being calcined at 250°C. The optical and structural properties of films have been investigated by visible spectrophotometer and field emission scanning electron microscope, respectively. At the same time, self-cleaning property generated from superhydrophilicity and photocatalysis was obtained. The results indicated that the as-prepared SiO2-MgF2/TiO2 double-layer films show a maximum increase in transmittance near 520 nm wavelength of 2.8% and photocatalytic property with the R value of 4.7(JIS R 1703–2).It has been demonstrated that high transmittance, self-cleaning and adherent composite has been obtained by a simple sol–gel route presenting good potential to be applied on photovoltaics systems.
Beginning in 2007, all newly diagnosed cancer patients at the Koo Foundation Sun Yat-Sen Cancer Center (KF–SYSCC) were screened for psychosocial distress. Our social workers, as part of the psychosocial care team (PCT), have engaged in proactive outreach with patients identified as distressed. The goal of the present study was to assess the prevalence of psychosocial distress and the extent of contact between the PCT and distressed patients.
Newly diagnosed patients who were treated at KF–SYSCC between 2007 and 2010 for cancer were eligible if there were at least 100 patients with the same type of cancer. Before treatment began, they were screened with the Pain Scale and the Distress Thermometer (DT) and had the option to specify a desire for help. The rates of distress were analyzed by cancer type and by probable related factors. Information regarding contact with the PCT was retrieved from computerized databases.
Overall, some 5,335 cancer patients representing 12 major cancer types were included in our study. Of these, 1,771 (33.20%) were significantly distressed. By multivariate logistic regression, younger age, female gender, higher pain score, and disease stage, but not cancer type, were found to be associated with higher rates of distress. Among these distressed patients, 628 (36%) had some contact with the PCT.
Significance of results:
This Taiwanese study with a large sample size revealed a prevalence rate of psychosocial distress similar to rates found in Western countries. Contact with the PCT was established in only 36% of significantly distressed patients, despite a proactive outreach program. It is very important to have screening results made available in a timely fashion to the psycho-oncology team so that appropriate care can be offered promptly.