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Diabetic retinopathy (DR) and age‐related macular degeneration (AMD) are two important leading causes of acquired blindness in developed countries. As accumulation of advanced glycation end products (AGEs) in retinal pigment epithelial (RPE) cells plays an important role in both DR and AMD, and the methylglyoxal (MGO) within the AGEs exerts irreversible effects on protein structure and function, it is crucial to understand the underlying mechanism of MGO‐induced RPE cell death. Using ARPE‐19 as the cell model, this study revealed that MGO induces RPE cell death through a caspase‐independent manner, which relying on reactive oxygen species (ROS) formation, mitochondrial membrane potential (MMP) loss, intracellular calcium elevation and endoplasmic reticulum (ER) stress response. Suppression of ROS generation can reverse the MGO‐induced ROS production, MMP loss, intracellular calcium increase and cell death. Moreover, store‐operated calcium channel inhibitors MRS1845 and YM‐58483, but not the inositol 1,4,5‐trisphosphate (IP3) receptor inhibitor xestospongin C, can block MGO‐induced ROS production, MMP loss and sustained intracellular calcium increase in ARPE‐19 cells. Lastly, inhibition of ER stress by salubrinal and 4‐PBA can reduce the MGO‐induced intracellular events and cell death. Therefore, our data indicate that MGO can decrease RPE cell viability, resulting from the ER stress‐dependent intracellular ROS production, MMP loss and increased intracellular calcium increase. As MGO is one of the components of drusen in AMD and is the AGEs adduct in DR, this study could provide a valuable insight into the molecular pathogenesis and therapeutic intervention of AMD and DR.

Adam Siepel and colleagues report a new computational method, LINSIGHT, that combines evolutionary conservation and functional genomic information to predict the fitness consequences of noncoding mutations in the human genome. They use LINSIGHT to show that fitness consequences of enhancer mutations depend on tissue and cell type specificity and promoter constraints. Many genetic variants that influence phenotypes of interest are located outside of protein-coding genes, yet existing methods for identifying such variants have poor predictive power. Here we introduce a new computational method, called LINSIGHT, that substantially improves the prediction of noncoding nucleotide sites at which mutations are likely to have deleterious fitness consequences, and which, therefore, are likely to be phenotypically important. LINSIGHT combines a generalized linear model for functional genomic data with a probabilistic model of molecular evolution. The method is fast and highly scalable, enabling it to exploit the 'big data' available in modern genomics. We show that LINSIGHT outperforms the best available methods in identifying human noncoding variants associated with inherited diseases. In addition, we apply LINSIGHT to an atlas of human enhancers and show that the fitness consequences at enhancers depend on cell type, tissue specificity, and constraints at associated promoters.

Sensorimotor synchronization (SMS) is the coordination of rhythmic movement with an external rhythm, ranging from finger tapping in time with a metronome to musical ensemble performance. An earlier review (Repp, 2005 ) covered tapping studies; two additional reviews (Repp, 2006a , b ) focused on music performance and on rate limits of SMS, respectively. The present article supplements and extends these earlier reviews by surveying more recent research in what appears to be a burgeoning field. The article comprises four parts, dealing with (1) conventional tapping studies, (2) other forms of moving in synchrony with external rhythms (including dance and nonhuman animals’ synchronization abilities), (3) interpersonal synchronization (including musical ensemble performance), and (4) the neuroscience of SMS. It is evident that much new knowledge about SMS has been acquired in the last 7 years.

PurposePrior research on green innovation has shown that institutional pressure stimulates enterprises to adopt green innovation. However, an institutional perspective does not explain why firms that face the same amount of institutional pressure execute different environmental practices and innovations. To address this research gap, the authors linked institutional theory with upper echelons theory and organization performance to build a comprehensive research model.Design/methodology/approachA total of 800 questionnaires were issued. The final usable questionnaires were 195, yielding a response rate of 24.38%. AMOS 23.0 was used to analyze the data and examine the relationships between the constructs in our model.FindingsInstitutional pressures affected both green innovation adoption (GIA) and the top management team's (TMT's) response. TMT's response influenced GIA. GIA was an important factor affecting firm performance. Furthermore, TMT's response mediated the relationship between institutional pressure and GIA. Institutional pressures indirectly affected green innovation performance but did not influence economic performance through GIA. Finally, TMT's response indirectly impacted firm performance through GIA.Originality/valueThe authors draw on institutional theory, upper echelons theory, and a performance-oriented perspective to explore the antecedents and consequences of GIA. This study has interesting implications for leaders and managers looking to implement green innovation and leverage it for firm performance to out compete with market rivals as well as to make the changes in collaboration with many other companies including market rivals to gain success in green innovation.

<p><b>A Comprehensive Review of the Recent Advances in Anechoic Chamber and Reverberation Chamber Designs and Measurements</b></p> <p><i>Anechoic and Reverberation Chambers</i> is a guide to the latest systematic solutions for designing anechoic chambers that rely on state-of-the-art computational electromagnetic algorithms. This essential resource contains a theoretical and practical understanding for electromagnetic compatibility and antenna testing. The solutions outlined optimise chamber performance in the structure, absorber layout and antenna positions whilst minimising the overall cost. The anechoic chamber designs are verified by measurement results from Microwave Vision Group that validate the accuracy of the solution.</p> <p><i>Anechoic and Reverberation Chambers</i> fills an important gap in the literature by providing a comprehensive reference to electromagnetic measurements, applications and over-the-air tests inside chambers. The expert contributors offer a summary of the latest developments in anechoic and reverberation chambers to help scientists and engineers apply the most recent technologies in the field. In addition, the book contains a comparison between reverberation and anechoic chambers and identifies their strengths and weaknesses. This important resource:</p> <ul> <li>Provides a systematic solution for anechoic chamber design by using state-of-the-art computational electromagnetic algorithms</li> <li>Examines both types of chamber in use, comparing and contrasting the advantages and disadvantages of each</li> <li>Reviews typical over-the-air measurements and new applications in reverberation chambers</li> <li>Offers a timely and complete reference written by authors working at the cutting edge of the technology</li> <li>Contains helpful illustrations, photographs, practical examples and comparisons between measurements and simulations</li> </ul> <p>Written for both academics and industrial engineers and designers, <i>Anechoic and Reverberation Chambers</i> explores the most recent advances in anechoic chamber and reverberation chamber designs and measurements.</p>

Abstract In evolutionary genomics, it is fundamentally important to understand how characteristics of genomic sequences, such as gene expression level, determine the rate of adaptive evolution. While numerous statistical methods, such as the McDonald–Kreitman (MK) test, are available to examine the association between genomic features and the rate of adaptation, we currently lack a statistical approach to disentangle the independent effect of a genomic feature from the effects of other correlated genomic features. To address this problem, I present a novel statistical model, the MK regression, which augments the MK test with a generalized linear model. Analogous to the classical multiple regression model, the MK regression can analyze multiple genomic features simultaneously to infer the independent effect of a genomic feature, holding constant all other genomic features. Using the MK regression, I identify numerous genomic features driving positive selection in chimpanzees. These features include well-known ones, such as local mutation rate, residue exposure level, tissue specificity, and immune genes, as well as new features not previously reported, such as gene expression level and metabolic genes. In particular, I show that highly expressed genes may have a higher adaptation rate than their weakly expressed counterparts, even though a higher expression level may impose stronger negative selection. Also, I show that metabolic genes may have a higher adaptation rate than their nonmetabolic counterparts, possibly due to recent changes in diet in primate evolution. Overall, the MK regression is a powerful approach to elucidate the genomic basis of adaptation.

Plasmon-mediated chemical reactions (PMCRs) constitute a vibrant research field, advancing such goals as using sunlight to convert abundant precursors such as CO 2 and water to useful fuels and chemicals. A key question in this burgeoning field which has not, as yet, been fully resolved, relates to the precise mechanism through which the energy absorbed through plasmonic excitation, ultimately drives such reactions. Among the multiple processes proposed, two have risen to the forefront: plasmon-increased temperature and generation of energetic charge carriers. However, it is still a great challenge to confidently separate these two effects and quantify their relative contribution to chemical reactions. Here, we describe a strategy based on the construction of a plasmonic electrode coupled with photoelectrochemistry, to quantitatively disentangle increased temperature from energetic charge carriers effects. A clear separation of the two effects facilitates the rational design of plasmonic nanostructures for efficient photochemical applications and solar energy utilization. Confidently separating the photothermal effect from the generation of energetic charge carriers and quantifying their relative contribution to chemical reactions remain a great challenge in plasmon-mediated chemical reactions. Here, authors describe a strategy based on the construction of a plasmonic electrode coupled with photoelectrochemistry to quantitatively disentangle these two effects.

There are no systemic treatments for patients with hepatocellular carcinoma (HCC) whose disease progresses during sorafenib treatment. We aimed to assess the efficacy and safety of regorafenib in patients with HCC who have progressed during sorafenib treatment. In this randomised, double-blind, parallel-group, phase 3 trial done at 152 sites in 21 countries, adults with HCC who tolerated sorafenib (≥400 mg/day for ≥20 of last 28 days of treatment), progressed on sorafenib, and had Child-Pugh A liver function were enrolled. Participants were randomly assigned (2:1) by a computer-generated randomisation list and interactive voice response system and stratified by geographical region, Eastern Cooperative Oncology Group performance status, macrovascular invasion, extrahepatic disease, and α-fetoprotein level to best supportive care plus oral regorafenib 160 mg or placebo once daily during weeks 1–3 of each 4-week cycle. Investigators, patients, and the funder were masked to treatment assignment. The primary endpoint was overall survival (defined as time from randomisation to death due to any cause) and analysed by intention to treat. This trial is registered with ClinicalTrials.gov, number NCT01774344. Between May 14, 2013, and Dec 31, 2015, 843 patients were screened, of whom 573 were enrolled and randomised (379 to regorafenib and 194 to placebo; population for efficacy analyses), and 567 initiated treatment (374 received regorafenib and 193 received placebo; population for safety analyses). Regorafenib improved overall survival with a hazard ratio of 0·63 (95% CI 0·50–0·79; one-sided p<0·0001); median survival was 10·6 months (95% CI 9·1–12·1) for regorafenib versus 7·8 months (6·3–8·8) for placebo. Adverse events were reported in all regorafenib recipients (374 [100%] of 374) and 179 (93%) of 193 placebo recipients. The most common clinically relevant grade 3 or 4 treatment-emergent events were hypertension (57 patients [15%] in the regorafenib group vs nine patients [5%] in the placebo group), hand–foot skin reaction (47 patients [13%] vs one [1%]), fatigue (34 patients [9%] vs nine patients [5%]), and diarrhoea (12 patients [3%] vs no patients). Of the 88 deaths (grade 5 adverse events) reported during the study (50 patients [13%] assigned to regorafenib and 38 [20%] assigned to placebo), seven (2%) were considered by the investigator to be related to study drug in the regorafenib group and two (1%) in the placebo group, including two patients (1%) with hepatic failure in the placebo group. Regorafenib is the only systemic treatment shown to provide survival benefit in HCC patients progressing on sorafenib treatment. Future trials should explore combinations of regorafenib with other systemic agents and third-line treatments for patients who fail or who do not tolerate the sequence of sorafenib and regorafenib. Bayer.

There is a growing interest in cell therapies using mesenchymal stromal cells (MSCs) for repairing bone defects. MSCs have the ability to differentiate into osteoprogenitors and osteoblasts as well as to form calcified bone matrix. However, the molecular mechanisms governing mineralization during osteogenic differentiation remain unclear. Non-collagenous proteins in the extracellular matrix are believed to control different aspects of the mineralization. Since osteocalcin is the most abundant non-collagenous bone matrix protein, the purpose of this study is to investigate the roles of osteocalcin in mineral species production during osteogenesis of MSCs. Using Raman spectroscopy, we found that the maturation of mineral species was affected by osteocalcin expression level. After osteocalcin was knocked down, the mineral species maturation was delayed and total hydroxyapatite was lower than the control group. In addition, the expression of osteogenic marker genes, including RUNX2, alkaline phosphatase, type I collagen, and osteonectin, was downregulated during osteogenic differentiation compared to the control group; whereas gene expression of osterix was upregulated after the knockdown. Together, osteocalcin plays an essential role for the maturation of mineral species and modulates osteogenic differentiation of MSCs. The results offer new insights into the enhancement of new bone formation, such as for the treatments of osteoporosis and fracture healing.

Predicting species' potential geographical range by species distribution models (SDMs) is central to understand their ecological requirements. However, the effects of using different modeling techniques need further investigation. In order to improve the prediction effect, we need to assess the predictive performance and stability of different SDMs. We collected the distribution data of five common tree species (Pinus massoniana, Betula platyphylla, Quercus wutaishanica, Quercus mongolica and Quercus variabilis) and simulated their potential distribution area using 13 environmental variables and six widely used SDMs: BIOCLIM, DOMAIN, MAHAL, RF, MAXENT, and SVM. Each model run was repeated 100 times (trials). We compared the predictive performance by testing the consistency between observations and simulated distributions and assessed the stability by the standard deviation, coefficient of variation, and the 99% confidence interval of Kappa and AUC values. The mean values of AUC and Kappa from MAHAL, RF, MAXENT, and SVM trials were similar and significantly higher than those from BIOCLIM and DOMAIN trials (p<0.05), while the associated standard deviations and coefficients of variation were larger for BIOCLIM and DOMAIN trials (p<0.05), and the 99% confidence intervals for AUC and Kappa values were narrower for MAHAL, RF, MAXENT, and SVM. Compared to BIOCLIM and DOMAIN, other SDMs (MAHAL, RF, MAXENT, and SVM) had higher prediction accuracy, smaller confidence intervals, and were more stable and less affected by the random variable (randomly selected pseudo-absence points). According to the prediction performance and stability of SDMs, we can divide these six SDMs into two categories: a high performance and stability group including MAHAL, RF, MAXENT, and SVM, and a low performance and stability group consisting of BIOCLIM, and DOMAIN. We highlight that choosing appropriate SDMs to address a specific problem is an important part of the modeling process.