The work of Chinese chronic conditions: adaptation and validation of the Distribution of Co-Care Activities Scale

PurposeThe Distribution of Co-Care Activities Scale was adapted into Chinese for the purposes of this study, and then the psychometric characteristics of the Chinese version of the DoCCA scale were confirmed in chronic conditions.MethodsA total of 434 patients with chronic diseases were recruited from three Chinese cities. A cross-cultural adaptation procedure was used to translate the Distribution of Co-Care Activities Scale into Chinese. Cronbach's alpha coefficient, split-half reliability, and test-retest reliability were used to verify the scale's reliability. Content validity indices, exploratory factor analysis, and confirmatory factor analysis were used to confirm the scale's validity.ResultsThe Chinese DoCCA scale includes five domains: demands, unnecessary tasks, role clarity, needs support, and goal orientation. The S-CVI was 0.964. Exploratory factor analysis yielded a five-factor structure that explained 74.952% of the total variance. According to the confirmatory factor analysis results, the fit indices were within the range of the reference values. Convergent and discriminant validity both met the criteria. Also, the scale's Cronbach's alpha coefficient is 0.936, and the five dimensions' values range from 0.818 to 0.909. The split-half reliability was 0.848, and the test-retest reliability was 0.832.ConclusionsThe Chinese version of the Distribution of Co-Care Activities Scale had high levels of validity and reliability for chronic conditions. The scale can assess how patients with chronic diseases feel about their service of care and provide data to optimize their personalized chronic disease self-management strategies

Transcriptomic insights into the molecular mechanism for response of wild emmer wheat to stripe rust fungus

IntroductionContinuous identification and application of novel resistance genes against stripe rust are of great importance for wheat breeding. Wild emmer wheat, Triticum dicoccoides, has adapted to a broad range of environments and is a valuable genetic resource that harbors important beneficial traits, including resistance to stripe rust caused by Puccinia striiformis f. sp. tritici (Pst). However, there has been a lack of systematic exploration of genes against Pst races in wild emmer wheat.MethodsGenome-wide transcriptome profiles were conducted on two wild emmer wheat genotypes with different levels of resistance to (Pst (DR3 exhibiting moderate (Pst resistance, and D7 displaying high (Pst resistance). qRT-PCR was performed to verify findings by RNA-seq.ResultsA higher number of DEGs were identified in the moderately (Pst-resistant genotype, while the highly (Pst-resistant genotype exhibited a greater enrichment of pathways. Nonetheless, there were consistent patterns in the enrichment of pathways between the two genotypes at the same time of inoculation. At 24 hpi, a majority of pathways such as the biosynthesis of secondary metabolites, phenylpropanoid biosynthesis, phenylalanine metabolism, and alpha-Linolenic acid metabolism exhibited significant enrichment in both genotypes. At 72 hpi, the biosynthesis of secondary metabolites and circadian rhythm-plant pathways were notably and consistently enriched in both genotypes. The majority of (WRKY, MADs , and AP2-ERF families were found to be involved in the initial stage of response to Pst invasion (24 hpi), while the MYB, NAC, TCP, and b-ZIP families played a role in defense during the later stage of Pst infection (72 hpi).DiscussionIn this present study, we identified numerous crucial genes, transcription factors, and pathways associated with the response and regulation of wild emmer wheat to Pst infection. Our findings offer valuable information for understanding the function of crucial Pst-responsive genes, and will deepen the understanding of the complex resistance mechanisms against Pst in wheat

Mechanical-Thermal Noise in Drive-Mode of a Silicon Micro-Gyroscope

A new closed-loop drive scheme which decouples the phase and the gain of the closed-loop driving system was designed in a Silicon Micro-Gyroscope (SMG). We deduce the system model of closed-loop driving and use stochastic averaging to obtain an approximate “slow” system that clarifies the effect of thermal noise. The effects of mechanical-thermal noise on the driving performance of the SMG, including the noise spectral density of the driving amplitude and frequency, are derived. By calculating and comparing the noise amplitude due to thermal noise both in the opened-loop driving and in the closed-loop driving, we find that the closed-loop driving does not reduce the RMS noise amplitude. We observe that the RMS noise frequency can be reduced by increasing the quality factor and the drive amplitude in the closed-loop driving system. The experiment and simulation validate the feasibility of closed-loop driving and confirm the validity of the averaged equation and its stablility criterion. The experiment and simulation results indicate the electrical noise of closed-loop driving circuitry is bigger than the mechanical-thermal noise and as the driving mass decreases, the mechanical-thermal noise may get bigger than the electrical noise of the closed-loop driving circuitry

The Influence of Education on Chinese Version of Montreal Cognitive Assessment in Detecting Amnesic Mild Cognitive Impairment among Older People in a Beijing Rural Community

To assess the influence of education on the performance of Chinese version of Montreal cognitive assessment (C-MoCA) in relation to the mini-mental state examination (MMSE) in detecting amnesic mild cognitive impairment (aMCI) among rural-dwelling older people C-MoCA and MMSE was administered and diagnostic interviews were conducted among community-dwelling elderly in two villages in Beijing. The performance of C-MoCA and MMSE in detecting aMCI was evaluated by the area under the ROC curve (AUC). Effect size of education on variations in C-MoCA scores was estimated with general linear model. Among 172 study participants (24 cases of aMCI and 148 normal controls), the AUC of C-MoCA was 0.72 (95% CI = 0.62–0.81, cutoff = 20/21), compared to AUC of MMSE of 0.74 (95% CI = 0.64–0.84, cutoff = 26/27). The performance of both C-MoCA and MMSE was especially poorer among those with low (0–6 years) education. After controlling for gender and age, education (η2 = 0.204) had a surpassing effect over aMCI diagnosis (η2 = 0.052) on variations in C-MoCA scores. Among rural older people, the MoCA showed modest accuracy and was no better than MMSE in detecting aMCI, especially in those with low education, due to the overwhelming effect of education relative to aMCI diagnosis on variations in C-MoCA performance

Phenotype Recognition with Combined Features and Random Subspace Classifier Ensemble

<p>Abstract</p> <p>Background</p> <p>Automated, image based high-content screening is a fundamental tool for discovery in biological science. Modern robotic fluorescence microscopes are able to capture thousands of images from massively parallel experiments such as RNA interference (RNAi) or small-molecule screens. As such, efficient computational methods are required for automatic cellular phenotype identification capable of dealing with large image data sets. In this paper we investigated an efficient method for the extraction of quantitative features from images by combining second order statistics, or Haralick features, with curvelet transform. A random subspace based classifier ensemble with multiple layer perceptron (MLP) as the base classifier was then exploited for classification. Haralick features estimate image properties related to second-order statistics based on the grey level co-occurrence matrix (GLCM), which has been extensively used for various image processing applications. The curvelet transform has a more sparse representation of the image than wavelet, thus offering a description with higher time frequency resolution and high degree of directionality and anisotropy, which is particularly appropriate for many images rich with edges and curves. A combined feature description from Haralick feature and curvelet transform can further increase the accuracy of classification by taking their complementary information. We then investigate the applicability of the random subspace (RS) ensemble method for phenotype classification based on microscopy images. A base classifier is trained with a RS sampled subset of the original feature set and the ensemble assigns a class label by majority voting.</p> <p>Results</p> <p>Experimental results on the phenotype recognition from three benchmarking image sets including HeLa, CHO and RNAi show the effectiveness of the proposed approach. The combined feature is better than any individual one in the classification accuracy. The ensemble model produces better classification performance compared to the component neural networks trained. For the three images sets HeLa, CHO and RNAi, the Random Subspace Ensembles offers the classification rates 91.20%, 98.86% and 91.03% respectively, which compares sharply with the published result 84%, 93% and 82% from a multi-purpose image classifier WND-CHARM which applied wavelet transforms and other feature extraction methods. We investigated the problem of estimation of ensemble parameters and found that satisfactory performance improvement could be brought by a relative medium dimensionality of feature subsets and small ensemble size.</p> <p>Conclusions</p> <p>The characteristics of curvelet transform of being multiscale and multidirectional suit the description of microscopy images very well. It is empirically demonstrated that the curvelet-based feature is clearly preferred to wavelet-based feature for bioimage descriptions. The random subspace ensemble of MLPs is much better than a number of commonly applied multi-class classifiers in the investigated application of phenotype recognition.</p

State of the climate in 2018

In 2018, the dominant greenhouse gases released into Earth’s atmosphere—carbon dioxide, methane, and nitrous oxide—continued their increase. The annual global average carbon dioxide concentration at Earth’s surface was 407.4 ± 0.1 ppm, the highest in the modern instrumental record and in ice core records dating back 800 000 years. Combined, greenhouse gases and several halogenated gases contribute just over 3 W m−2 to radiative forcing and represent a nearly 43% increase since 1990. Carbon dioxide is responsible for about 65% of this radiative forcing. With a weak La Niña in early 2018 transitioning to a weak El Niño by the year’s end, the global surface (land and ocean) temperature was the fourth highest on record, with only 2015 through 2017 being warmer. Several European countries reported record high annual temperatures. There were also more high, and fewer low, temperature extremes than in nearly all of the 68-year extremes record. Madagascar recorded a record daily temperature of 40.5°C in Morondava in March, while South Korea set its record high of 41.0°C in August in Hongcheon. Nawabshah, Pakistan, recorded its highest temperature of 50.2°C, which may be a new daily world record for April. Globally, the annual lower troposphere temperature was third to seventh highest, depending on the dataset analyzed. The lower stratospheric temperature was approximately fifth lowest. The 2018 Arctic land surface temperature was 1.2°C above the 1981–2010 average, tying for third highest in the 118-year record, following 2016 and 2017. June’s Arctic snow cover extent was almost half of what it was 35 years ago. Across Greenland, however, regional summer temperatures were generally below or near average. Additionally, a satellite survey of 47 glaciers in Greenland indicated a net increase in area for the first time since records began in 1999. Increasing permafrost temperatures were reported at most observation sites in the Arctic, with the overall increase of 0.1°–0.2°C between 2017 and 2018 being comparable to the highest rate of warming ever observed in the region. On 17 March, Arctic sea ice extent marked the second smallest annual maximum in the 38-year record, larger than only 2017. The minimum extent in 2018 was reached on 19 September and again on 23 September, tying 2008 and 2010 for the sixth lowest extent on record. The 23 September date tied 1997 as the latest sea ice minimum date on record. First-year ice now dominates the ice cover, comprising 77% of the March 2018 ice pack compared to 55% during the 1980s. Because thinner, younger ice is more vulnerable to melting out in summer, this shift in sea ice age has contributed to the decreasing trend in minimum ice extent. Regionally, Bering Sea ice extent was at record lows for almost the entire 2017/18 ice season. For the Antarctic continent as a whole, 2018 was warmer than average. On the highest points of the Antarctic Plateau, the automatic weather station Relay (74°S) broke or tied six monthly temperature records throughout the year, with August breaking its record by nearly 8°C. However, cool conditions in the western Bellingshausen Sea and Amundsen Sea sector contributed to a low melt season overall for 2017/18. High SSTs contributed to low summer sea ice extent in the Ross and Weddell Seas in 2018, underpinning the second lowest Antarctic summer minimum sea ice extent on record. Despite conducive conditions for its formation, the ozone hole at its maximum extent in September was near the 2000–18 mean, likely due to an ongoing slow decline in stratospheric chlorine monoxide concentration. Across the oceans, globally averaged SST decreased slightly since the record El Niño year of 2016 but was still far above the climatological mean. On average, SST is increasing at a rate of 0.10° ± 0.01°C decade−1 since 1950. The warming appeared largest in the tropical Indian Ocean and smallest in the North Pacific. The deeper ocean continues to warm year after year. For the seventh consecutive year, global annual mean sea level became the highest in the 26-year record, rising to 81 mm above the 1993 average. As anticipated in a warming climate, the hydrological cycle over the ocean is accelerating: dry regions are becoming drier and wet regions rainier. Closer to the equator, 95 named tropical storms were observed during 2018, well above the 1981–2010 average of 82. Eleven tropical cyclones reached Saffir–Simpson scale Category 5 intensity. North Atlantic Major Hurricane Michael’s landfall intensity of 140 kt was the fourth strongest for any continental U.S. hurricane landfall in the 168-year record. Michael caused more than 30 fatalities and $25 billion (U.S. dollars) in damages. In the western North Pacific, Super Typhoon Mangkhut led to 160 fatalities and$6 billion (U.S. dollars) in damages across the Philippines, Hong Kong, Macau, mainland China, Guam, and the Northern Mariana Islands. Tropical Storm Son-Tinh was responsible for 170 fatalities in Vietnam and Laos. Nearly all the islands of Micronesia experienced at least moderate impacts from various tropical cyclones. Across land, many areas around the globe received copious precipitation, notable at different time scales. Rodrigues and Réunion Island near southern Africa each reported their third wettest year on record. In Hawaii, 1262 mm precipitation at Waipā Gardens (Kauai) on 14–15 April set a new U.S. record for 24-h precipitation. In Brazil, the city of Belo Horizonte received nearly 75 mm of rain in just 20 minutes, nearly half its monthly average. Globally, fire activity during 2018 was the lowest since the start of the record in 1997, with a combined burned area of about 500 million hectares. This reinforced the long-term downward trend in fire emissions driven by changes in land use in frequently burning savannas. However, wildfires burned 3.5 million hectares across the United States, well above the 2000–10 average of 2.7 million hectares. Combined, U.S. wildfire damages for the 2017 and 2018 wildfire seasons exceeded $40 billion (U.S. dollars)

Large expert-curated database for benchmarking document similarity detection in biomedical literature search

Document recommendation systems for locating relevant literature have mostly relied on methods developed a decade ago. This is largely due to the lack of a large offline gold-standard benchmark of relevant documents that cover a variety of research fields such that newly developed literature search techniques can be compared, improved and translated into practice. To overcome this bottleneck, we have established the RElevant LIterature SearcH consortium consisting of more than 1500 scientists from 84 countries, who have collectively annotated the relevance of over 180 000 PubMed-listed articles with regard to their respective seed (input) article/s. The majority of annotations were contributed by highly experienced, original authors of the seed articles. The collected data cover 76% of all unique PubMed Medical Subject Headings descriptors. No systematic biases were observed across different experience levels, research fields or time spent on annotations. More importantly, annotations of the same document pairs contributed by different scientists were highly concordant. We further show that the three representative baseline methods used to generate recommended articles for evaluation (Okapi Best Matching 25, Term Frequency-Inverse Document Frequency and PubMed Related Articles) had similar overall performances. Additionally, we found that these methods each tend to produce distinct collections of recommended articles, suggesting that a hybrid method may be required to completely capture all relevant articles. The established database server located at https://relishdb.ict.griffith.edu.au is freely available for the downloading of annotation data and the blind testing of new methods. We expect that this benchmark will be useful for stimulating the development of new powerful techniques for title and title/abstract-based search engines for relevant articles in biomedical research.Peer reviewe

Integrated performance optimization of industrial buildings in relation to thermal comfort and energy consumption: A case study in hot summer and cold winter climate

In the context of environmental protection, researches on thermal performance of buildings represent a growing field, most of which use a variety of linked calculating algorithms to maximize performance metrics. However, previous studies have done little to investigated in balancing energy consumption and indoor thermal comfort of industrial buildings. With various combinations of architecture design parameters, this paper aims to optimize the comprehensive thermal performance of clean plants. Matlab software was used for data preparation, deployment, and sampling. Two thousand simulations based on the different combinations were performed using Monte Carlo technique. The most appropriate combinations of sensitive factors were then analyzed to offer a reference for the architecture design, with the uncertainties of the original alternative parameters being substituted by mathematical statistics and data filtering. The result shows that the total thermal discomfort hours (TTD) increase by 14.95% and the total energy consumption (TEC) increases by 35.19% when the heating setpoint temperature is raised from 14 °C to 24 °C. The TTD increase by 3.93% and the TEC decreases by 21.68% when the cooling setpoint temperature is raised from 20 °C to 28 °C, respectively. The findings can contribute to understanding of the parameter combinations enhancing the comprehensive thermal performance of industrial buildings