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Summary: Advanced glycation end products (AGEs) in diabetes can cause endothelial damage. Heparin, widely known as a recognized anticoagulant, is also a multifunctional therapeutic drug. This study investigated whether heparin could ameliorate AGEs-induced endothelial injury. Remarkably, heparin effectively attenuated this cellular damage and assumed a reparative role. Furthermore, heparin inhibited the AGEs-RAGE-NFκB axis, thereby mitigating endothelial inflammatory injury. Comprehensive proteome and knockdown experiments suggested that heparin may exert a positive influence on cell growth and further alleviate pathological damage by upregulating the expression of LYAR (cell growth-regulating nucleolar protein). Diabetic mouse model was also used to further verify the changes of endothelial tissue in diabetic state and heparin intervention. In summary, these findings demonstrate that heparin has the potential to ameliorate AGEs-induced endothelial injury, opening new avenues for exploring the expanded therapeutic roles of heparin and its potential application in the management of diabetes and its associated complications.

Construction activities discharge considerable carbon emissions, causing serious environmental problems and gaining increasing attention. For the large-scale construction area, high emission intensity, and significant carbon reduction potential, embodied carbon emissions of buildings worth special studying. However, previous studies are usually post-evaluation and ignore the influences of project, construction and management. This paper focuses on critical building materials and adopts machine learning methods to realize carbon prediction at design stage. The activity data, including critical building materials, water, and energy consumption, is analyzed and 30 influencing factors at the project, construction, and management levels are identified. Three algorithms (artificial neural network, support vector regression and extreme gradient boosting) are used to develop machine learning models. The proposed methodology is applied to 70 projects in the Yangtze River Delta region of China. Results show that the established models achieved high interpretability (R2 >0.7) and small average error (5.33%), well proving theirs feasibility. Furthermore, an automated tool is developed to assist practitioners to predict the critical materials consumption and embodied carbon emissions conveniently. The proposed operable model and practical tool can efficiently support effective adjustments and improvement to reduce carbon in construction.

Abstract This study aimed to evaluate the value of dynamic contrast‐enhanced ultrasound (CEUS) combined with dynamic contrast‐enhanced magnetic resonance imaging (DCE‐MRI) in predicting pathological complete response (pCR) in patients with breast cancer receiving neoadjuvant chemotherapy (NAC). Fifty‐seven female patients with breast cancer (mean age, 50.46 years; range, 32–66 years) scheduled for NAC were recruited. CEUS and DCE‐MRI were performed before and after NAC. Imaging features and their changes were compared with postoperative pathological results. After the clinical differences were balanced using propensity score matching, univariate and multiple logistic regression analyses were used to derive the characteristics independently associated with pCR. Receiver operating characteristic curve analysis was performed to assess diagnostic performance. After six to eight cycles of NAC, 24 (42.1%) patients achieved pCR, while 33 (57.9%) did not. Multivariate analysis showed that enhancement order on CEUS and DCE‐MRI before NAC, reduction in diameter and enhancement shape on CEUS, maximum diameter on DCE‐MRI, and the type of progressive dynamic contrast enhancement after NAC were independently associated with pCR after NAC. The area under the receiver operating characteristic curve for CEUS+DCE‐MRI was 0.911 (95% confidence interval, 0.826–0.997), and the specificity and positive predictive values were 87.0% and 87.5%. CEUS and DCE‐MRI have the potential for assessing the pathological response to NAC in patients with breast cancer; their combination showed the best diagnostic performance. CEUS+DCE‐MRI has proved beneficial for comprehensive assessment and personalizing treatment strategies for patients with breast cancer.

Abstract Background Melatonin (MEL), an endogenous hormone, has been widely investigated in neurological diseases. Microglia (MG), a resident immunocyte localizing in central nervous system is reported to play important functions in the animal model of temporal lobe epilepsy (TLE). Some evidence showed that MEL influenced activation of MG, but the detailed model of action that MEL plays in remains uncertain. Methods In this study, we established a model of TLE in mice by stereotactic injection of kainic acid (KA). We treated the mice with MEL. Lipopolysaccharide, ROCK2‐knockdown (ROCK‐KD) and ‐overexpression (ROCK‐OE) of lentivirus‐treated cells were used in cell experiments to simulate an in vitro inflammatory model. Results The results of electrophysiological tests showed that MEL reduced frequency and severity of seizure. The results of behavioral tests indicated MEL improved cognition, learning, and memory ability. Histological evidences demonstrated a significant reduction of neuronal death in the hippocampus. In vivo study showed that MEL changed the polarization status of MG from a proinflammatory M1 phenotype to an anti‐inflammatory M2 phenotype by inversely regulating the RhoA/ROCK signaling pathway. In cytological study, we found that MEL had a significant protective effect in LPS‐treated BV‐2 cells and ROCK‐KD cells, while the protective effect of MEL was significantly attenuated in ROCK‐OE cells. Conclusion MEL played an antiepileptic role in the KA‐induced TLE modeling mice both in behavioral and histological levels, and changed MG polarization status by regulating the RhoA/ROCK signaling pathway.

Abstract Background Epidemiological studies have confirmed that abnormal circadian rhythms are associated with tumorigenesis in breast cancer. However, few studies have investigated the pathological roles of rhythm genes in breast cancer progression. In this study, we aimed to evaluate the aberrant expression of 32 rhythm genes in breast cancer and detect the pathological roles and molecular mechanisms of the altered rhythm gene in regulating the progression of triple negative breast cancer (TNBC). Methods The aberrant expression of rhythm genes in breast cancer was screened by searching the GEPIA database and validated by using qRT-PCR and immunohistochemistry staining. Bioinformatics analysis combined with luciferase reporter experiment and chromatinimmunopercitation (ChIP) were used to investigate the molecular mechanism about aberrant expression of identified rhythm gene in breast cancer. The pathological roles of identified rhythm gene in TNBC progression was evaluated by colony formation assay, wound healing experiment, transwell assay, subcutaneous tumor formation and the mouse tail vein injection model through gain-of-function and loss-of-function strategies respectively. mRNA array, bioinformatics analysis, luciferase reporter experiment, ChIP and immunoflurescence assay were employed to investigate the key molecules and signaling pathways by which the identified rhythm gene regulating TNBC progression. Results We identified that nuclear factor interleukin 3 regulated (NFIL3) expression is significantly altered in TNBC compared with both normal breast tissues and other subtypes of breast cancer. We found that NFIL3 inhibits its own transcription, and thus, downregulated NFIL3 mRNA indicates high expression of NFIL3 protein in breast cancer. We demonstrated that NFIL3 promotes the proliferation and metastasis of TNBC cells in vitro and in vivo, and higher expression of NFIL3 is associated with poor prognosis of patients with TNBC. We further demonstrated that NFIL3 enhances the activity of NF-κB signaling. Mechanistically, we revealed that NFIL3 directly suppresses the transcription of NFKBIA, which blocks the activation of NF-κB and inhibits the progression of TNBC cells in vitro and in vivo. Moreover, we showed that enhancing NF-κB activity by repressing NFKBIA largely mimics the oncogenic effect of NFIL3 in TNBC, and anti-inflammatory strategies targeting NF-κB activity block the oncogenic roles of NFIL3 in TNBC. Conclusion NFIL3 promotes the progression of TNBC by suppressing NFKBIA transcription and then enhancing NF-κB signaling-mediated cancer-associated inflammation. This study may provide a new target for TNBC prevention and therapy. Graphical Abstract

It is worth noting that neuroinflammation is well recognized as a symptom of neurodegenerative diseases (NDs). The regulation of neuroinflammation becomes an attractive focus for innovative ND treatment technologies. There is evidence that IL-22 is associated with the development and progression of a wide assortment of NDs. For example, IL-22 can activate glial cells, causing them to generate pro-inflammatory cytokines and encourage lymphocyte infiltration in the brain. IL-22 mRNA is highly expressed in Alzheimer’s disease (AD) patients, and a high expression of IL-22 has also been detected in the brains of patients with other NDs. We examine the role of IL-22 in the development and treatment of NDs in this review, and we believe that IL-22 has therapeutic potential in these diseases.

IntroductionEarly diagnosis could lead to a cure of colorectal cancer (CRC). Since CRC is related to aging and lifestyles, we tested if the environmental information-enriched monosaccharide composite (MC) of circulating glycans could serve as an early diagnostic biomarker for CRC. Meanwhile, we evaluated its role in predicting prognosis.MethodsHPAEC-PAD was used to quantify glycan monosaccharide compositions from a total of 467 serum samples including CRC patients, colorectal adenoma (CRA) patients and healthy individuals. Two diagnostic model was constructed by logistic regression analysis. The diagnostic performance of the two models was verified in the retrospective validation group and the prospective validation group. The prognostic performance of the model was assessed by survival analysis.ResultsThe concentrations of monosaccharides in serum were significantly higher in CRA and CRC patients than in healthy individuals. Two diagnostic models were constructed: MC1 was used to distinguish between healthy individuals and CRC; MC2 was used to distinguish between healthy individuals and CRA. Area under receptor operating characteristic curve (AUC) of MC2 and MC1 was 0.8025 and 0.9403 respectively. However, the AUC of CEA between healthy individuals and CRC was 0.7384. Moreover, in early stage of CRC (without lymph node metastasis), the positive rates of CEA and MC1 were 28% and 80%, respectively. The follow-up data showed that the increased MC1 value was associated with poor survival in patients with CRC (p=0.0010, HR=5.30).DiscussionThe MC1 model is superior to CEA in the diagnosis of CRC, especially in the early diagnosis. MC1 can be used for predicting prognosis of CRC patients, and elevated MC1 values indicate poor survival.

Abstract Background The benefit of adjuvant chemotherapy in invasive lobular carcinoma (ILC) is still unclear. The objective of the current study was to elucidate the effectiveness of adjuvant chemotherapy in hormone receptor (HR)-positive, human epidermal growth factor receptor 2 (HER2)-negative, pT1b-c/N0–1/M0 ILC. Methods Based on Surveillance, Epidemiology, and End-Results (SEER) database, we identified original 12,334 HR-positive, HER2-negative, pT1b-c/N0–1/M0 ILC patients, who were then divided into adjuvant chemotherapy group and control group. End-points were overall survival (OS) and breast cancer-specific mortality (BCSM). Aiming to minimize the selection bias of baseline characteristics, Propensity Score Matching (PSM) method was used. Results In a total of 12,334 patients with HR-positive, HER2-negative, pT1b-c/N0–1/M0 ILC, 1785 patients (14.5%) were allocated into adjuvant chemotherapy group and 10,549 (85.5%) into control group. Used PSM, the 1785 patients in adjuvant chemotherapy group matched to the 1785 patients in control group. By Kaplan-Meier survival analyses, we observed no beneficial effect of adjuvant chemotherapy on OS in both original samples (P = 0.639) and matched samples (P = 0.962), however, ineffective or even contrary results of adjuvant chemotherapy on BCSM both in original samples (P = 0.001) and in matched samples (P = 0.002). In both original and matched multivariate Cox models, we observed ineffectiveness of adjuvant chemotherapy on OS (hazard ratio (HR) for overall survival = 0.82, 95% confidence interval (CI) [0.62–1.09]; P = 0.172 and HR = 0.90, 95%CI [0.65–1.26]; P = 0.553, respectively), unexpectedly promoting effect of adjuvant chemotherapy on BCSM (HR = 2.33, 95%CI [1.47–3.67]; P = 0.001 and HR = 2.41, 95%CI [1.32–4.39]; P = 0.004, respectively). Standard surgery was beneficial to the survival of patients. Lymph node metastasis was detrimental to survival and radiotherapy brought survival benefit in original samples, but two issues had unobvious effect in matched samples. Conclusion In this study, adjuvant chemotherapy did not improve survival for patients with HR-positive, HER2-negative pT1b-c/N0–1/M0 ILC.

Interleukin 17 (IL-17) and its main producer, T cell receptor γδ cells, have neurotoxic effects in the pathogenesis of intracerebral hemorrhage (ICH), aggravating brain injuries. To investigate the correlation between IL-17 and ICH, we dynamically screened serum IL-17 concentrations using enzyme-linked immunosorbent assay and explored the clinical values of IL-17 in ICH patients. There was a significant negative correlation between serum IL-17 level and neurological recovery status in ICH patients (r = –0.498, P < 0.01). To study the neurotoxic role of IL-17, C57BL/6 mice were used to establish an ICH model by injecting autologous blood into the caudate nucleus. Subsequently, the mice were treated with mouse neural stem cells (NSCs) and/or IL-17 neutralizing antibody for 72 hours. Flow cytometry, brain water content detection, Nissl staining, and terminal deoxynucleotidyl transferase-mediated dUTP nick end labeling results indicated that NSC transplantation significantly reduced IL-17 expression in peri-hematoma tissue, but there was no difference in T cell receptor γδ cells. Compared with the ICH group, there were fewer apoptotic bodies and more Nissl bodies in the ICH + NSC group and the ICH + NSC + IL-17 group. To investigate the potential effect of IL-17 on directional differentiation of NSCs, we cultured mouse NSCs (NE-4C) alone or co-cultured them with T cell receptor γδ cells, which were isolated from mouse peripheral blood mononuclear cells, for 7 days. The results of western blot assays revealed that IL-17 secreted by T cell receptor γδ cells reduced the differentiation of NSCs into astrocytes and neurons, while IL-17 neutralization relieved the inhibition of directional differentiation into astrocytes rather than neurons. In conclusion, serum IL-17 levels were elevated in the early stage of ICH and were negatively correlated with outcome in ICH patients. Animal experiments and cytological investigations therefore demonstrated that IL-17 probably has neurotoxic roles in ICH because of its inhibitory effects on the directional differentiation of NSCs. The application of IL-17 neutralizing antibody may promote the directional differentiation of NSCs into astrocytes. This study was approved by the Clinical Research Ethics Committee of Anhui Medical University of China (For human study: Approval No. 20170135) in December 2016. All animal handling and experimentation were reviewed and approved by the Institutional Animal Care and Use Committee of Anhui Medical University (approval No. 20180248) in December 2017.

Tamoxifen (TAM) is the most commonly used adjuvant endocrine drug for hormone receptor-positive (HR+) breast cancer patients. However, how to accurately evaluate the risk of breast cancer recurrence and metastasis after adjuvant TAM therapy is still a major concern. In recent years, many studies have shown that the clinical outcomes of TAM-treated breast cancer patients are influenced by the activity of some cytochrome P450 (CYP) enzymes that catalyze the formation of active TAM metabolites like endoxifen and 4-hydroxytamoxifen. In this study, we aimed to first develop and validate an algorithm combining polymorphisms in CYP genes and clinicopathological signatures to identify a subpopulation of breast cancer patients who might benefit most from TAM adjuvant therapy and meanwhile evaluate major risk factors related to TAM resistance. Specifically, a total of 256 patients with invasive breast cancer who received adjuvant endocrine therapy were selected. The genotypes at 10 loci from three TAM metabolism-related CYP genes were detected by time-of-flight mass spectrometry and multiplex long PCR. Combining the 10 loci with nine clinicopathological characteristics, we obtained 19 important features whose association with cancer recurrence was assessed by importance score via random forests. After that, a logistic regression model was trained to calculate TAM risk-of-recurrence score (TAM RORs), which is adopted to assess a patient’s risk of recurrence after TAM treatment. The sensitivity and specificity of the model in an independent test cohort were 86.67% and 64.56%, respectively. This study showed that breast cancer patients with high TAM RORs were less sensitive to TAM treatment and manifested more invasive characteristics, whereas those with low TAM RORs were highly sensitive to TAM treatment, and their conditions were stable during the follow-up period. There were some risk factors that had a significant effect on the efficacy of TAM. They were tissue classification (tumor Grade < 2 vs. Grade ≥ 2, p = 2.2e−16), the number of lymph node metastases (Node-Negative vs. Node < 4, p = 5.3e−07; Node < 4 vs. Node ≥ 4, p = 0.003; Node-Negative vs. Node ≥ 4, p = 7.2e−15), and the expression levels of estrogen receptor (ER) and progesterone receptor (PR) (ER < 50% vs. ER ≥ 50%, p = 1.3e−12; PR < 50% vs. PR ≥ 50%, p = 2.6e−08). The really remarkable thing is that different genotypes of CYP2D6*10(C188T) show significant differences in prediction function (CYP2D6*10 CC vs. TT, p < 0.019; CYP2D6*10 CT vs. TT, p < 0.037). There are more than 50% Chinese who have CYP2D6*10 mutation. So the genotype of CYP2D6*10(C188T) should be tested before TAM therapy.

Abstract Purpose Several molecular epidemiology studies have evidenced an association of environmental factors and genetic polymorphisms with breast cancer (BC) risk. However, most have considered the functions of a single element rather than combined effects. Methods This case‐control study of 693 newly‐diagnosed BC cases and 714 cancer‐free controls evaluated the effect of multiple exposures to environmental factors and polymorphisms in CYP27B1 and IL‐13 on BC risk. Genotypes were detected using TaqMan genotyping. Combinations and interactions were analyzed using cross‐over analysis and multivariate logistic regression. Combining exposure models were assessed using classification and regression tree and multivariate logistic regression analyses. Results No significant independent association was observed for any polymorphism in CYP27B1 or IL‐13 with the risk of BC. However, significant combined effects were noted for ≥1 time/wk physical activity with rs10877012 (adjusted odds ratio [ORadj] = 0.21, 95% confidence interval [CI] = 0.11‐0.39) and rs4646536 (ORadj = 0.21, 95% CI = 0.11‐0.39) in CYP27B1. Furthermore, taking garlic ≥4 times/wk, ≥1 time/wk physical activity, and a psychological index score ≥33 all displayed significant combined effects with three IL‐13 polymorphisms. These relationships remained significant after Bonferroni correction for multiple comparisons. Combining exposure models indicated that compared with consuming garlic ≥4 times/wk, five models (model 5, ORadj = 2.94, 95% CI = 1.07‐8.06; model 6, ORadj = 10.26, 95% CI = 5.81‐18.10; model 7, ORadj = 5.05, 95% CI = 2.78‐9.17; model 8, ORadj = 3.95, 95% CI = 2.79‐5.58; and model 9, ORadj = 7.97, 95% CI = 5.26‐12.07) showed a significant increased risk. Conclusions Our findings suggest that personalized adjustments to diet and behavioral patterns may aid BC prevention in variant carriers of CYP27B1 and IL‐13.

Breast cancer (BRCA) is one of the most deadly cancers worldwide, with poor survival rates that could be due to its high proliferation. Human all-alpha dCTP pyrophosphatase 1 (DCTPP1) is implicated in numerous diseases, including cancers. However, its role in BRCA is unclear. In this study, we used bioinformatic analyses of the ONCOMINE, UALCAN, and GEPIA databases to determine the expression pattern of DCTPP1 in BRCA. We found that elevated DCTPP1 levels correlate with poor BRCA prognosis. DCTPP1 silencing inhibited BRCA cell proliferation and induced apoptosis in vitro, as well as in vivo. Our data show that this tumorigenic effect depends on DNA repair signaling. Moreover, we found that DCTPP1 is directly modulated by miR-378a-3p, whose downregulation is linked to BRCA progression. Our results showed down-regulation of miR-378a-3p in BRCA. Upregulation of miR-378a-3p, on the other hand, can inhibit BRCA cell growth and proliferation. This study shows that reduced miR-378a-3p level enhances DCTPP1 expression in BRCA, which promotes proliferation by activating DNA repair signaling in BRCA.

When encountering crisis events, systems, organizations, and people must react and handle these unpredictable events. Under these circumstances, important social functions and infrastructures must be restored or adapted as quickly as possible. This capacity refers to resilience. Although considerable research has been conducted on the resilience of infrastructure systems over the past years, a critical review of these studies remains lacking. Therefore, this study aims to bridge the knowledge gap by presenting a comprehensive review of infrastructure research conducted in the past decade, namely, from 2011 to 2021. On the basis of a systematic search, this study identified 222 journal articles investigating infrastructure resilience. A review of the identified papers revealed five research streams in the area of infrastructure resilience (IR), namely, the assessment of infrastructure resilience, improvement of infrastructure resilience, conceptualizing infrastructure resilience from various perspectives, factors influencing infrastructure resilience, and the prediction of infrastructure resilience. This study also presented some directions that future research can pursue. These directions include analyzing factors influencing infrastructure resilience based on simulation, assessing the resilience of green infrastructure, improving the resilience of interdependent infrastructure, and predicting the resilience of infrastructure based on empirical research.

Solid fuels used for heating and cooking in rural households cause a large amount of pollutant emissions. Actions are being taken to replace these solid fuels with cleaner energy carriers. However, the pollutant emission amounts from solid fuels over large areas have rarely been evaluated. In this study, we tested eight common heating stoves consuming bituminous coal chunk, anthracite coal chunk, and anthracite coal briquette; three honeycomb briquette stoves; and three traditional cookstoves consuming corn straw and wood in rural Beijing. Emission factors of particulate matter with aerodynamic diameters ≤2.5 μm (PM2.5), carbon monoxide (CO), nitrogen oxide (NOx), and sulfur dioxide (SO2), were measured as 0.08–13.74 g/kg, 10.80–148.5 g/kg, 0.52–8.44 g/kg, and 0–0.85 g/kg, respectively, for coal heating stoves; 0.35–1.11 g/kg, 16.10–109.43 g/kg, 0.51–0.75 g/kg, and 0–1.98 g/kg, respectively, for honeycomb briquette cookstoves; and 5.90–11.79 g/kg, 28.96–50.23 g/kg, 1.52–2.46 g/kg, and 0–0.05 g/kg, respectively, for traditional biomass cookstoves. Combining emission performance and solid fuel consumption, the estimated annual PM2.5, CO, NOx, and SO2 emission amounts were 26.18 Gg, 394.07 Gg, 14.56 Gg, and 1.53 Gg, respectively. The results present useful information regarding the emission inventory of common solid fuels in rural Beijing on a city-scale. This study provides an example for future intervention projects and environment evaluation in the rural areas of other cities. Keywords: Solid fuels, Rural Beijing, Heating, Cooking, Pollutant emission factors, Pollutant emission amounts

Governmental governance is an emerging concept in the area of governance and is critical to the success of megaprojects. The aims of this study are to investigate the impact of governmental governance on megaproject performance and to identify the most critical component of governmental governance. To achieve these goals, a conceptual framework of governmental governance and a comprehensive framework of megaproject performance were established first, followed by proposing a research hypothesis that governmental governance could contribute to megaproject performance. To test the hypothesis, data collected by a questionnaire administered to 239 professionals were analyzed using partial least squares structural equation modelling. Results showed that governmental governance could contribute to megaproject performance significantly. It also reported that “public monitoring and scrutiny” was the most critical latent variable of governmental governance on megaproject performance, followed by “systemic risk management,” “regulatory oversight,” “construction of clean government,” “strategic planning,” and “institutional design.” This study has contributed to the body of knowledge of governmental governance by investigating its impact on megaproject performance. The findings from this study are useful to the industry as well, because they can enhance practitioners’ understanding of governmental governance, which could help them improve their management of megaprojects eventually.

Over the past two decades, project governance has attracted increasing attention from researchers and practitioners worldwide and has become an important research area of project management. However, an inclusive quantitative and systematic analysis of the state-of-the-art recently available research in this field is still missing. This study attempts to map the global research on project governance through a state-of-the-art review. A total of 285 bibliographic records were retrieved from the Web of Science Core Collection database and analyzed by the visual analytic tool—CiteSpace. The results indicated that there has been an increasing research interest in project governance. The most productive and the most highly cited author in the area of project governance is Müller R., and most of the existing project governance research achievements are from Australia, China, USA, and Norway. By synthetically analyzing the keywords, future research might focus on governance of megaprojects and project success. Additionally, 9 knowledge domains of project governance were identified, including conceptual framework, public projects, governance structure, governance context, megaproject governance, contractual and relational governance, sustainability, portfolio governance, and project success. This study contributes to the body of knowledge by mapping the existing project governance research. It is particularly helpful to new and early-stage researchers who plan to do research on project governance, as it can provide them an overview of project governance research, including key authors, main institutions, hot topics, and knowledge domains. Moreover, the findings from the study are beneficial to industry practitioners as well, as they can help industry practitioners understand the latest development of governance theory and practice and thereby help them locate the best governance strategies for project management.

Background and rationale: Stroke is considered the most common cause of adult disability. Intensive rehabilitation protocols outperform nonintensive counterparts. The subacute stroke phase represents a potential window to recovery. Virtual reality (VR) has been shown to provide a more stimulating environment, allowing for increased patient compliance. However, the quality of current literature comparing VR with standard therapies is limited. Our aim is to measure the impact of VR versus standard therapy on the recovery of the upper limb motor function in patients with stroke in the early subacute recovery phase. Method: This is a randomized, controlled trial that will assign 262 patients to tailor-made standard rehabilitation (TMSR) or TMSR plus immersive VR device. The trial will be conducted in an urban rehabilitation clinic in the United States with expertise in the management of poststroke patients. Patients will be 18 to 70 years of age and in the early subacute period (30-90 days post ischemic stroke). The primary outcome will be the change of Fugl-Meyer Assessment—Upper Extremity (FMA-UE) score, measured at baseline and 13 weeks after randomization. The secondary outcome will be the change in the UK Functional Independence Measure and Functional Assessment Measure (UK FIM-FAM) score at the same time points. Discussion: If the use of VR in the rehabilitation of patients with stroke proves to have a significant impact on their motor recovery, it will constitute an extremely important step into decreasing the functional impairment associated with stroke and the related health care expense burden.

During the past two decades, critical infrastructures (CIs) faced a growing number of challenges worldwide due to natural disasters and other disruptive events. To respond to and handle these disasters and disruptive events, the concept of resilience was introduced to CIs. Particularly, many institutions and scholars developed various types of frameworks to assess and enhance CI resilience. The purpose of this paper is to review the resilience assessment frameworks of the CIs proposed by quality papers published in the past decade, determine and analyze the common dimensions and the key indicators of resilience assessment frameworks of CIs, and propose possible opportunities for future research. To achieve these goals, a comprehensive literature review was conducted, which identified 24 resilience assessment frameworks from 24 quality papers. This paper contributes to the current body of resilience research by identifying the common dimensions and the key indicators of the resilience assessment frameworks proposed for CIs. In addition, this paper is beneficial to the practice, because it provides a comprehensive view of the resilience assessment frameworks of CIs from the perspective of implementation, and the indicators are pragmatic and actionable in practice.

Background and aim: Quantitative digital imaging analysis to evaluate liver fibrosis is accurate, but its clinical use is limited by its high cost and lack of standardization. We aimed to validate an inexpensive digital imaging analysis technique for fibrosis quantification in chronic hepatitis B patients. Material and methods: In total, 142 chronic hepatitis B patients who underwent liver biopsy and analysis of serum fibrosis markers were included. Images of Sirius red stain sections were captured and processed using Adobe Photoshop CS3 software. The percentage of fibrosis (fibrosis index) was determined by the ratio of the fibrosis area to the total sample area, expressed in pixels, and calculated automatically. Results: A strong correlation between the fibrosis index and the Ishak, Metavir, and Laennec histological staging systems were observed (r = 0.83, 0.86, and 0.84, respectively; p < 0.001). The cutoff value associated with cirrhosis was 7.7% with an area under the receiver operating characteristic curve (AUROC) of 0.95 (95% confidence interval [CI], 0.92-0.99, p < 0.001). Furthermore, the fibrosis index yielded a cutoff value of 8.9% (AUROC, 0.74; 95% CI, 0.66-0.86), 12% (AUROC, 0.84; 95% CI, 0.75-0.93), and 14% (AUROC, 0.97; 95% CI, 0.92-1.0) for the diagnosis of cirrhosis 4a, 4b, and 4c, respectively. No serum markers or fibrosis models were correlated with the fibrosis index in Metavir F2-F4. Conclusions: The present digital imaging analysis technique is reproducible and available worldwide, allowing its use in clinical practice, and can be considered as a complementary tool to traditional histological methods.

With the improvement in laws and regulations related to children's passenger car safety and full implementation of the two-child policy in China, more and more people are going to be paying attention to child safety seats. Therefore, there will be a huge market for child safety seats of China in the future. The child safety seats withe the single function of ensuring security can no longer meet the needs of parents and children, the multi-function complex product is becoming the development mainstream gradually. And they even began to pursue appearance molding of products. Most child safety seats on the market have only one function, which can be used in specific scenarios-cars, and after purchase, low frequency of use leads to increased cost. A multi-functional child safety seat is needed to meet the more advanced and increasingly diverse needs of users on this account. This research not only takes the needs of users in different scenarios as the entry point for the design, takes the multi-function of child safety seats as the subject, and combines the behavior and psychology of the user population of relevant disciplines such as ergonomics and design psychology, but also analyzes the potential and obvious needs of the user population for child safety seats, and optimizes the existing child safety seats on the market from the aspects of child safety seat functions and appearance, so as to further meet the modern higher level of demand for child safety seats.

Abstract The Spectacled Fulvetta (Fulvetta ruficapilla) is the type species of Fulvetta, an evolutionarily distinct group whose species show a high degree of sympatry in distribution and phenotypic convergence. To pave the way for insights into their adaptive evolution and speciation, we have assembled the first high quality reference genome for F. ruficapilla using high-fidelity (HiFi) long-read and Hi-C sequencing technologies. The resulting assembly spans a total of ~1.21 Gb with a contig N50 of 18.8 Mb and scaffold N50 of 75.9 Mb, and has a BUSCO completeness of 97.0%. The quality assessment suggests a high standard in base accuracy, continuity, and completeness of the assembly, comparable or close to that of Vertebrate Genomes Project. On this basis, we have annotated 23,774 protein-coding genes, of which 18,832 are functionally identified. The availability of this high-quality genome provides a solid foundation for the future studies of evolution and local adaptation in birds.

Air-conditioning systems contribute the most to energy consumption among building equipment. Hence, energy saving for air-conditioning systems would be the essence of reducing building energy consumption. The conventional energy-saving diagnosis method through observation, test, and identification (OTI) has several drawbacks such as time consumption and narrow focus. To overcome these problems, this study proposed a systematic method for energy-saving diagnosis in air-conditioning systems based on data mining. The method mainly includes seven steps: (1) data collection, (2) data preprocessing, (3) recognition of variable-speed equipment, (4) recognition of system operation mode, (5) regression analysis of energy consumption data, (6) constraints analysis of system running, and (7) energy-saving potential analysis. A case study with a complicated air-conditioning system coupled with an ice storage system demonstrated the effectiveness of the proposed method. Compared with the traditional OTI method, the data-mining-based method can provide a more comprehensive analysis of energy-saving potential with less time cost, although it strongly relies on data quality in all steps and lacks flexibility for diagnosing specific equipment for energy-saving potential analysis. The results can deepen the understanding of the operating data characteristics of air-conditioning systems.

Increased data monitoring enables the energy-efficient operation of air-conditioning systems via data-mining. The latter is projected to have lesser consumption but more comprehensive diagnosis than traditional methods. Following the companion paper that proposed a systematic method for energy-saving potential calculations via data-mining, this article presents a detailed case study in an ice-storage air-conditioning system by employing the proposed method. Raw data were preprocessed prior to recognizing the constant- and variable-speed devices in the system. Classification and regression tree algorithms were utilized to identify the operating modes of the system. The regression models between the energy-consumption and operating-state parameters of the nine pumps and two chillers were fitted. Furthermore, the constraints pertaining to system operation were summarized. From the results, the particle swarm optimization method was applied to elucidate the benchmark energy cost and the consequent cost savings potential. The cost savings potential for the chiller plant room during the investigation duration of 59 d reached as high as 24.03%. The case study demonstrates the feasibility, effectiveness, and stability of the systematic approach. Further studies can facilitate the development of corresponding control strategies based on the potential analysis results, to investigate better optimization algorithm, and visualize the analysis process.

Motor-driven equipment (ME) is one of the key components in an air-conditioning system, which contributes to the vast majority of the total energy consumption by air-conditioning systems. Distinguishing variable- and constant-speed equipment is important since the energy simulation models of the two types differ. Traditionally, types of ME are known in advance, and energy consumption data are consequently analyzed. However, in the application scenarios of energy consumption data mining, precedent information on the ME type could be missing. Thus, this study applies this process in reverse, providing new insight into energy consumption data of ME to recognize variable-speed ME in an air-conditioning system. The energy consumption data of ME in an air-conditioning system implemented in a commercial building were collected and numerically analyzed. A proposed simple parameter, coefficient of the median, and several numerical parameters were calculated and used to distinguish variable- from constant-speed ME. Results showed that the energy consumption data distributions of the two types of ME differed. The proposed coefficient of the median could successfully distinguish variable- from constant-speed ME, and it could be applied as an important step in energy consumption data mining of air-conditioning systems.

Being an insidious risk to construction projects, collusion has attracted extensive attention from numerous researchers around the world. However, little effort has ever been made to assess collusion, which is important and necessary for curbing collusion in construction projects. Specific to the context of China, this paper developed an artificial neural network model to assess collusion risk in construction projects. Based on a comprehensive literature review, a total of 22 specific collusive practices were identified first, and then refined by a two-round Delphi interview with 15 experienced experts. Subsequently, using the consolidated framework of collusive practices, a questionnaire was further developed and disseminated, which received 97 valid replies. The questionnaire data were then utilized to develop and validate the collusion risk assessment model with the facilitation of artificial neural network approach. The developed model was finally applied in a real-life metro project in which its reliability and applicability were both verified. Although the model was developed under the context of Chinese construction projects, its developing strategy can be applied in other countries, especially for those emerging economies that have a significant concern of collusion in their construction sectors, and thus contributing to the global body of knowledge of collusion.

The construction industry contributes to a large proportion of industrial injury and mortality. It is of high importance to evaluate the effectiveness of the Safety Management System (SMS). In particular, it is necessary to compare the quality and level of achievement of SMS and safety performance of a construction project. However, a sizeable sample of construction accidents is often not available. Therefore, possible proxies to indicate the safety performance were established. Moreover, the motivation factors which characterize the quality and level of achievement should be identified. In this study, a structural model has been established to examine the relationship between the SMS implementation and operational & safety performance of the construction projects. Results of the structural model illustrated the relationship between (i) SMS implementation and project safety outcome, (ii) SMS implementation and five motivation factors, and (iii) project safety outcome and six proxies. Results of this study have unfolded the motivation factors in SMS implementation and their subsequent effects on project performance, throwing light on the need to enhance the safety management practice in order to reduce accidents and injuries in the construction industry in the long run.

The Delphi method has been used as a main research method by a growing number of researchers in the Construction Engineering and Management (CEM) field in the past two decades. Although a number of studies are available on the use of Delphi, few researchers fully examine the potential of the Delphi method in the combined use of statistical techniques, which is an inevitable trend for future Delphi research. This paper aims to review the combined use of Delphi and other quantitative methods in the CEM field based on a structured literature review of 88 relevant papers. All of the 88 papers are systematically identified from ten well-known peer-reviewed CEM journals published in the period of 1990–2012. Topic coverage, application requirements, and statistical techniques in the 88 Delphi papers are reviewed. The mix use of the Delphi method with three advanced modelling methods, such as Fuzzy sets, Analytical Hierarchy Process, and Analytical Network Process is also examined. These review results provide practical references for researchers having interests in applying Delphi method in CEM research. First published online: 21 Jan 2016