Silver spoon effects of hatching order in an asynchronous hatching bird

The silver spoon hypothesis proposes that individuals which develop under favourable conditions will gain fitness benefits throughout their lifetime. Hatching order may create a considerable size hierarchy within a brood and lead to earlier-hatched nestlings having a competitive advantage over their siblings, which has been illustrated in some studies. However, there have been few explorations into the effect on subsequent generations. Here, using a 15-year-long study, we investigated the long-term fitness consequence of hatching order in the endangered crested ibis, Nipponia nippon, a species with complete hatching asynchrony. In this study, we found strong support for silver spoon effects acting on hatching order. Compared to later-hatched nestlings, first-hatched nestlings begin reproduction at an earlier age, have higher adult survival rates, possess a longer breeding life span and achieve higher lifetime reproductive success. Interestingly, we found carry-over effects of hatching order into the next generation. Nestlings which hatched earlier and became breeders in turn also produced nestlings with larger tarsus and better body condition. Additionally, we found a positive correlation among life-history traits in crested ibis. Individuals which started reproduction at a younger age were shown to possess a longer breeding life span. And the annual brood size increased with an individual’s breeding life span. This suggests that the earlier-hatched nestlings are of better quality and the ‘silver spoon’ effects of hatching order cover all life-history stages and next generation effects

Towards graphane field emitters.

We report on the improved field emission performance of graphene foam (GF) following transient exposure to hydrogen plasma. The enhanced field emission mechanism associated with hydrogenation has been investigated using Fourier transform infrared spectroscopy, plasma spectrophotometry, Raman spectroscopy, and scanning electron microscopy. The observed enhanced electron emissionhas been attributed to an increase in the areal density of lattice defects and the formation of a partially hydrogenated, graphane-like material. The treated GF emitter demonstrated a much reduced macroscopic turn-on field (2.5 V μm-1), with an increased maximum current density from 0.21 mA cm-2 (pristine) to 8.27 mA cm-2 (treated). The treated GFs vertically orientated protrusions, after plasma etching, effectively increased the local electric field resulting in a 2.2-fold reduction in the turn-on electric field. The observed enhancement is further attributed to hydrogenation and the subsequent formation of a partially hydrogenated structured 2D material, which advantageously shifts the emitter work function. Alongside augmentation of the nominal crystallite size of the graphitic superstructure, surface bound species are believed to play a key role in the enhanced emission. The hydrogen plasma treatment was also noted to increase the emission spatial uniformity, with an approximate four times reduction in the per unit area variation in emission current density. Our findings suggest that plasma treatments, and particularly hydrogen and hydrogen-containing precursors, may provide an efficient, simple, and low cost means of realizing enhanced nanocarbon-based field emission devices via the engineered degradation of the nascent lattice, and adjustment of the surface work function.For assistance in ATR FTIR and EDXRF measurements we thank Dr Bob Keighley and Dr Ralph Vokes of Shimadzu Corp; and for plasma optical spectrophotometry analysis, Dr Thomas Schűtte of PLASUS GmbH. This work is supported by National Key Basic Research Program 973(2010CB327705), National Natural Science Foundation Project (51120125001, 51002031, 61101023, 51202028), Foundation of Doctoral Program of Ministry of Education (20100092110015), an EPSRC Impact Acceleration grant, and the Research Fund for International Young Scientists from NSFC (510501101 42, 51350110232). MT Cole thanks the Oppenheimer Trust for their generous financial support.This is the author accepted manuscript. The final version is available from the Royal Society of Chemistry via http://dx.doi.org/10.1039/C5RA20771

Prognostic value of baseline metabolic tumor volume and total lesion glycolysis in patients with lymphoma: A meta-analysis.

Whether baseline metabolic tumor volume (TMTV) and total lesion glycolysis (TLG) measured by FDG-PET/CT affected prognosis of patients with lymphoma was controversial. We searched PubMed, EMBASE and Cochrane to identify studies assessing the effect of baseline TMTV and TLG on the survival of lymphoma patients. Pooled hazard ratios (HR) for overall survival (OS) and progression-free survival (PFS) were calculated, along with 95% confidence intervals (CI). Twenty-seven eligible studies including 2,729 patients were analysed. Patients with high baseline TMTV showed a worse prognosis with an HR of 3.05 (95% CI 2.55-3.64, p<0.00001) for PFS and an HR of 3.07 (95% CI 2.47-3.82, p<0.00001) for OS. Patients with high baseline TLG also showed a worse prognosis with an HR of 3.44 (95% CI 2.37-5.01, p<0.00001) for PFS and an HR of 3.08 (95% CI 1.84-5.16, p<0.00001) for OS. A high baseline TMTV was significantly associated with worse survival in DLBCL patients treated with R-CHOP (OS, pooled HR = 3.52; PFS, pooled HR = 2.93). A high baseline TLG was significantly associated with worse survival in DLBCL patients treated with R-CHOP (OS, pooled HR = 3.06; PFS, pooled HR = 2.93). The negative effect of high baseline TMTV on PFS was demonstrated in HL (pooled HR = 3.89). A high baseline TMTV was significantly associated with worse survival in ENKL patients (OS, pooled HR = 2.24; PFS, pooled HR = 3.25). A high baseline TLG was significantly associated with worse survival in ENKL patients (OS, pooled HR = 2.58; PFS, pooled HR = 2.99). High baseline TMTV or TLG predict significantly worse PFS and OS in patients with lymphoma. Future studies are warranted to explore whether TMTV or TLG could be integrated into various prognostic models for clinical decision making

Solving the production transportation problem via a deterministic annealing neural network method

The production transportation problem is a famous NP-hard problem which is a challenge to be solved. This study develops a deterministic annealing neural network method based on Lagrange-barrier functions and two neural network models to solve the problem of this kind. According to the problem's formulation, the Lagrange function will be applied to deal with the linear equality constraints. At the same time, the barrier function will be applied to make the solution arrive at the near-global or global optimal solution. For each of the two neural network models, an iterative procedure to optimize the proposed neural network will be developed and the descent direction is obtained. Then two Lyapunov functions corresponding to the two neural network models are proposed. On the basis of the Lyapunov functions, this deterministic annealing neural network method are shown to converge to the stable equilibrium state and be completely stable. Finally, preliminary numerical results on a number of test problems show that the developed method is promising and could be expanded to other similar issues in the real world

Cascading Failure Analysis of Hierarchical Industrial Wireless Sensor Networks under the Impact of Data Overload

As industrialization accelerates, the industrial sensor network environment becomes more complex. Hierarchical multi-cluster wireless sensing network topology is generally used due to large-scale industrial environments, harsh environments, and data overload impact. In industrial wireless sensor networks, the overload of some nodes may lead to the failure of the whole network, which is called cascading failure. This phenomenon has incalculable impact on industrial production. However, cascading failure models have mainly been studied for planar structures, and there is no cascading failure model for hierarchical topologies in industrial environments. Therefore, this paper built a cascading failure model for hierarchical industrial wireless sensor networks (IWSNs) for realistic industrial network topologies. By establishing an evaluation mechanism considering the efficiency of the network and the viability of nodes, the network communication efficiency that is not considered in the traditional evaluation mechanism is solved. In addition, aiming at the problem of network topology changes caused by node failure, dynamic load distribution methods (ADD, SLD) are used to improve network invulnerability. Theoretical analysis and experimental results show that the traditional allocation method (SMLD) does not apply in hierarchical topologies; when the general cluster head node capacity is moderate, increasing the capacity of single-hop cluster head nodes can prevent cascading failures more effectively

The Detection of Quality Deterioration of Apple Juice by Near Infrared and Fluorescence Spectroscopy

International audienceProcessing and storage of apple juice often triggers quality deterioration regarding nutritional valuable compounds and unfavourable color changes resulting from browning. Fluorescence and near-infrared (NIR) spectroscopy were applied to detect such quality loss in apple juice. Juice samples were produced from Malus x domestica ’Pinova’, stored at 20 °C for 4 days or heated at 80 °C for 10 min and stored at the same conditions. The quality of apple juice was measured by standard parameters such as soluble solids content, pH, CIE L*, a*, and b* values. Juice fluorescence spectra were recorded with fluorescence excitation at 250, 266, 355, and 408 nm and emission at 280-899 nm resulting in an excitationemission- matrix (EEM) of 1240×4 for each sample. The NIR transmittance spectra were recorded in the wavelength range 900-1350 nm. The often used color b*-value for monitoring browning was correlated with the EEM variation and a reasonable calibration was built by means of n-way partial least squares (N-PLS) regression. The correlation coefficients were >0.9 in all treatments. NIR spectra were sensitive for predicting soluble solids content, but had poor capability to measure the color deterioration. Results indicated that the combination of NIR spectra and fluorescence EEM can be used to monitor the quality deterioration of apple juice

A Fault Diagnosis Methodology for Gear Pump Based on EEMD and Bayesian Network.

This paper proposes a fault diagnosis methodology for a gear pump based on the ensemble empirical mode decomposition (EEMD) method and the Bayesian network. Essentially, the presented scheme is a multi-source information fusion based methodology. Compared with the conventional fault diagnosis with only EEMD, the proposed method is able to take advantage of all useful information besides sensor signals. The presented diagnostic Bayesian network consists of a fault layer, a fault feature layer and a multi-source information layer. Vibration signals from sensor measurement are decomposed by the EEMD method and the energy of intrinsic mode functions (IMFs) are calculated as fault features. These features are added into the fault feature layer in the Bayesian network. The other sources of useful information are added to the information layer. The generalized three-layer Bayesian network can be developed by fully incorporating faults and fault symptoms as well as other useful information such as naked eye inspection and maintenance records. Therefore, diagnostic accuracy and capacity can be improved. The proposed methodology is applied to the fault diagnosis of a gear pump and the structure and parameters of the Bayesian network is established. Compared with artificial neural network and support vector machine classification algorithms, the proposed model has the best diagnostic performance when sensor data is used only. A case study has demonstrated that some information from human observation or system repair records is very helpful to the fault diagnosis. It is effective and efficient in diagnosing faults based on uncertain, incomplete information

Cascading Failure Analysis of Hierarchical Industrial Wireless Sensor Networks under the Impact of Data Overload

As industrialization accelerates, the industrial sensor network environment becomes more complex. Hierarchical multi-cluster wireless sensing network topology is generally used due to large-scale industrial environments, harsh environments, and data overload impact. In industrial wireless sensor networks, the overload of some nodes may lead to the failure of the whole network, which is called cascading failure. This phenomenon has incalculable impact on industrial production. However, cascading failure models have mainly been studied for planar structures, and there is no cascading failure model for hierarchical topologies in industrial environments. Therefore, this paper built a cascading failure model for hierarchical industrial wireless sensor networks (IWSNs) for realistic industrial network topologies. By establishing an evaluation mechanism considering the efficiency of the network and the viability of nodes, the network communication efficiency that is not considered in the traditional evaluation mechanism is solved. In addition, aiming at the problem of network topology changes caused by node failure, dynamic load distribution methods (ADD, SLD) are used to improve network invulnerability. Theoretical analysis and experimental results show that the traditional allocation method (SMLD) does not apply in hierarchical topologies; when the general cluster head node capacity is moderate, increasing the capacity of single-hop cluster head nodes can prevent cascading failures more effectively

Prognostic Value of MET Gene Copy Number and Protein Expression in Patients with Surgically Resected Non-Small Cell Lung Cancer: A Meta-Analysis of Published Literatures

<div><p>Background</p><p>The prognostic value of the copy number (GCN) and protein expression of the mesenchymal-epithelial transition (MET) gene for survival of patients with non-small cell lung cancer (NSCLC) remains controversial. This study aims to comprehensively and quantitatively asses the suitability of MET GCN and protein expression to predict patients' survival.</p><p>Methods</p><p>PubMed, Embase, Web of Science and Google Scholar were searched for articles comparing overall survival in patients with high MET GCN or protein expression with those with low level. Pooled hazard ratio (HR) and 95% confidence intervals (CIs) were calculated using the random and the fixed-effects models. Subgroup and sensitivity analyses were also performed.</p><p>Results</p><p>Eighteen eligible studies enrolling 5,516 patients were identified. Pooled analyses revealed that high MET GCN or protein expression was associated with poor overall survival (OS) (GCN: HR = 1.90, 95% CI 1.35–2.68, <i>p</i><0.001; protein expression: HR = 1.52, 95% CI 1.08–2.15, <i>p</i> = 0.017). In Asian populations (GCN: HR = 2.22, 95% CI 1.46–3.38, <i>p</i><0.001; protein expression: HR = 1.89, 95% CI 1.34–2.68, <i>p</i><0.001), but not in the non-Asian subset. For adenocarcinoma, high MET GCN or protein expression indicated decreased OS (GCN: HR = 1.49, 95% CI 1.05–2.10, <i>p</i> = 0.025; protein expression: HR = 1.69, 95% CI 1.31–2.19, <i>p</i><0.001). Results were similar for multivariate analysis (GCN: HR = 1.61, 95% CI 1.15–2.25, <i>p</i> = 0.005; protein expression: HR = 2.18, 95% CI 1.60–2.97, <i>p</i><0.001). The results of the sensitivity analysis were not materially altered and did not draw different conclusions.</p><p>Conclusions</p><p>Increased MET GCN or protein expression was significantly associated with poorer survival in patients with surgically resected NSCLC; this information could potentially further stratify patients in clinical treatment.</p></div