Search

Comprehensive Summary: Expressed protein ligation (EPL) provides a powerful tool to access large‐size proteins with precise structures. Existing methods for constructing the critical protein thioester for EPL have predominantly relied on the recombinant intein fusion expressed in Escherichia coli (E. coli). Despite its powerful applications, the expression of thioester derived from eukaryotic protein in E. coli inherently suffers from its limited solubility, the inactivity of intein, premature hydrolysis and low yields. To overcome these obstacles, we present herein the facile one‐flask synthesis of inaccessible protein α‐thioester via a SUMO‐protein‐intein (SPI) sandwich model. The utility of SUMO enhances the protein fusion yield and solubility, prevents premature hydrolysis and simplifies the purification process. The inaccessible protein thioester with internal Cys residues can be readily produced and is compatible with the EPL‐desulfurization protocol used to prepare complex proteins, which is otherwise difficult to obtain using traditional methods. Its utility has been highlighted through the synthesis of human granulocyte colony‐stimulating factor (G‐CSF). [ABSTRACT FROM AUTHOR]

Radiation enteropathy (RE) is common in patients treated with radiotherapy for pelvic–abdominal cancers. Accumulating data indicate that gut commensal bacteria determine intestinal radiosensitivity. Radiotherapy can result in gut bacterial dysbiosis. Gut bacterial dysbiosis contributes to the pathogenesis of RE. Mild to moderate depressive symptoms can be observed in patients with RE in clinical settings; however, the rate of these symptoms has not been reported. Studies have demonstrated that gut bacterial dysbiosis induces depression. In the state of comorbidity, RE and depression may be understood as local and abscopal manifestations of gut bacterial disorders. The ability of comorbid depression to worsen inflammatory bowel disease (IBD) has long been demonstrated and is associated with dysfunction of cholinergic neural anti‐inflammatory pathways. There is a lack of direct evidence for RE comorbid with depression. It is widely accepted that RE shares similar pathophysiologic mechanisms with IBD. Therefore, we may be able to draw on the findings of the relationship between IBD and depression. This review will explore the relationship between gut bacteria, RE, and depression in light of the available evidence and indicate a method for investigating the mechanisms of RE combined with depression. We will also describe new developments in the treatment of RE with probiotics, prebiotics, and fecal microbial transplantation. [ABSTRACT FROM AUTHOR]

The mesh reflector suffers inevitably from complicated environmental interferences during long-term on-orbit operation and consequently the reflector may deviate significantly from its initial state. It is thus crucial to identify the deformed reflector on-orbit in order to evaluate timely the mesh reflector accuracy. However, the existing reflector identification methods are difficult to achieve this object as the most of them need to install additional equipment on antenna or satellite. Aiming at realizing on-orbit mesh reflector identification, a novel identification approach based on tie cable tensions sensing (TCTS) is proposed in the paper. The TCTS method leverages real-time measurements of tie cable tensions through embedded force sensors to calculate the remaining cable tensions required to address reflector deformations. For this purpose, an optimization problem is established based on tension balance equation and then solved using a proposed iteration solution algorithm incorporating the genetic algorithm. The actual configuration of mesh reflector is then determined by all cable tensions according to the force density method and the reflector can be identified ultimately. The simulated studies on reflector identification of a mesh reflector with 8 m aperture diameter are conducted under temperature loads and cable stiffness degradations respectively. The results indicate that the TCTS method possesses high reflector identification accuracy and good anti-noise capability under different environmental interferences. Furthermore, a reflector identification method based on partial tie cable tensions sensing (PTCTS) is developed in order to reducing system complexity and cost. The sensitivity analyses of tie cable tensions are performed in order to provide a reference for location placement of force sensors in the PTCTS method. The simulated results indicate that the optimized location placement scheme can enhance significantly the reflector identification accuracy of PTCTS method. Besides, the PTCTS method exhibits equal anti-noise capability with the TCTS method. Finally, the experimental studies are conducted on reflector identification of a mesh reflector test model with 1.5 m diameter and 0.5 m height under cable stiffness degradations. Through comparing with the reflector identification results obtained by the photogrammetry method under different cable stiffness degradation degrees, the accuracy and effectiveness of the TCTS/PTCTS methods are demonstrated experimentally. • The mesh reflector is identified based on tie cable tension sensing (TCTS). • Partial tie cable tensions sensing (PTCTS) can reduce system complexity. • The TCTS/PTCTS method provides a promising way to identify mesh reflector in orbit. • The TCTS/PTCTS approach is demonstrated numerically and experimentally. [ABSTRACT FROM AUTHOR]

The massive production and accumulation of industrial solid waste-iron tailings not only occupy valuable land resources but also easily cause environmental pollution and personnel injury. Based on this, the thermoplastic polyurethane (TPU)/modified iron tailings (MIT) composites were prepared by surface modification of iron tailings (MIT) and adding it as flame retardant to TPU. The combustion performance and smoke toxicity of TPU/MIT composites were analyzed by thermogravimetric analysis (TG), thermogravimetric-spectroscopy (TG-IR) and cone calorimetry (CCT). The morphology and structure of the carbon slag of the composites were analyzed by scanning electron microscopy(SEM) and Raman spectroscopy. The results showed that the initial thermal decomposition temperature (T-5%) of TPU/MIT30 composite increased at about 7oC, and the residual carbon content at 700oC increased from 3.13 wt% to 32.17 wt%. The peak heat release rate(PHRR), total heat release(THR) and total smoke release(TSR) of TPU/MIT30 composite decreased from 800.6 kW/m2, 83.1 MJ/m2 and 827.6 m2/m2 of pure TPU to 488.0 kW/m2, 63.6 MJ/m2 and 517.4 m2/m2, respectively. The SEM and Raman1 spectroscopy of char residue showed that the compactness and graphitization degree of char residue for TPU/MIT30 composite was the highest, indicating that the MIT could effectively improve the fire safety of TPU composites. This study provided some ideas for the high-value utilization of iron tailings. [ABSTRACT FROM AUTHOR]

SUMMARY: In this study, magnitude estimation in earthquake early warning (EEW) systems is seen as a classification problem: the single-channel waveform, starting from the P-wave onset and lasting 4 s, is given in the input, and earthquake severity (medium and large earthquakes: local magnitude (ML) ≥ 5; small earthquakes: ML < 5) is the classification result. The convolutional neural network (CNN) is proposed to estimate the severity of the earthquake, which is composed of several blocks that can extract the latent representation of the input from different receptive fields automatically. We train and test the proposed CNN model using two data sets. One is recorded by the China Earthquake Networks Center (CENC), and the other is the Stanford Earthquake Dataset (STEAD). Accordingly, the proposed CNN model achieves a test accuracy of 97.90 per cent. The proposed CNN model is applied to estimate two real-world earthquake swarms in China (the Changning earthquake and the Tangshan earthquake swarms) and the INSTANCE data set, and demonstrated the promising performance of generalization. In addition, the proposed CNN model has been connected to the CENC for further testing using real-world real-time seismic data. [ABSTRACT FROM AUTHOR]

Cinnamomum plants are rich in natural essential oils, which are widely used as materials in the fragrance, insecticidal, antibacterial agent, pharmaceutical, and food industries; however, few studies have investigated the essential oil components of Cinnamomum bodinieri. Therefore, this study investigated the diversity of essential oils from the leaves of 885 individual C. bodinieri plants across 32 populations in five provinces. Essential oils were extracted by hydrodistillation, and then qualitative and quantitative analyses of the compounds were performed by GC-MS and GC-FID. A total of 87 chemical constituents were identified in the essential oils, including 33 monoterpenes, 48 sesquiterpenes, and six other compounds. The average oil yield was 0.75%, and individual oil yields ranged from 0.01% to 4.28%. A total of 16 chemotypes were classified according to variations in the essential oil chemical constituents of C. bodinieri, among which the camphor-type, citral-type, and eucalyptol-type were dominant. Moreover, the borneol-type, cymol-type, elemol-type, methylisoeugenol-type, and selina-6-en-4-ol-type were reported in C. bodinieri for the first time. The yield and principal components of the essential oils were mainly affected by altitude, temperature, and sunshine duration, among which altitude had the most significant effect; thus, low-altitude areas are more suitable for the synthesis and accumulation of essential oils. Based on the different characteristics of the essential oils in the leaves of C. bodinieri, several excellent populations and individuals were identified in this study. Moreover, the findings provide a foundation for breeding superior varieties and studying essential oil biosynthesis mechanisms in the future. [ABSTRACT FROM AUTHOR]

Objectives: The COVID-19 pandemic has propelled the use of technology for health care services delivery. Because of inequities in health care and technology access, we investigated the use of telehealth services among racial and ethnic minority groups before and during the COVID-19 pandemic. Methods: For this retrospective study, we examined the electronic health records of privately insured patients in the Healthjump database, provided by the COVID-19 Research Database Consortium. We examined 17.98 million unique visit records of 2.93 million patients from March through December 2019 and 22.17 million records of 3.55 million patients from March through December 2020. We conducted a descriptive analysis and used multiple logistic regression to examine differences in the use of telehealth services among 3 racial and ethnic groups: non-Hispanic White, non-Hispanic Black, and Hispanic people. Results: Telehealth visits before and during COVID-19 accounted for 8.3% and 10.9% of total visits, respectively, with a peak of 15.5% in April 2020. Pre–COVID-19, Hispanic patients had a significantly lower monthly utilization rate (5.3%) than non-Hispanic White patients (8.4%, P <.001) and non-Hispanic Black patients (10.4%, P =.001). During the pandemic study period, Hispanic patients were 41% less likely than non-Hispanic White patients to have a telehealth visit, controlling for age and sex. Conclusions: The likelihood of using telehealth was lower among Hispanic patients than among non-Hispanic White and non-Hispanic Black patients during the pandemic. Culturally sensitive measures are needed to support telehealth use among the Hispanic population. [ABSTRACT FROM AUTHOR]

Knowledge extraction aims to identify entities and extract relations between them from unstructured text, which are in the form of triplets. Analysis of the fruit nutrition domain corpus revealed many overlapping triplets, that is, multiple correspondences between a subject and multiple objects or the same subject and object. The current relevant methods mainly target the extraction of ordinary triplets, which cannot accurately identify overlapping triplets. To solve this problem, a deep learning based model for overlapping triplet extraction is proposed in this study. The relation is modeled as a function that maps a subject to an object. The hybrid information of the subject is entered into the relation-object extraction model to detect the object and relation. The experimental results show this model outperforms existing extraction models and achieves state-of-the-art performance on the manually labeled fruit nutrition domain dataset. In terms of application value, the proposed work can obtain a high-quality and structured fruit nutrition knowledge base, which provides application fundamentals for downstream applications of nutrition matching. [ABSTRACT FROM AUTHOR]

Purpose: The purpose of this study is to provide a new convolutional neural network (CNN) model with multi-scale feature extractor to segment and recognize wear particles in complex ferrograph images, especially fatigue and severe sliding wear particles, which are similar in morphology while different in wear mechanism. Design/methodology/approach: A CNN model named DWear is proposed to semantically segment fatigue, severe sliding particles and four other types of particles, that is, chain, spherical, cutting and oxide particles, which unifies segmentation and recognition together. DWear is constructed using four modules, namely, encoder, densely connected atrous spatial pyramid pooling, decoder and fully connected conditional random field. Different from the architectures of ordinary semantic segmentation CNN models, a multi-scale feature extractor using cascade connections and a coprime atrous rate group is incorporated into the DWear model to obtain multi-scale receptive fields and better extract features of wear particles. Moreover, fully connected conditional random field module is adopted for post-processing to smooth coarse prediction and obtain finer results. Findings: DWear is trained and verified on the ferrograph image data set, and experimental results show that the final Mean Pixel Accuracy is 95.6% and the Mean Intersection over Union is 92.2%, which means that the recognition and segmentation accuracy is higher than those of previous works. Originality/value: DWear provides a promising approach for wear particle analysis and can be further developed in equipment condition monitoring applications. [ABSTRACT FROM AUTHOR]

Knowledge graphs are large-scale semantic networks that considerably impact knowledge representation. Mining hidden knowledge from existing data, including triplet knowledge reasoning, is a primary objective of knowledge graphs. With the development of Neural Network (NN) and Deep Learning (DL), the interpretability of triplet knowledge reasoning gradually decreases; furthermore, what machines learn is not actual reasoning but digital reasoning shortcuts. To solve this problem, more background knowledge needs to be introduced into knowledge graphs: causal graphs can offer valuable causal logic knowledge for reasoning; temporal quadruples can provide essential temporal distribution details; and commonsense graphs can furnish pertinent commonsense understanding to support reasoning. In recent years, many scholars have incorporated additional background knowledge into knowledge graphs to construct more complex reasoning mechanisms. This paper reviews the basic concepts and definitions of knowledge reasoning and the reasoning methods used for knowledge graphs. Specifically, we dissect the reasoning methods into four categories: triplet reasoning, causal inference, temporal inference, and commonsense reasoning. Finally, we discuss the remaining challenges and research opportunities related to knowledge graph reasoning. [ABSTRACT FROM AUTHOR]

Neural operators have emerged as a powerful tool for learning the mapping between infinite-dimensional parameter and solution spaces of partial differential equations (PDEs). In this work, we focus on multiscale PDEs that have important applications such as reservoir modeling and turbulence prediction. We demonstrate that for such PDEs, the spectral bias towards low-frequency components presents a significant challenge for existing neural operators. To address this challenge, we propose a hierarchical attention neural operator (HANO) inspired by the hierarchical matrix approach. HANO features a scale-adaptive interaction range and self-attentions over a hierarchy of levels, enabling nested feature computation with controllable linear cost and encoding/decoding of multiscale solution space. We also incorporate an empirical H 1 loss function to enhance the learning of high-frequency components. Our numerical experiments demonstrate that HANO outperforms state-of-the-art (SOTA) methods for representative multiscale problems. [ABSTRACT FROM AUTHOR]

• An extended modal approach is proposed for OMA under the harmonic excitations. • The extended modal matrix is identified using PSDT method. • The extended modal responses (EMRs) are acquired using least squares reconstruction method. • The spurious mode can be screened according to EDF of reconstructed EMR. • The approach is validated numerically and experimentally. The existing operational modal analysis (OMA) methods for the structure subjected to white noise and harmonic combined excitations have some limitations such as easy misjudgment of true and spurious modes and slow identification speed. Through expressing the system response with the extended modal matrix (EMM) and the extended modal response (EMR) vector, a novel operational modal parameter identification approach called the extended modal approach is proposed to obtain structural true modes under the existence of multiple harmonic excitations. The EMRs consist of the true modal responses and the spurious modal responses, and each of the former is random while each of the latter is harmonic. It is also proved theoretically that the EMM can be identified using the power spectrum density transmissibility (PSDT) method. Therefore, the extended modal approach includes three steps: (1) Using the PSDT to identify the EMM; (2) Utilizing the least squares reconstruction method to obtain the EMRs from the underdetermined extended modal equation; (3) Judging each of the EMRs to be true or spurious according to its empirical density function. Structural true modes can be ultimately obtained by removing all spurious modes from the identified results. The new approach is numerically verified through the OMA of two multiple-degree-of-freedom systems and then experimentally verified through the OMA of a test beam. The results in numerical simulation and experiments all show that the extended modal approach can identify accurately and quickly structural true modes under multiple harmonic excitations. Besides, the approach has good robustness against noise contamination. [ABSTRACT FROM AUTHOR]

Body measurement is an important link in monitoring the growth of livestock in breeding management. Conventional measurements are notoriously difficult, unstable, stressful and time-consuming. 3D Computer vision technology has demonstrated its superiority in body size measurement and live body weight estimation because it has advantages of being both non-contact and effective. The objective of this paper was validating the use of single-frame surface point clouds to measure the body size, and develop a model to estimate the pig weight. With a limit bar not applied, a Kinect camera was fixed above the drinking area allowed multiple images of pigs to be captured at one time in a group-house. Body size parameters were extracted from back surface point clouds, and subsequently used as independent variables to build the three regression analysis models: i) stepwise regression analysis, ii) ridge regression analysis, and iii) partial least squares regression analysis. An experiment was conducted on fifty pigs in a group-house, half of which were Landrace gilts, half were Yorkshire shoats. The result of size measurement showed the mean relative absolute errors of body length, height, shoulder width were 0.7%, 1.8%, and 3.3%, respectively. Final ridge regression equation was developed with an R 2 of 0.958 and a MAE of 2.961 kg. It was concluded that the proposed methods based on Kinect v2 was shown to be accurate and reliable for body size measurement and live body mass estimation. • Novel body size measurement method based on single view Kinect without limit bar presented. • Mean relative absolute errors of body length, height, shoulder width were acceptable. • Non-contact and low-cost measurement for small-scale farms was provided. • Scheme for data collection in intelligent breeding environment provided. • Ridge regression performed well in weight estimation R 2 of 0.958 & MAE of 2.961 kg. [ABSTRACT FROM AUTHOR]

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

Background: Maternal and child health (MCH) hospitals play an essential role in providing MCH services in China, while the supply has become increasingly challenging in the past decade, especially among secondary MCH hospitals. In this study we aimed to evaluate the technical efficiency (TE) of secondary MCH hospitals in Hubei province (China) to generate evidence-based decision-making for efficiency improvement. Methods: The data were collected from the Department of Maternal and Child Health of Health Commission of Hubei Province in 2019. A total of 59 out of 74 secondary MCH hospitals were included as our study sample. Four input indicators (number of health professionals, number of beds, number of equipment with value greater than 10,000 RMB Yuan, building area for hospital operations) and three output indicators (number of total diagnostic patients, number of discharged patients, and number of birth deliveries) were selected based on previous literature. TE scores of the sample hospitals were estimated by using Bootstrap-Data Envelopment Analysis (Bootstrap-DEA). Results: After bias-correction with Bootstrap-DEA model, the average TE score of the MCH hospitals was 0.673. 20 (33.89%) MCH hospitals had TE scores below the average. No MCH hospitals achieved excellent efficiency; 16 (27.11%) MCH hospitals reached good efficiency; and 26 (44.06%) MCH hospitals fell into poor and failing efficiency. Besides, 17 MCH hospitals had TE scores of 1 before bias-corrections, while none of them reached 1 after bias correction. Conclusions: Significant capacity variations existed among the secondary MCH hospitals in terms of input and output indicators and their overall TE was low in Hubei of China. For better improvement, the secondary MCH hospitals in Hubei need to improve their internal management and strengthen the construction of their information systems. A series of policy supports are needed from the health and insurance administrations to optimize health resources. Third-party performance evaluation can be piloted to improve efficiency and overall performance of the MCH hospitals. The policy recommendations we raise for MCH hospitals in Hubei can be worth learning for some low- and middle- income countries. [ABSTRACT FROM AUTHOR]

In this paper, convolutional neural networks (CNNs) were used to distinguish between tectonic and non-tectonic seismicity. The proposed CNNs consisted of seven convolutional layers with small kernels and one fully connected layer, which only relied on the acoustic waveform without extracting features manually. For a single station, the accuracy of the model was 0.90, and the event accuracy could reach 0.93. The proposed model was tested using data from January 2019 to August 2019 in China. The event accuracy could reach 0.92, showing that the proposed model could distinguish between tectonic and non-tectonic seismicity. [ABSTRACT FROM AUTHOR]

• Steel slag (SS) was modified by a DOPO-derived silane via sol-gel reaction. • The optimal RPUF formulation exhibited a LOI value of 24.0% and UL-94V-0 rating. • Fire hazards of RPUF including heat release rate and smoke production reduced significantly. • Modified steel slag (mSS) showed better flame retardant efficiency than SS. Steel slag (SS) waste was microencapsulated by a 9, 10-dihydro-9-oxa-10-phosphaphenanthrene-10-oxide (DOPO)-derived silane via sol–gel reaction to improve its compatibility with polymer as well as to enhance flame retardant efficiency. The modified steel slag (mSS) was combined with expanded graphite (EG) as flame retardant system for rigid polyurethane foams (RPUFs). The RPUF-3 sample with 10 wt% mSS and 10 wt% EG showed an optimal comprehensive performance including a compressive strength of 206.9 kPa, a thermal conductivity of 0.0304 W/(m·K), a limiting oxygen index (LOI) value of 24.0% and UL-94 V-0 classification. The peak heat release rate (pHRR) and total heat release (THR) of the RPUF-3 dropped by 55% and 47%, respectively. By comparison, the RPUF-6 sample with 10 wt% SS and 10 wt% EG exhibited poorer flame-retardant behaviors. The superior flame retardancy of RPUF-3 over RPUF-6 could be ascribed to phosphorus-silicon complex in mSS promoted charring that provided better barrier effect during combustion. [ABSTRACT FROM AUTHOR]

• Steel slag was used as co-flame retardants for rigid polyurethane foams (RPUFs). • The PHRR value of RPUF-2 reduced by 47% compared to that of original RPUF. • The presence of steel slag improved the thermal resistance of intermediates in TGA. • The addition of steel slag enhanced the graphitization degree of the residues. To explore the potential application of industrial waste, steel slag powder in combination with melamine pyrophosphate (MPP) was adopted to improve the flame retardancy of rigid polyurethane foam (RPUF). The incorporation of steel slag slightly reduced the thermal conductivity of the resulting flame-retardant RPUF samples. The addition of MPP and/or steel slag did not significantly alter the thermal stability in terms of T -10% and T max but did obviously increase the T -50% value, suggesting the improved thermal resistance of the residues. The coaddition of MPP and steel slag into RPUF resulted in higher LOI values and lower peak heat release rates than the samples incorporating either MPP or steel slag alone. The superior flame retardancy could be attributed to MPP promoting char formation, which then acted as a barrier at the beginning of RPUF thermal decomposition; simultaneously, the thermally stable inorganics in the steel slag powder strengthened the thermal resistance of this char layer. [ABSTRACT FROM AUTHOR]

Background: Great variation exists in the prevalence rates of diabetes across different geographic regions in China. The purpose of this study was to examine the prevalence of type 2 diabetes among a Chinese population in Western China and to explore associated risk factors. Methods: Adult residents in Luzhou City in Sichuan Province, China, were selected using a multistage area probability design. A validated questionnaire, physical examination, and lab tests were administered to the participants. Bivariate analysis and multivariate analysis using logistic regression were performed to determine factors that may affect the risk of diabetes. Results: The final study sample included 3513 participants with complete questionnaires (response rate was 97.58%). The prevalence rate of diabetes was 17.76%. Multivariate logistic regression analysis demonstrated that residents who fit within the following categories were associated with higher risk for diabetes after controlling for participant demographic factors and other covariates: age groups 50–59, 60–69, and 70 or above; a monthly income of less than ¥1999; family history of diabetes; using mainly animal fat as cooking oil; having a white collar job; BMI above 28; and hypertriglyceridemia. In addition to identifying risk factors previously reported, this study indicates that dietary intake factors may be important to the development of diabetes. Conclusion: This study contributes to the literature of prevalence and risk factors of diabetes in Western China. Future research is needed to further evaluate these risk factors. [ABSTRACT FROM AUTHOR]

The efficacy, safety and health-economic outcomes were compared between corticosteroid and non-corticosteroid treatments in acute gout patients. All electronic literatures comparing the curative effects or full economic evaluations of corticosteroids versus non-corticosteroids on acute pain in acute gout patients and published until June 30, 2017 in any language were searched through PubMed, EMBASE, Web of Science, and Cochrane Central Register of Controlled Trials. Pooled odds ratios with 95% confidence intervals and standard(or weighted) mean difference were calculated using random-or fixed-effects models according to the I 2 statistic test of heterogeneity. Economic elevations were combined through qualitative narrative synthesis. Finally, seven randomized controlled trials(RCTs) involving 929 patients were included here and suggested corticosteroids had comparable analgesic efficacy to non-corticosteroids on day 5. As for inflammation and PGA, corticosteroids might outperform non-corticosteroids in reducing tenderness and swelling. Corticosteroids versus non-corticosteroids could significantly reduce incidence of only serious adverse advents, but not total adverse advents, with substantial heterogeneity. Qualitative narrative synthesis of economic elevation involving only one study shows corticosteroids are more cost-effective than indomethacin. The existing RCTs do not provide sufficient or precise evidence that corticosteroids are superior to non-corticosteroids in pain relief of acute gout patients. Therefore, studies on chronic use of corticosteroids or comparative studies with colchicine, tramadol and/or opiates may be needed in the future, as is patient satisfaction with analgesic control. [ABSTRACT FROM AUTHOR]

Rapid urbanization leads to losses in arable land; quantitatively analyzing the impact of urbanization on arable land is significant for arable land management. However, changes in arable land due to urbanization with scale and neighborhood effects remains poorly understood at the town scale. In this study, high-resolution historical land use data, landscape metric analysis and spatial regression were integrated to quantify the impacts of urbanization on arable land use change (abandonment and conversion) at spatial scales of 300 m–3300 m using a block size increment of 200 m and at the catchment scale in the town of Jinjing in subtropical central China. Arable land abandonment was the predominant type of arable land change and presented strong spatial autocorrelations at each spatial scale. Arable land was converted to tea fields because agricultural structure transformations were occurring around the urban cores, and the amount of arable land converted to residential land accounted for only a small proportion of the total arable land loss and had no spatial autocorrelation. The significance and robustness of the arable land changes impacted by urbanization had obvious scale effects and strong neighborhood effects in nearby regions. Compared with block scales, the catchment scale is an optimal scale for assessing the influence of urbanization and applying planning policy. Our results highlight the significance of incorporating spatial interactions in urbanization research, which can generate less biased estimations and consequently lead to proper policy implication and recommendations. In addition, multi-scale comparisons are helpful for better understanding the relationships between arable land changes and urbanization and provide further insights into the harmonious development of rural settlements and urban cores to preserve arable land. [ABSTRACT FROM AUTHOR]

Local binary pattern (LBP) is a frequently‐used texture descriptor. Lots of LBP‐variants have been proposed to improve its performance of representing textures. However, most of them ignore the global and neighbour‐difference information of an image texture. In this study, the authors propose a structural difference histogram representation by fusing the segmented structure pattern (SSP), the refined LBP (RLBP) and the neighbour‐difference pattern (NDP) for texture classification. Particularly, the segmented structure, which contains the global contour information of an image texture, is first constructed to compute its SSP histogram feature. Simultaneously, the RLBP is defined to represent the local texture information. Furthermore, the NDP is presented to describe differences between neighbours of a centre pixel in the local patch of texture images. Experimental results on Brodatz and Columbia‐Utrecht reflectance databases indicate that the proposed method can achieve the satisfactory classification accuracy compared with several representative methods. [ABSTRACT FROM AUTHOR]

The generation of free radicals was enhanced by triboelectric pulsed direct-current during the organic pollutant degradation. The self-powered electrochemical system was constructed for enhancing 4-CP removal powered by a water-driven triboelectric nanogenerator (WD-TENG). The output of current, voltage, and power of WD-TENG were studied. The removal of 4-CP and its degradation mechanism were also discussed. The results shows that the formation of hydroxyl radicals (·OH) was improved by improving PMA(Mo5+) conversion efficiency by the WD-TENG in the phosphomolybdic acid/H 2 O 2 (PMA/H 2 O 2) system, resulting the mineralizing the 4-CP via dichlorination and oxidation. The removal of 4-CP improved by 10% in 120 min in the WD-TENG powered electro-PMA/H 2 O 2 system. This study provides a promising methodology for improving the performance of self-powered electrochemical processes for the treatment of environmental pollution. [Display omitted] • The formation of hydroxyl radical (·OH) speed was accelerated by WD-TENG. • The conversion efficiency of PMA(Mo5+) was enhanced by WD-TENG in PMA/H 2 O 2 system. • The energy from liquid level difference was harvest by WD-TENG. [ABSTRACT FROM AUTHOR]

Base flow is recognized as an important hydrological pathway for NO 3 − –N export, however, the base flow contribution to NO 3 − –N loading in rice agriculture catchments remains unknown. In this study, stream discharge and NO 3 − –N concentration were observed in two contrasting rice agriculture catchments (named Tuojia and Jianshan) in subtropical central China between November 2010 and December 2013, to quantify the base flow contribution to NO 3 − –N loading and determine its relationship with rice agriculture. The results suggested that Tuojia produced more base flow (727.0 vs. 426.5 mm) and had higher base flow contribution to stream discharge (41.9% vs. 28.4%) than Jianshan did during the observation period, due to the more groundwater recharge associated with the higher areal proportion of rice agriculture in Tuojia. The average flow-weighted NO 3 − –N concentration in the base flow was higher in Tuojia than in Jianshan (1.43 vs. 1.07 mg N L −1 ), because rice agriculture could result in obvious N leaching into groundwater system. The NO 3 − –N loading via the base flow reached 0.27 kg N ha −1 month −1 in Tuojia, which contributed 36.5% of the NO 3 − –N loading via the stream discharge. These values were much greater than 0.12 kg N ha −1 month −1 and 27.3% in Jianshan. The more NO 3 − –N loading and greater base flow contribution in Tuojia were attributed to the more base flow and higher NO 3 − –N concentration in base flow associated with the intensive rice cropping. Specifically, the base flow contribution to the NO 3 − –N loading was greater during the fallow seasons than during the rice-growing seasons, likely due to the NO 3 − –N “landscape memory” effects from previous rice cropping seasons. Therefore, NO 3 − –N reduction practices in the rice agriculture catchments should be applied to mitigate the base flow contribution to NO 3 − –N loading in subtropical central China. [ABSTRACT FROM AUTHOR]

Catchment stream water total nitrogen (TN) (a) and phosphorus (TP) (b) concentrations are higher in the dry season than in the wet season, and the upstream 2000 ha are a hot zone for TN (c) and TP pollutions (d). Note: Red shading indicates stream water TN and TP concentrations saturation, with the threshold for saturation being stream water TN and TP removal efficiency below the first-order process. [Display omitted] • Stream water TN and TP concentrations were higher in dry season than in wet season. • Underlying surface conditions determines stream water TN and TP levels (67–90%). • Catchment area was greatest determinant of stream water TN and TP levels (32–69%). • Upstream were hotspots for N and P pollutions in an area of 2,000 ha. Catchment underlying surface conditions influence stream water nitrogen (N) and phosphorus (P) concentrations, but it is not known how much this characteristic affects N and P concentrations at different spatial and temporal scales, thus limiting the effectiveness of N and P controls at key spatial and temporal scales. Here, the area, elevation, mean slope gradient, and land use in eight subtropical hydrologically nested catchments were investigated, and the stream water total-N (TN) and total-P (TP) concentrations were observed from 2011 to 2017 to quantify the extent to which the underlying surface conditions of the catchment influenced stream water N and P levels. The stream water TN and TP concentrations were significantly higher in the dry season than in the wet season (2.79 vs. 2.35 mg N L-1 and 0.16 vs. 0.14 mg P L-1), indicating significant seasonal variation in the stream water N and P levels. The underlying surface conditions of the catchment, including area, elevation, mean slope gradient, and land use, were significantly correlated with the stream water N and P concentrations (p < 0.05) and explained 67.1%-90.3% of the total variation in the seasonal stream water N and P concentrations. The catchment area was the greatest determinant of stream water N and P levels, independently explaining 32.2–69.3% of the variation. Interestingly, the N and P concentrations were higher in the 2000 ha upstream catchments and decreased rapidly with increasing catchment area during both wet and dry seasons. This indicates that the saturation threshold for N and P concentrations in subtropical agricultural catchments was within the upstream 2000 ha. Therefore, an upstream catchment area of 2000 ha was likely the optimal spatial scale for implementing pollution control practices in agricultural catchments. [ABSTRACT FROM AUTHOR]

Highlights: [•] Reaction between lignin model compound and ClO2 is researched by UV–vis. [•] Reaction mechanism of lignin model compound oxidation by ClO2 is derived. [•] Kinetics for lignin model compound oxidation by ClO2 is propose. [•] Bring new door open for resolving the environmental pollution in ECF bleaching. [ABSTRACT FROM AUTHOR]

Abstract: In this paper, some key parameters, such as the system pH, the periodate concentration, and the reaction temperature, on the influence of the bagasse fiber degradation were studied based on the oxygenant of periodate. And the feasible reaction mechanism was also discussed through the FTIR characterization for bagasse fiber before and after the oxidizing reaction. As the results shown, the crystallinity of bagasse fiber decreased with the oxidation level increasing. It was interesting that the aldehyde content of the reaction system rose gradually along with cellulose degradation. Based on this result, the selective oxidation kinetics was constructed by introducing of variable factor R (the ratio of aldehyde content to the degradation of cellulose fiber), and the results shown that there was a better correlation between the dynamic model and the experimental data, so the oxidation degree of bagasse fiber oxidized by periodate can be quantitative evaluated based on this model. [Copyright &y& Elsevier]

Abstract: Activated carbon supported iron catalysts derived from pine bark were prepared by different methods using a microwave reactor. The catalyst samples were characterized by physical adsorption of N2, Fourier Transform Infrared (FTIR) spectroscopy, and Transmission Electron Microscopy (TEM). The catalyst activity was evaluated for catalytic wet peroxide oxidation of phenol at 50°C. The results showed that the catalyst prepared by preloading the metal precursor to deashed biomass before carbonization and activation in a microwave reactor possessed much higher reactivity than catalyst prepared from other methods. The acid washing to remove ash from biomass eliminated the adverse effect of metals contained in the ash on catalyst, modified the biomass tissue which favored the catalyst dispersion on the biomass. The metal pre-loading to biomass led to a more uniform dispersion of catalyst on activated carbon. The microwave treatment saved energy, shortened preparation time. High catalytic activity for the catalytic wet peroxide oxidation of phenol was observed for catalyst samples prepared from pre-loading the active metal to pre-deashed bark samples. [Copyright &y& Elsevier]

Abstract: The ash content of biomass has some negative impacts on the efficient utilization of biomass for the production of value-added products. In this paper, the techniques of ash removal from Mountain Pine Beetle (MPB) killed pine bark and switchgrass were examined systematically. Size fractionation was found to be effective for ash removal not only for the heterogeneous switchgrass but also for the homogeneous pine bark. Inorganic constituents of untreated samples and treated samples showed that leaching by water and four dilute acid solutions was effective in removing most of ash from both biomass samples. The inorganic element distributions in ash of pine bark samples before and after the acid leaching showed that substantial reductions were achieved in different main elements, Al (66%), Ca (90%), Fe (66%), K (92%), Mg (98%), Na (100%) and S (25%). For switchgrass, most of the element concentrations were reduced by water leaching because of its high content of water-soluble metals in the ash. For the purpose of practical applications of the two types of biomass, especially the underutilized pine barks, different leaching methods should be evaluated to identify the most suitable method for the removal of inorganic constituents. [Copyright &y& Elsevier]

• An improved DCNN model for semantic segmentation of wear particle images. • An end-to-end process for the wear particle classification. • An Encoder-ASPP-Decoder architecture for semantic segmentation DCNN. Automatic wear particle detection and classification has remained a high priority research area for wear condition monitoring and failure analysis. In this study, a deep convolutional neural network (DCNN) with three modules, namely, an encoder, atrous spatial pyramid pooling (ASPP), and a decoder, is constructed. Instead of using handcrafted features, the DCNN can automatically learn features through a layer-wise representation and realize semantic segmentation, i.e., segmentation and identification concurrently, of five types of wear particles in ferrograph images using end-to-end processing. Experimental results show that the DCNN achieves 82.5% accuracy. This proposed method unifies the segmentation, classification, and edge location of the wear particles into a single model, avoids the accumulation and transmission of errors caused by numerous steps applied in a traditional linear process, and improves the efficiency and accuracy of the wear particle analysis. [ABSTRACT FROM AUTHOR]

• Population balance model achieved excellent approximation of PHL floc size evolution. • High molecular weight PDADMAC resulted higher collision efficiency and stronger flocs. • Shear rate had negative effect on collision efficiency. • Electrolyte addition increased collision efficiency but reduced floc strength. In this research, population balance model was employed to investigated the effect of different molecular weights of poly(diallyldimethylammonium chloride) (PDADMAC) on flocculation process for lignin recovery. Results shows high molecular weight PDADMAC (1,100,000 g/mol) resulted in higher collision efficiency, higher restructure rate and stronger flocs than the lower molecular weight PDADMAC (200,000 g/mol). High molecular weight PDADMAC (1,100,000 g/mol) facilitated bridging while low molecular weight PDADMAC (200,000 g/mol) induced electrostatic patch flocculation mechanism. Polymer dosage, shear rate, temperature and electrolyte concentration affected both flocculation systems to different extents. Shear rate had more significant effect on the high molecular weight PDADMAC (1,100,000 g/mol) flocculation system than on low molecular weight PDADMAC (200,000 g/mol) flocculation system. The addition of a electrolyte had positive effect on the collision efficiency but negative effect on floc strength. More significant restructure at higher ionic strength was also observed. [ABSTRACT FROM AUTHOR]

• Multiple stepwise linear regression model effectively predicts riverine N levels and loadings. • Variation partitioning analysis reveals the determinants of riverine N levels and loadings. • Land use compositions decide riverine N levels and nitrate-N loadings. • Socioeconomic conditions determine riverine ammonium-N and total-N loadings. Protecting and restoring riverine environmental quality requires a quantitative understanding between riverine nitrogen (N) and various determinants, however, it is still challenging in highly naturally-and-anthropogenically differentiated agricultural catchments. The study observed riverine ammonium-N (NH 4 +−N), nitrate-N (NO 3 −−N), and total-N (TN) in the eleven Chinese subtropical agricultural catchments, to develop predicting functions for riverine N concentrations and loadings through integrating the multiple stepwise linear regression (MLR) and variation partitioning analysis (VPA). The results suggested severe riverine N pollution in the catchments, with the observed TN concentrations of 1.23–4.24 mg N L−1 and average annual TN loadings of 19.80 ± 24.72 kg N ha−1 yr−1. The mean annual flow-weighted N concentrations and loadings could be effectively predicted by the MLR (R2 = 0.49−0.87, relative error = 4.1%–153.1%), using the explanatory variables in the catchment hydrogeographic characteristics (catchment area and topographic wetness index (Twi)), land use compositions (forest and agriculture areal percentages), soil properties (bulk density (BD) and total soil N content (TSN)), and socioeconomic (livestock densities) categories. The land use compositions had the largest relative independent contributions to all the flow-weighted N concentrations (78.46%–86.11%), showing the importance of land use compositions on riverine N levels. The socioeconomic conditions had the largest relative independent contributions to the NH 4 +−N (47.92%) and TN (40.74) loadings, due to the discharge animal excretions of rich NH 4 +−N and TN into stream systems. The land use category independently contributed to 82.24% of the explainability of the NO 3 −−N loadings, probably explained to the unique characteristics of NO 3 −−N loss under different land uses. Therefore, the study provided useful ideas and tools to environment managers and agencies for controlling riverine N pollution in the subtropical China. [ABSTRACT FROM AUTHOR]

• Landscape patterns at the class level determine stream water NP levels. • Small-scale land uses of ponds and tea fields determine stream NP levels. • The PLAND and FRAC of ponds critically affect stream water N levels. • The FRAC of tea fields critically affects stream water P levels. The landscape patch amount and configuration at the class level profoundly affect stream nitrogen (N) and phosphorus (P) concentrations in agricultural catchments, but their relationships and contributions to stream NP levels remain unclear. Here, we observed the stream water NP concentrations, stream flow, and landscape pattern at the class level in 11 catchments in a Chinese subtropical agricultural catchment during 2011–2017 to quantify their relationships and contributions. Pearson correlation analysis suggested that landscape patch amount and configuration at the class level significantly affected stream water NP levels, and stepwise multiple linear regression models showed that the landscape pattern of a single land use could effectively predict the NP levels. The determination coefficients (R2) of the total nitrogen (TN) formulas were 0.539–0.811 and smaller than those of the total phosphorus (TP) formulas (0.360–0.672). The best fit was obtained for the TN concentration by using the percentage of land use (PLAND) and fractal dimension index (FRAC) of ponds as independent variables (R2 = 0.811, p < 0.01), while that for the TP concentration was obtained when the edge density (ED) of rivers was used (R2 = 0.672, p < 0.01). Variance partitioning analysis was applied to further quantify the contributions of land use classes to the NP levels. Ponds and tea fields contributed the most to the stream TN and TP levels (30.2 % and 31.1 %, respectively). The PLAND and FRAC of ponds and FRAC of tea fields were the key landscape variables determining stream water NP levels. This study provides detailed instructions and suggestions for reasonable landscape planning to control stream water NP pollution at the practical level. [ABSTRACT FROM AUTHOR]

The integrated recognition of spatio-temporal characteristics (e.g., speed, interaction with surrounding areas, and driving forces) of urbanization facilitates regional comprehensive development. In this study, a large-scale data-driven approach was formed for exploring the township urbanization process. The approach integrated logistic models to quantify urbanization speed, partial triadic analysis to reveal dynamic relationships between rural population migration and urbanization, and random forest analysis to identify the response of urbanization to spatial driving forces. A typical subtropical town was chosen to verify the approach by quantifying the spatio-temporal process of township urbanization from 1933 to 2012. The results showed that (i) urbanization speed was well reflected by the changes of time-course areas of urban cores fitted by a four-parameter logistic equation (R2 = 0.95–1.00, p < 0.001), and the relatively fast and steady developing periods were also successfully predicted, respectively; (ii) the spatio-temporal sprawl of urban cores and their interactions with the surrounding rural residential areas were well revealed and implied that the town experienced different historically aggregating and splitting trajectories; and (iii) the key drivers (township merger, elevation and distance to roads, as well as population migration) were identified in the spatial sprawl of urban cores. Our findings proved that a comprehensive approach is powerful for quantifying the spatio-temporal characteristics of the urbanization process at the township level and emphasized the importance of applying long-term historical data when researching the urbanization process. [ABSTRACT FROM AUTHOR]

In view of the low identification accuracy of the crack-detection technology of asphalt pavement under the current conditions of complex pavement (subject to strong light, water on the road, debris, and so on), an asphalt pavement crack-detection algorithm based on improved YOLOv5s was proposed by building the data set for asphalt pavement cracks. The first step was to make the following improvements to the original YOLOv5s model according to the characteristics of the asphalt pavement crack data set: The k -means++ algorithm was used to recluster the anchor points of the crack data set, and the initial anchor frame matching the crack characteristics of the asphalt pavement was obtained to replace the default initial anchor frame in the YOLOv5 original model. In the prediction part of the original model, the Convolutional Block Attention Module (CBAM) was added in the order of first channel and then space to improve the detection ability of the model to small cracks. The CIoU_Loss function was used as the regression loss function of the model to replace the GIoU_Loss function in the original model to improve the positioning accuracy of the anchor frame. The second step was to perform an ablation experiment on the improved YOLOv5s model. This would prove that each improvement scheme could increase detection ability without conflict. The final step was to compare the improved YOLOv5s model with various classic target detection models in the data set of this paper: the Crack Forest Data set (CFD), the Crack500 data set, and the Crack200 data set. The results showed that the detection of the improved YOLOv5s model on each data set was better than other target detection models. The mAP@0.5 and mAP@[0.5:0.95] of this model on the data set of this paper were 90.58% and 56.08%, respectively, which were much higher than other target detection models. These findings indicate that the improved YOLOv5s model had better detection under complex pavement conditions and could provide a theoretical basis for the automatic detection of asphalt pavement cracks. [ABSTRACT FROM AUTHOR]

Vaccines have been the primary remedy in the global fight against coronavirus disease 2019 (COVID-19). The receptor-binding domain (RBD) of the spike protein, a critical viral immunogen, is affected by the heterogeneity of its glycan structures and relatively low immunogenicity. Here, we describe a scalable synthetic platform that enables the precise synthesis of homogeneously glycosylated RBD, facilitating the elucidation of carbohydrate structure–function relationships. Five homogeneously glycosylated RBDs bearing biantennary glycans were prepared, three of which were conjugated to T-helper epitope (Tpep) from tetanus toxoid to improve their weak immune response. Relative to natural HEK293-derived RBD, synthetic RBDs with biantennary N -glycan elicited a higher level of neutralising antibodies against SARS-CoV-2 in mice. Furthermore, RBDs containing Tpep elicited significant immune responses in transgenic mice expressing human angiotensin-converting enzyme 2. Our collective data suggest that trimming the N -glycans and Tpep conjugation of RBD could potentially serve as an effective strategy for developing subunit vaccines providing efficient protection. [ABSTRACT FROM AUTHOR]

A series of donor–acceptor (D–A) type luminophores, i.e., aminoboranes containing phenothiazyl substituents 4a, 4b, and 4c, were synthesized and characterized. These aminoborane derivatives showed good air/moisture stability and excellent optical performance. The results revealed that these compounds exhibited typical intramolecular charge-transfer (ICT) and aggregation-induced emission (AIE) behavior. Moreover, these luminophores displayed a significant stimuli-induced emission behavior and obvious chromism properties with high reversibility. Upon grinding, the emission spectrum of the ground sample 4c exhibited a significant 106 nm red-shift from 461 to 567 nm. Compared to the B–N-containing compound, the C–N-containing compound 4d did not show any evident AIE properties and MFC phenomenon; hence, interesting electronic, photophysical, luminescence, and chemical properties were only observed for 4a, 4b, and 4c. Comprehensive analysis of the crystal structure and theoretical calculations revealed that the remarkable MFC phenomenon originated due to the strongly twisted conjugated backbones and loose molecular packing. [ABSTRACT FROM AUTHOR]

Population balance model successfully described the flocculation process induced by cationic polyacrylamide (CPAM) and provided an excellent agreement between the experimental and predicted floc size. • Population balance model predicted very well the experimental flocculation process. • High charge CPAM resulted in lower collision efficiency and lower restructure rate. • High charge CPAM resulted in stronger flocs. • Shear rate had negative effect on collision efficiency. In order to predict and control the CPAM induced flocculation process for lignin recovery and subsequent dissolved hemicelluloses utilization from pre-hydrolysis liquor (PHL), quantitative population balance model which can describe aggregation, breakage and restructure phenomenon in the flocculation process was developed to describe the process. The simulation results showed, in general, high charge CPAM resulted in lower collision efficiency, lower restructure rate and stronger flocs than the lower charged CPAM. The increase of CPAM dosage had positive effect on collision efficiency and restructure rate and negative effect on energy dissipation rate. With the increase of temperature, low charge density CPAM flocculation system experienced more significant change regarding energy dissipation rate and restructure rate. The collision efficiency was not significantly affected by the temperature change for both CPAMs. Shear rate had negative effect on collision efficiency, positive effect on energy dissipation rate and restructure rate. The addition of salt slightly increased the collision efficiency, decreased the energy dissipation rate for both CPAMs system. More negative effect is observed for high charge density flocculation system. [ABSTRACT FROM AUTHOR]

Deep Learning Technique (DLT) is the sub-branch of Machine Learning (ML) which assists to learn the data in multiple levels of representation and abstraction and shows impressive performance on many Artificial Intelligence (AI) tasks. This paper presents a new method to analyse the healthcare data using DLT algorithms and associated mathematical formulations. In this study, we have first developed a DLT to programme two types of deep learning neural networks, namely: (a) a two-hidden layer network, and (b) a three-hidden layer network. The data was analysed for predictability in both of these networks. Additionally, a comparison was also made with simple and multiple Linear Regression (LR). The demonstration of successful application of this method is carried out using the dataset that was constructed based on 2014 Medicare Provider Utilization and Payment Data. The results indicate a stronger case to use DLTs compared to traditional techniques like LR. Furthermore, it was identified that adding more hidden layers to neural network constructed for performing deep learning analysis did not have much impact on predictability for the dataset considered in this study. Therefore, the experimentation described in this article sets up a case for using DLTs over the traditional predictive analytics. The investigators assume that the algorithms described for deep learning is repeatable and can be applied for other types of predictive analysis on healthcare data. The observed results indicate, the accuracy obtained by DLT was 40% more accurate than the traditional multivariate LR analysis. [ABSTRACT FROM AUTHOR]

We present a self-adaptive genetic algorithm for the problem of predicting if a Medicare standardized payment to a physical therapist will be above or below the national median. The percentage of Americans 65 and over is expected to increase in the coming years, increasing the need for physical therapy services. As a result, accurate prediction of expected Medicare payments based on local factors will be of increasing importance. A self-adaptive genetic algorithm is an evolutionary algorithm in which some or all of the algorithm's parameters are evolved over the course of its execution. Self-adaptation is a useful tool both for improving the performance of evolutionary algorithms, as well as improving usability through lessening the amount of parameter tuning required of the algorithm's user. While other self-adaptive approaches tend to focus on self-adaptation of only a few parameters, our approach self-adapts all of the parameters related to crossover and mutation. We compare the performance of our self-adaptive genetic algorithm with that of logistic regression and a canonical genetic algorithm on the problem of predicting Medicare payments. Logistic regression is a commonly used benchmark for this type of problem and a canonical genetic algorithm is included to allow us to see if any performance costs arise from the self-adaptive mechanisms. Results show that our self-adaptive genetic algorithm is effective at the classification of Medicare standardized payments to physical therapists, achieving accuracies of over 93%. Performance remains strong with training sets as small as 5% of the full data set. The problem representation used by our method allows for the identification of the relevant features for classification which means that our approach is capable of simultaneously performing classification and feature selection. • Self adaptation eases the burden of parameter tuning for a genetic algorithm. • Classification of expected Medicare payments based on provider features. • Identification of relevant features for effective classification. • Performance comparable to a canonical genetic algorithm and logistic regression. [ABSTRACT FROM AUTHOR]

Purpose: A significant body of research indicates that the conflict environment is detrimental to the quality of life and well-being of civilians. This study assesses the health-related quality of life, stress, and insecurity of the West Bank, which has been engaged in conflict for seven decades, in comparison to a demographically and culturally similar population in Jordan, a neighboring nation with no conflict. We expect the Jordanian sample to report better functioning.Methods: We collected 793 surveys from university students (mean age = 20.2) in Nablus, West Bank (398 [50.2%]) and Irbid, Jordan (395 [49.8%]). The survey instrument consisted of the SF-36 to measure HRQoL, the PSS-4 to measure stress, and an insecurity scale, along with demographic characteristics.Results: Our findings indicate that outcomes in the West Bank were not significantly worse than in Jordan, and in some cases represented better functioning, especially in the SF-36 measures.Conclusions: Our counterintuitive results suggest that health and well-being outcomes are dependent on many factors in addition to conflict. For one, it may be that the better perceived health and well-being of the Palestinians is because they have developed a culture of resilience. Additionally, Jordanians are undergoing a period of instability due to internal struggles and surrounding conflicts. [ABSTRACT FROM AUTHOR]

Cinnamomum camphora is a traditional aromatic plant used to produce linalool and borneol flavors in southern China; however, its leaves also contain many other unutilized essential oils. Herein, we report geographic relationships for the yield and compositional diversity of C. camphora essential oils. The essential oils of 974 individual trees from 35 populations in 13 provinces were extracted by hydrodistillation and analyzed qualitatively and quantitatively by gas chromatography-mass spectrometry and gas chromatography-flame ionization detection, respectively. Oil yields ranged from 0.01% to 3.46%, with a significantly positive correlation with latitude and a significantly negative correlation with longitude. In total, 41 compounds were identified, including 15 monoterpenoids, 24 sesquiterpenoids, and two phenylpropanoids. Essential oil compositions varied significantly among individuals and could be categorized into various chemotypes. The six main chemotypes were eucalyptol, nerolidol, camphor, linalool, selina, and mixed types. The other 17 individual plants were chemotypically rare and exhibited high levels of methyl isoeugenol, methyl eugenol, δ-selinene, or borneol. Eucalyptol-type plants had the highest average oil yield of 1.64%, followed in decreasing order by linalool-, camphor-, mixed-, selina-, and nerolidol-type plants. In addition, the five main compounds exhibited a clear geographic gradient. Eucalyptol and linalool showed a significantly positive correlation with latitude, while selina-6-en-4-ol was significantly and negatively correlated with latitude. trans-Nerolidol and selina-6-en-4-ol showed significantly positive correlations with longitude, whereas camphor was significantly and negatively correlated with longitude. Canonical correspondence analysis indicated that environmental factors could strong effect the oil yield and essential oil profile of C. camphora. [ABSTRACT FROM AUTHOR]

Study objectives: Sleep quality is essential to health. The current study aimed to adapt and validate the Sleep Quality Questionnaire (SQQ) into Chinese language. Methods: The Chinese version of the SQQ (SQQ-C) was created following the guidelines for cross-cultural adaptation. Compliant with the COSMIN methodology, baseline data (N = 13,325) examined three validity domains and internal consistency, including content validity using the content validity index (CVI) and the cognitive debriefing and focus group (relevance, comprehensiveness and comprehensibility), construct validity using structural validity and cross‑sectional measurement invariance, and criterion validity using concurrent/convergent validity. Follow-up data (N = 3410) gathered within a mean of 168 (167–207) h interval were used to additionally assess longitudinal measurement invariance and test–retest reliability using intraclass correlation coefficient (ICC). Results: Scale-level CVI/Average was equal to 0.922; Item-level CVIs ranged from 0.889 to 1.000 (excellent), except for item 2 (0.556-fair). A panel of local experts and local participants during cognitive debriefing and focus group stated that it had sufficient relevance and comprehensibility but a slight deficiency in comprehensiveness. Confirmatory factor analysis indicated a stable two-factor structure encompassing Daytime Sleepiness Subscale and Sleep Difficulty Subscale from baseline to follow-up data. The SQQ-C-9 (without item 2) outperformed the SQQ-C-10 (full form). The SQQ-C-9 provided evidence of measurement invariance (strict) across subgroups (cohorts, gender, and age) and across time. The SQQ-C was negatively correlated with the Chinese Nonrestorative Sleep Scale and the Chinese Sleep Condition Indicator. Cronbach's alpha (α), McDonald's Omega (ω), and ICC, respectively, ranged from 0.712 to 0.838, 0.723 to 0.840, and 0.738 to 0.764 for total scale and each subscale. Conclusion: The SQQ-C exhibits adequate psychometric properties and a stable two-factor structure, and should enable valuable assessments of sleep quality in clinical and research settings. [ABSTRACT FROM AUTHOR]

Objective: To investigate the role of early initiation of rehabilitation on length of stay (LOS) and cost following total hip arthroplasty, total knee arthroplasty, or unicompartmental knee arthroplasty. Data sources: Electronic databases PubMed, CINAHL, Pedro, Embase, AMED, and the Cochrane Library were searched in July 2016. Five additional trials were identified through reference list scanning. Study selection: Eligible studies were published in English language peer-reviewed journals; included participants that had undergone total hip arthroplasty, total knee arthroplasty, or unicompartmental knee arthroplasty reported clearly defined timing of rehabilitation onset for at least two groups; and reported at least one measure of LOS or cost. Inclusion criteria were applied by 2 independent authors, with disagreements being determined by a third author. Searching identified 1,029 potential articles, of which 17 studies with 26,614 participants met the inclusion criteria. Data extraction: Data was extracted independently by 2 authors, with disagreements being determined by a third author. Methodological quality of each study was evaluated independently by 2 authors using the Downs and Black checklist. Pooled analyses were analyzed using a random-effects model with inverse variance methods to calculate standardized mean differences (SMD) and 95% confidence intervals for LOS. Data synthesis: When compared with standard care, early initiation of physical therapy demonstrated a decrease in length of stay for the 4 randomized clinical trials (SMD = -1.90; 95% CI -2.76 to -1.05; I2 = 93%) and for the quasi-experimental and 5 prospective studies (SMD = -1.47; 95% CI -1.85 to -1.10; I2 = 88%). Conclusion: Early initiation of rehabilitation following total hip arthroplasty, total knee arthroplasty, or unicompartmental knee arthroplasty is associated with a shorter LOS, a lower overall cost, with no evidence of an increased number of adverse reactions. Additional high quality studies with standardized methodology are needed to further examine the impact of early initiation of physical therapy among patients with joint replacement procedures. [ABSTRACT FROM AUTHOR]

Although constructed wetlands (CWs) are used as one relatively low-cost technology for livestock wastewater treatment, the improvement of phosphorus removal in CWs is urgently needed. In this study, a three-stage pilot-scale CW system consisting of three surface flow CWs (SFCWs; CW1, CW2, and CW3) in series from inlet to outlet was constructed to treat swine wastewater (SW) from a lagoon. The CWs were planted with Myriophyllum aquaticum . Considering different inlet loading rates, three strengths of swine wastewater (low: 33% SW, medium: 66% SW, and high: 100% SW) were fed to the CW system to determine total phosphorus (TP) removal efficiency and clarify the important role of plant harvest. Results from the period 2014–2016 indicate that the three-stage CW system had mean TP cumulative removal efficiencies and removal rates of 78.2–89.8% and 0.412–0.779 g m − 2 d − 1 respectively, under different inlet loading rates. The TP removal efficiency and removal rate constant had temporal variations, which depended on temperature condition and the annual growth pattern of M . aquaticum . The harvested phosphorus mass was 15.1–40.9 g m − 2 yr − 1 in the CWs except for CW1 with high strength SW, and contributed 22.5–59.6% of TP mass removal rate by the SFCWs. The TP removal was mainly by adsorption and precipitation in the substrate in CW1 but by uptake and multiple harvests of M . aquaticum in CW2 and CW3. The results suggest the three-stage CW system planted with M . aquaticum is suited for removing high TP concentrations from swine wastewater with a high removal efficiency. However, TP removal in high strength SW amounted to 70.1 ± 23.3%, and the outflow concentration of 17.0 ± 14.9 mg L − 1 was still high. Optimal loading rates for high strength SW still need to be investigated for the CW system presented here. [ABSTRACT FROM AUTHOR]

A series of rigid polyurethane foam/aluminum hypophosphite/expanded graphite (RPUF/AHP/EG) composites were prepared by one-step water-blown method. The effects of AHP/EG on the cell structure, thermal stability, flame retardant as well as combustion behavior of the composites were investigated by Scanning electron microscope (SEM), Thermogravimetric analysis (TG), Limiting oxygen index (LOI), UL-94 vertical burning test and Micro-combustion calorimetry (MCC). Flame-retardant tests and thermogravimetric analysis confirmed that the AHP/EG system enhanced high-temperature stability and flame-retardant performance of the composites. The char residue of RPUF/AHP15/EG15 at 700 °C was 22.4 wt%, the LOI value was 26.4 vol% with V-1 rating in UL-94 test. Thermogravimetric analysis-Fourier transform infrared spectrometer (TG-FTIR) indicated that AHP/EG system significantly inhibited the generation of hydrocarbons, CO2, CO and esters from the pyrolysis RPUF matrix. XPS analysis showed that the combination of AHP and EG could promote RPUF matrix to form aromatic and aromatic heterocyclic structure, which was benefit to the formation of dense char layer, thus achieving the purpose of flame retardancy. Based on the analysis, the flame-retardant mechanism of RPUF/AHP/EG composites was proposed. [ABSTRACT FROM AUTHOR]

[Display omitted] IL-1α is a pleiotropic cytokine that is involved in various immune responses, such as inflammatory processes and hematopoiesis. Herein, we report the semi-synthesis of Interleukin-1α by expressed threonine ligation and native chemical ligation-desulfurization. This method involves two critical steps: 1) The ligation between the peptide containing a C -terminal salicylaldehyde ester and the peptide possessing an N -terminal Thr residue, and 2) the ligation between the peptide containing a C -terminal thioester and the peptide possessing an N -terminal β-thiolated leucine. The applicability of this methodology can also be introduced into the synthesis of other Cys-free glycoproteins. [ABSTRACT FROM AUTHOR]

Background: Low back pain (LBP) is common and associated healthcare costs are significant. While clinical practice guidelines have been established in an attempt to reduce costs and healthcare utilization, it is unclear if adherence to physical therapy guidelines for those with LBP is efficacious. Therefore, the purpose of this study was to assess current evidence and evaluate the impact of physical therapy guideline adherence on subsequent healthcare costs and utilization for patients with LBP. Methods: An electronic search was conducted in PubMed, CINAHL (EBSCO Host), AMED (Ovid), and PEDro. Studies included in this review were published in peer reviewed journals and the primary mode of treatment was administered by a physical therapist. Also, the definition of adherence was clearly defined based on claims data and at least one measure of cost or utilization reported. Quality assessment was evaluated via a modified Downs and Black checklist. Due to the conceptual heterogeneity in variable measurements, data were qualitatively synthesized and stratified by reported utilization and cost measures. Results: A total of 256 results were identified and after omitting duplicates, 4 articles were retained, which were all retrospective in nature. Quality scores ranged between 19 and 21 points out of a possible 26 on the modified Downs and Black checklist. All identified studies used the same definition of guideline adherence, which focused on billing active codes and minimizing use of passive codes. The results demonstrated trends that, with a few exceptions, suggested those patients with LBP that were treated with an adherent guideline program demonstrated decreased healthcare utilization and an overall healthcare savings. Conclusion: Preliminary evidence suggests that adherence to established clinical practice guidelines may assist with decreasing healthcare utilization and costs. Additional research based on prospective randomized controlled trials are needed to provide high quality evidence regarding the impact of guideline adherence among patients with LBP. [ABSTRACT FROM AUTHOR]

The imbalance between precipitation and water evaporation has caused crop yield reduction, drought, and desertification. Furthermore, most parts of the world are short of water, including China. We proposed a low-cost polymer multilayer film to reduce water evaporation by only passing through several sunlight wavelengths necessary for photosynthesis. A series of experiments were conducted to characterize the influence of partial sunlight on the reduction of water evaporation. Evaporation containers and evaporation pans were placed in open-air (CK), under a glass shed (GS), and under a glass-shed covered with multilayer films (GMF). Our results showed a significant reduction in water evaporation under GMF. Cumulative soil surface evaporation of CK, GS and GMF over 45 days was 80.53 mm, 68.12 mm, and 56.79 mm, respectively. Under GMF, cumulative water evaporation from soil and pan surfaces decreased by 29% and 26%. The slope ( β 1 ≠ 0) of simple linear regression showed a significant relationship between evaporation time and cumulative water evaporation (p = 0.000 < α = 0.05 shown in the ANOVA table). The correlation coefficient was more than 0.91 in all treatments, suggesting a strong positive linear relationship. This study may contribute to future drought resistance and agrivoltaic sustainability development. [ABSTRACT FROM AUTHOR]