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The investigation of taxonomic diversity within island plant communities stands as a central focus in the field of island biogeography. Phylogenetic diversity is crucial for unraveling the evolutionary history, ecological functions, and species combinations within island plant communities. Island effects (area and isolation effect) may shape species distribution patterns, habitat heterogeneity affects habitat diversity, and anthropogenic disturbances can lead to species extinction and habitat destruction, thus impacting both species diversity and phylogenetic diversity. To investigate how taxonomic and phylogenetic diversity in island natural plant communities respond to island effects, habitat heterogeneity, and anthropogenic disturbances, we took the main island of Haitan (a land-bridge island) and nine surrounding islands (oceanic islands) of varying sizes as the subjects of our study on the Pingtan islands. We aim to elucidate the influence of island effects, habitat heterogeneity, and anthropogenic disturbances on taxonomic and phylogenetic diversity. The results showed that, (1) Both the taxonomic and phylogenetic diversity of plants on the Pingtan islands followed the island area effect, indicating that as the island area increases, both taxonomic and phylogenetic diversity also increase. (2) Island effects and habitat heterogeneity were found to enhance taxonomic and phylogenetic diversity, whereas anthropogenic disturbances were associated with a decrease in both taxonomic and phylogenetic diversity. Furthermore, the synergistic influence of island effects, habitat heterogeneity, and anthropogenic disturbances collectively exerted a negative impact on both taxonomic and phylogenetic diversity. (3) The contribution of explanatory variables of anthropogenic disturbances for taxonomic and phylogenetic diversity was higher than that of island effects and habitat heterogeneity. Additionally, the contribution of the explanatory variables under the combined influence of island effects, habitat heterogeneity, and anthropogenic disturbances is higher than that of the individual variables for island effects and habitat heterogeneity. These findings suggest that anthropogenic disturbances emerged as the dominant factors influencing both taxonomic and phylogenetic diversity. These findings demonstrate the intricate interplay between island effects, habitat heterogeneity, and anthropogenic disturbances, highlighting their combined influence on both taxonomic and phylogenetic diversity on island. [ABSTRACT FROM AUTHOR]

Given their intricate nature and inherent ambiguity, sarcastic texts often mask deeper emotions, making it challenging to discern the genuine feelings behind the words. The proposal of the sarcasm detection task is to assist us with more accurately understanding the true intention of the speaker. Advanced methods, such as deep learning and neural networks, are widely used in the field of sarcasm detection. However, most research mainly focuses on sarcastic texts in English, as other languages lack corpora and annotated datasets. To address the challenge of low-resource languages in sarcasm detection tasks, a zero-shot cross-lingual transfer learning method is proposed in this paper. The proposed approach is based on prompt learning and aims to assist the model with understanding downstream tasks through prompts. Specifically, the model uses prompt templates to construct training data into cloze-style questions and then trains them using a pre-trained cross-lingual language model. Combining data augmentation and contrastive learning can further improve the capacity of the model for cross-lingual transfer learning. To evaluate the performance of the proposed model, we utilize a publicly accessible sarcasm dataset in English as training data in a zero-shot cross-lingual setting. When tested with Chinese as the target language for transfer, our model achieves F1-scores of 72.14% and 76.7% on two test datasets, outperforming the strong baselines by significant margins. [ABSTRACT FROM AUTHOR]

Acute mountain sickness (AMS) is a common ailment in high-altitude areas caused by the body's inadequate adaptation to low-pressure, low-oxygen environments, leading to organ edema, oxidative stress, and impaired intestinal barrier function. The gastrointestinal tract, being the first to be affected by ischemia and hypoxia, is highly susceptible to injury. This study investigates the role of Lactobacillus delbrueckii subsp. bulgaricus in alleviating acute hypoxic-induced intestinal and tissue damage from the perspective of daily consumed lactic acid bacteria. An acute hypoxia mouse model was established to evaluate tissue injury, oxidative stress, inflammatory responses, and intestinal barrier function in various groups of mice. The results indicate that strain 4L3 significantly mitigated brain and lung edema caused by hypoxia, improved colonic tissue damage, and effectively increased the content of tight junction proteins in the ileum, reducing ileal permeability and alleviating mechanical barrier damage in the intestines due to acute hypoxia. Additionally, 4L3 helped to rebalance the intestinal microbiota. In summary, this study found that Lactobacillus delbrueckii subsp. bulgaricus strain 4L3 could alleviate acute intestinal damage caused by hypoxia, thereby reducing hypoxic stress. This suggests that probiotic lactic acid bacteria that exert beneficial effects in the intestines may alleviate acute injury under hypoxic conditions in mice, offering new insights for the prevention and treatment of AMS. [ABSTRACT FROM AUTHOR]

Inland bodies of water, such as lakes, play a crucial role in sustaining life and supporting ecosystems. However, with the rapid development of socio-economics, water resources are facing serious pollution problems, such as the eutrophication of water bodies and degradation of wetlands. Therefore, the monitoring, management, and protection of inland water resources are particularly important. In past research, empirical models and machine learning models have been widely used for the water quality assessment of inland lakes. Due to the complexity of the optical properties of inland lake water bodies, the performance of these models is often limited. To overcome the limitations of these models, this study uses in situ water quality data from 2017 to 2018 and multispectral (MS) remote sensing data from Sentinel-2 to construct experimental samples of Poyang Lake. Based on these experimental samples, we constructed a spatio-temporal ensemble model (STE) to evaluate four common water quality parameters: chlorophyll-a (Chl-a), total phosphorus (TP), total nitrogen (TN), and chemical oxygen demand (COD). The model adopts an ensemble learning strategy, improving the model's performance by merging multiple advanced machine learning algorithms. We introduced several indices related to water quality parameters as auxiliary variables, such as NDCI and Enhanced Three, and used band data and these auxiliary variables as predictive variables, thereby greatly enhancing the predictive potential of the model.The results show that the inversion accuracy of these four inversion models is high ( R 2 of 0.94, 0.88, 0.92, and 0.93; RMSE of 1.15, 0.01, 0.02, and 0.02; MAE of 0.81, 0.01, 0.09, and 0.10), indicating that the STE model has good evaluation accuracy. Meanwhile, we used the STE model to reveal the spatio-temporal distribution of Chl-a, TP, TN, and COD from 2017 to 2018, and analyzed their seasonal and spatial variation rules. The results of this study not only provide an effective and practical method for monitoring and managing water quality parameters in inland lakes, but also provide water security for socio-economic and ecological environmental safety. [ABSTRACT FROM AUTHOR]

Evaluating the carbon storage of forests and identifying the factors that influence it are essential in working towards the "dual carbon" goal. This assessment will facilitate research on carbon neutrality and promote regional ecological protection and development. This study utilized the "One Map" data of forest resources (2020) and the first year (2017) of the establishment of the national park in Wuyi Mountain National Park (WMNP). The continuous biomass expansion factor method, in conjunction with the vegetation carbon content coefficient, was employed to estimate the forest carbon storage within the park's forested areas. Subsequently, the distribution of forest carbon storage was analyzed using remote sensing estimation methods, and a comparison was made between the forest carbon storage of these two years. Finally, correlation analysis and path analysis were conducted to identify the primary factors influencing forest carbon storage. The study findings reveal that in 2020, the total carbon storage in forests reached 4.851 × 106 t C, with an average carbon density of 49.55 t C·hm−2. Furthermore, the study identified positive correlations between dominant tree species, age groups, and elevation with carbon storage, whereas slope length and aspect were found to have negative correlations. Dominant tree species were observed to have the greatest impact on forest carbon storage in both 2017 and 2020, followed by age groups. These findings offer valuable scientific insights for the implementation of forest carbon storage pilot projects in WMNP. [ABSTRACT FROM AUTHOR]

The escalation of global nitrogen deposition levels has heightened the inhibitory impact of phosphorus limitation on plant growth in subtropical forests. Plant roots area particularly sensitive tissue to nitrogen and phosphorus elements. Changes in the morphological characteristics of plant roots signify alterations in adaptive strategies. However, our understanding of resource-use strategies of roots in this environment remains limited. In this study, we conducted a 10-month experiment at the Castanopsis kawakamii Nature Reserve to evaluate the response of traits of seedling roots (such as specific root length, average diameter, nitrogen content, and phosphorus content) to nitrogen and phosphorus addition. The aim was to reveal the adaptation strategies of roots in different nitrogen and phosphorus addition concentrations. The results showed that: (1) The single phosphorus and nitrogen–phosphorus interaction addition increased the specific root length, surface area, and root phosphorus content. In addition, single nitrogen addition promotes an increase in the average root diameter. (2) Non-nitrogen phosphorus addition and single nitrogen addition tended to adopt a conservative resource-use strategy to maintain growth under low phosphorus conditions. (3) Under the single phosphorus addition and interactive addition of phosphorus and nitrogen, the roots adopted an acquisitive resource-use strategy to obtain more available phosphorus resources. Accordingly, the adaptation strategy of seedling roots can be regulated by adding appropriate concentrations of nitrogen or phosphorus, thereby promoting the natural regeneration of subtropical forests. [ABSTRACT FROM AUTHOR]

Traumatic brain injury usually triggers glial scar formation, neuroinflammation, and neurodegeneration. However, the molecular mechanisms underlying these pathological features are largely unknown. Using a mouse model of hippocampal stab injury (HSI), we observed that miR-331, a brain-enriched microRNA, was significantly downregulated in the early stage (0–7 days) of HSI. Intranasal administration of agomir-331, an upgraded product of miR-331 mimics, suppressed reactive gliosis and neuronal apoptosis and improved cognitive function in HSI mice. Finally, we identified IL-1β as a direct downstream target of miR-331, and agomir-331 treatment significantly reduced IL-1β levels in the hippocampus after acute injury. Our findings highlight, for the first time, agomir-331 as a pivotal neuroprotective agent for early rehabilitation of HSI. [ABSTRACT FROM AUTHOR]

The diffusion behavior of iodine in micro/nano-porous graphite under high-temperature conditions was studied using analysis methods such as Rutherford backscattering Spectrometry, scanning electron microscopy, X-ray diffraction, and Raman spectroscopy. The results indicate that iodine diffusion leads to the Lattice Contractions in Microcrystals, a decrease in interlayer spacing, and a rise of defect density. And the reversal or repair of microstructure change was observed: the microcrystal size of the graphite increases, the interlayer spacing appears to return to the initial state, and the defect density decreases, upon diffusion of iodine out of iodine-loaded graphite. The comparative study comparing the iodine diffusion performance of nanoporous graphite (G400 and G450) between microporous graphite (G500), showed that nanoporous graphite exhibits a better barrier to the iodine diffusion. The study on the diffusion behavior of iodine in micro/nano-porous graphite holds substantial academic and engineering value for the screening, design, and performance optimization of nuclear graphite. [ABSTRACT FROM AUTHOR]

Purpose: Type 2 diabetes mellitus (T2DM) is a persistent metabolic condition with an unknown pathophysiology. Moreover, T2DM remains a serious health risk despite advances in medication and preventive care. Randomised controlled trials (RCTs) have provided evidence that probiotics may have positive effects on glucolipid metabolism. Therefore, we performed a meta-analysis of RCTs to measure the effect of probiotic therapy on glucolipid metabolism in patients with T2DM. Methods: With no constraints on the language used in the literature, Excerpta Medica Database, PubMed, the Cochrane Library, and the Web of Science were searched for pertinent RCTs published between the date of creation and 18 August 2022. Stringent inclusion and exclusion criteria were applied by two reviewers to independently examine the literature. The risk of bias associated with the inclusion of the original studies was assessed using the Cochrane risk-of-bias tool, and Stata 15.0 was used to perform the meta-analysis. Results: Thirty-seven publications containing a total of 2502 research participants were included in the meta-analysis. The results showed that after a probiotic intervention, the experimental group showed a significant decrease in body mass index (standardised mean difference (SMD) = −0.42, 95% confidence interval (CI) [−0.76, −0.08]), fasting glucose concentration (SMD = −0.73, 95% CI [−0.97, −0.48]), fasting insulin concentration (SMD = −0.67, 95% CI [−0.99, −0.36]), glycated haemoglobin concentration (SMD = −0.55, 95% CI [−0.75, −0.35]), Homeostatic Model Assessment for Insulin Resistance score (SMD = −0.88, 95% CI [−1.17, −0.59]), triglyceride concentration (SMD = −0.30, 95% CI [−0.43, −0.17]), total cholesterol concentration (SMD = −0.27, 95% CI [−0.43, −0.11]), and low-density lipoprotein concentration (SMD = −0.20, 95% CI [−0.37, −0.04]), and an increase in high-density lipoprotein concentration (SMD = 0.31, 95% CI [0.08, 0.54]). Moreover, subgroup analyses showed that patients with a longer intervention time, or those who were treated with multiple strains of probiotics, may benefit more than those with a shorter intervention time or those who were treated with a single probiotic strain, respectively. Conclusion: Probiotic supplementation improves glucolipid metabolism in patients with T2DM, offering an alternative approach for the treatment of these patients. [ABSTRACT FROM AUTHOR]

In public goods games, it is common for agents to learn strategies from those who possess the highest utility. However, in reality, because of the lack of information, strategies and utilities from others cannot be obtained or predicted during learning and updating. To address this issue, we introduce a learning update mechanism based on aspirations. To make this model more universal, we study goods that can be shared with k-hop neighbors. Additionally, when a free rider accesses an investor, it is required to pay an access cost to him. We investigate the influence of aspiration, shared scope k, and access cost on the social invest level and utility. It is shown that large shared scope k, moderate aspiration, and moderate access cost are conducive to the maximum utilization of social benefits. However, with low aspiration, the utilities of investors are very close and limited, while both the high aspiration and high access cost could disrupt the social stability. [ABSTRACT FROM AUTHOR]

How to explain the emergence of cooperative behavior remains a significant problem. As players may hold diverse perceptions on a particular dilemma, the concept of multigames has been introduced. Therefore, a multigame is studied within various binary networks. Since group structures are common in human society and a person can participate in multiple groups, this paper studies an evolutionary multigame with high-order interaction properties. For this purpose, a uniform random hypergraph is adopted as the network structure, allowing players to interact with all nodes in the same hyperedge. First, we investigate the effect of the multigame payoff matrix differences on the evolution of cooperation and find that increasing the differences in the payoff matrix promotes cooperation on the hypergraph network. Second, we discover that an increase in the average hyperdegree of the hypergraph network promotes network reciprocity, wherein high-hyperdegree nodes influence surrounding nodes to form a cooperator cluster. Conversely, groups with a low hyperdegree are more susceptible to betrayal, leading to a decline in cooperation. [ABSTRACT FROM AUTHOR]

There have been many articles on the degradation of pollutants by binary and ternary nanocomposites in the field of photocatalysis. However, there has been no research comparing the photocatalytic performance of Rhodamine B (Rh B) between (M(M=Pt, Ag and Au)-TiO2)@MoS2 nanocomposites and binary nanocomposites. To this end, we prepared and studied (M(M=Pt, Ag and Au)-TiO2)@MoS2 nanocomposites and compared their photocatalytic degradation efficiency with binary composites and parent materials for Rhodamine B. We concluded that the best ternary polymer nanocomposite for degrading Rhodamine B is (Pt(5 wt%)-TiO2(15 wt%))@MoS2. In this work, a series of MoS2, TiO2@MoS2, and (M(M=Pt, Ag and Au)-TiO2)@MoS2 nanocomposites with various compositions were synthesized by the hydrothermal and deposition–precipitation methods, and their photocatalytic characteristics were studied in depth using X-ray diffraction (XRD), scanning electron microscopy (SEM), transmission electron microscopy (TEM), X-ray photoelectron spectroscopy (XPS) photoluminescence spectra (PL), FTIR spectra, UV–Vis DRS spectra, and BET analyzer. The results confirmed that TiO2 and M(Pt, Ag and Au) nanoparticles (NPs) were evenly distributed on MoS2 nanosheets (NSs) to form (M(M=Pt, Ag and Au)-TiO2)@MoS2 nanocomposite heterojunction. The UV–Vis absorption spectrum test results indicated that (Pt(5 wt%)-TiO2(15 wt%))@MoS2 ternary heterojunction nanocomposites exhibited the highest photocatalysis activity, with the maximum value of 99.0% compared to 93% for TiO2(15 wt%)@MoS2, 96.5% for (Ag(5 wt%)-TiO2(15 wt%))@MoS2, and 97.8% for (Au(5 wt%)-TiO2(15 wt%))@MoS2 within 9 min. The advanced structure of (Pt-TiO2)@MoS2 improved both light harvesting and electron transfer in the photocatalytic composites, contributing to remarkable catalytic effectiveness and extended durability for the photodegradation of Rhodamine B (Rh B). In-depth discussions of the potential growth and photocatalytic mechanism, which will help improve the energy and environmental fields, are included. [ABSTRACT FROM AUTHOR]

Oxide metallurgy technology can improve the microstructure of a coarse-grained heat-affected zone (CGHAZ) but introduces extra inclusions. Local corrosion behavior of the CGHAZ of a Zr–Ti–Al–RE deoxidized steel was investigated in this work using theoretical calculations and experimental verification. The modified inclusions have a (Zr–Mg–Al–Ca–RE)Ox core claded by a CaS and TiN shell. CaS dissolves first, followed by the oxide core, leaving TiN parts. This confirms that the addition of rare earth can reduce lattice distortion and prevent a galvanic couple between the inclusions and the matrix, while the chemical dissolution of CaS causes localized acidification, resulting in the pitting corrosion initiation. [ABSTRACT FROM AUTHOR]

Transition metal carbide reinforcement can improve the performance of pure W. W-(10–50) vol% TaC composites were prepared by spark plasma sintering at 2100 °C. The effect of TaC content on the microstructure, mechanical properties, and thermal conductivity of the composites was studied. The ablation resistance of the W-TaC composites was evaluated under an air plasma torch. The addition of TaC into the W matrix enhanced the densification of W-TaC composites, the density of W-40 vol% TaC composite exceeded 93%. TaC particles inhibited the growth of W grains during sintering. Reactive diffusion occurred between W and TaC, forming the solid solutions of (W,Ta)ss and (Ta,W)Css. W and TaC react to form the W2C phase at a TaC content of 50 vol%. The Vickers hardness of the composite increases from 3.06 GPa for WTA1 to 10.43 GPa for WTA5. The flexural strength reached 528 MPa in the W-40 vol% TaC composite. The thermal conductivity of W-20 vol% TaC composite was 51.2 ± 0.2 W·m−1·K−1 at 750 °C. The addition of TaC improved the ablation resistance of W-TaC composites. The mass ablation rate of W-30 vol% TaC composite was 0.0678 g·s−1. The ablation products were mainly W oxides and complex oxides of W-Ta-O. [ABSTRACT FROM AUTHOR]

Epistatic interactions are referred to as SNPs (single nucleotide polymorphisms) that affect disease development and trait expression nonlinearly, and hence identifying epistatic interactions plays a great role in explaining the pathogenesis and genetic heterogeneity of complex diseases. Many methods have been proposed for epistasis detection; nevertheless, they mainly focus on low-order epistatic interactions, two-order or three-order for instance, and often ignore high-order interactions due to computational burden. In this paper, a module detection method called MDSN is proposed for identifying high-order epistatic interactions. First, an SNP network is constructed by a construction strategy of interaction complementary, which consists of low-order SNP interactions that can be obtained from fast computations. Then, a node evaluation measure that integrates multi-topological features is proposed to improve the node expansion algorithm, where the importance of a node is comprehensively evaluated by the topological characteristics of the neighborhood. Finally, modules are detected in the constructed SNP network, which have high-order epistatic interactions associated with the disease. The MDSN was compared with four state-of-the-art methods on simulation datasets and a real Age-related Macular Degeneration dataset. The results demonstrate that MDSN has higher performance on detecting high-order interactions. [ABSTRACT FROM AUTHOR]

The chemical corrosion aging of plutonium is a very important topic. It is easy to be corroded and produces oxidation products of various valence states because of its 5f electron orbit between local and non-local. On the one hand, the phase diagram of plutonium and oxygen is complex, so there is still not enough research on typical structural phases. On the other hand, most of the studies on plutonium oxide focus on PuO2 and Pu2O3 with stoichiometric ratio, while the understanding of non-stoichiometric ratio, especially for Pu2O3-x, is not deep enough. Based on this, using the DFT + U theoretical scheme of density functional theory, we have systematically studied the structural stability, lattice parameters, electronic structure, mechanical and optical properties of six typical high temperature phases of β -Pu2O3, α -Pu2O3, γ -Pu2O3, PuO, α -PuO2, γ -PuO2. Further, the mechanical properties and optical behavior of Pu2O3-x under different oxygen vacancy concentrations are analyzed and discussed in detail. The result shows that the elasticity modulus of single crystal in mechanical properties is directly related to the oxygen/plutonium ratio and crystal system. As the number of oxygen vacancies increases, the mechanical constants continue to increase. In terms of optical properties, PuO has the best optical properties, and the light absorption rate decreases with the increase of oxygen vacancy concentration. [ABSTRACT FROM AUTHOR]

Lysyl oxidases (LOXs) are copper-dependent monoamine oxidases, and they play critical roles in extracellular matrix (ECM) remodeling. The LOX and LOX-like (LOXL) proteins also have a variety of biological functions, such as development and growth regulation, tumor suppression, and cellular senescence. However, the functions of LOXLs containing repeated scavenger receptor cysteine-rich (SRCR) domains in immunity are rarely reported. In this study, we characterized the antiviral and antibacterial functions of a lysyl oxidase-like (LOXL) protein containing tandem SRCR domains in Marsupenaeus japonicus. The mRNA level of LoxL was significantly upregulated in the hemocytes and intestines of shrimp challenged using white spot syndrome virus (WSSV) or bacteria. After the knockdown of LoxL via RNA interference, WSSV replication and bacterial loads were apparently increased, and the survival rate of the shrimp decreased significantly, suggesting that LOXL functions against pathogen infection in shrimp. Mechanistically, LOXL interacted with the envelope proteins of WSSV or with lipopolysaccharide and peptidoglycan from bacteria in shrimp challenged using WSSV or bacteria, and it promoted the expression of a battery of antimicrobial peptides (AMPs) via the induction of Dorsal nuclear translocation against viral and bacterial infection. Moreover, LOXL expression was also positively regulated by Dorsal in the shrimp challenged by pathogens. These results indicate that, by acting as a pattern recognition receptor, LOXL plays vital roles in antiviral and antibacterial innate immunity by enhancing the expression of AMPs in shrimp. [ABSTRACT FROM AUTHOR]

Compared to single-drug therapy, drug combinations have shown great potential in cancer treatment. Most of the current methods employ genomic data and chemical information to construct drug–cancer cell line features, but there is still a need to explore methods to combine topological information in the protein interaction network (PPI). Therefore, we propose a network-embedding-based prediction model, NEXGB, which integrates the corresponding protein modules of drug–cancer cell lines with PPI network information. NEXGB extracts the topological features of each protein node in a PPI network by struc2vec. Then, we combine the topological features with the target protein information of drug–cancer cell lines, to generate drug features and cancer cell line features, and utilize extreme gradient boosting (XGBoost) to predict the synergistic relationship between drug combinations and cancer cell lines. We apply our model on two recently developed datasets, the Oncology-Screen dataset (Oncology-Screen) and the large drug combination dataset (DrugCombDB). The experimental results show that NEXGB outperforms five current methods, and it effectively improves the predictive power in discovering relationships between drug combinations and cancer cell lines. This further demonstrates that the network information is valid for detecting combination therapies for cancer and other complex diseases. [ABSTRACT FROM AUTHOR]

In this paper, a symmetric power-exponent prismatic phononic crystal configuration was proposed for the vibration reduction of thin plate structures, and the mechanism of bandgap generation and the influencing factors of the band gaps were analyzed. The results showed that the proposed symmetric power-exponent prismatic phononic crystal structure has three complete band gaps of bending waves, where the width of the second band gap can go up to 1639 Hz. The band gaps of bending waves of the phononic crystal were verified using a combination of numerical simulations and experimental methods, and subsequently, the bandgap characteristics and energy-focusing effect of the phononic crystals were effectively used to suppress the bending vibration of the thin plate. With the increase in prismoid height of the structure, the width of the first band gap expanded, while the bandwidths of the other two band gaps narrowed down. It was observed that an increase in the power of the power-exponent prismoid would reduce the starting and ending frequencies of the band gaps, whereas an increase in the prismoid edge thickness would weaken the energy-focusing effect and narrow the band gaps gradually. Our research results provide a new technique and a pathway to realize vibration reduction in thin plate structures. [ABSTRACT FROM AUTHOR]

Many microRNAs (miRNAs) have been confirmed to be associated with the generation of human diseases. Capturing miRNA–disease associations (M-DAs) provides an effective way to understand the etiology of diseases. Many models for predicting M-DAs have been constructed; nevertheless, there are still several limitations, such as generally considering direct information between miRNAs and diseases, usually ignoring potential knowledge hidden in isolated miRNAs or diseases. To overcome these limitations, in this study a novel method for predicting M-DAs was developed named TLNPMD, highlights of which are the introduction of drug heuristic information and a bipartite network reconstruction strategy. Specifically, three bipartite networks, including drug–miRNA, drug–disease, and miRNA–disease, were reconstructed as weighted ones using such reconstruction strategy. Based on these weighted bipartite networks, as well as three corresponding similarity networks of drugs, miRNAs and diseases, the miRNA–drug-disease three-layer heterogeneous network was constructed. Then, this heterogeneous network was converted into three two-layer heterogeneous networks, for each of which the network path computational model was employed to predict association scores. Finally, both direct and indirect miRNA–disease paths were used to predict M-DAs. Comparative experiments of TLNPMD and other four models were performed and evaluated by five-fold and global leave-one-out cross validations, results of which show that TLNPMD has the highest AUC values among those of compared methods. In addition, case studies of two common diseases were carried out to validate the effectiveness of the TLNPMD. These experiments demonstrate that the TLNPMD may serve as a promising alternative to existing methods for predicting M-DAs. [ABSTRACT FROM AUTHOR]

The effect of various heat inputs on the microstructure and impact toughness of the simulated coarse-grained heat-affected zone (CGHAS) of a niobium microalloyed (0.14 wt.%) low-carbon steel was studied. The results showed that higher impact toughness was achieved at a low heat input of 20 kJ/cm, which resulted from the formation of acicular ferrite laths/plates. They sectioned large prior austenite grains into many smaller regions, resulting in smaller crystallographic grains and high-angle grain boundaries. Conversely, when specimens were simulated with larger heat-inputs (100, 200 kJ/cm), the microstructure of the CGHAZ was predominantly composed of granular bainite plus massive MA constituents, thus impairing the impact toughness. [ABSTRACT FROM AUTHOR]

Chaff jamming is a widely used passive interference method. A chaff cloud diffusion model for the widespread chaff cloud was presented. The high-density chaff cloud aerodynamic model can rapidly predict the chaff elements' time-varying spatial orientation, location, and overall spatial distribution. Basing on these pieces of information, the radar cross section (RCS) for the single chaff fibre can be obtained based on the method of moments (MoM). For high-density chaff clouds, the attenuation and shielding effects on the signal must be considered. This paper proposed a voxel splitting method in rotated coordinates, dividing the chaff cloud into rectangular cells with one side perpendicular to the direction of radar entry. The varied polarized RCS features of the chaff cloud were simulated and analysed on this foundation. By dividing the chaff cloud into dynamic voxels and considering the uneven density distribution of the chaff cloud and the signal attenuation in each segment caused by the continuous fluctuating shape, fine-grained simulations of the chaff cloud radar echoes can be performed. [ABSTRACT FROM AUTHOR]

In genome-wide association studies, epistasis detection is of great significance for the occurrence and diagnosis of complex human diseases, but it also faces challenges such as high dimensionality and a small data sample size. In order to cope with these challenges, several swarm intelligence methods have been introduced to identify epistasis in recent years. However, the existing methods still have some limitations, such as high-consumption and premature convergence. In this study, we proposed a multi-objective artificial bee colony (ABC) algorithm based on the scale-free network (SFMOABC). The SFMOABC incorporates the scale-free network into the ABC algorithm to guide the update and selection of solutions. In addition, the SFMOABC uses mutual information and the K2-Score of the Bayesian network as objective functions, and the opposition-based learning strategy is used to improve the search ability. Experiments were performed on both simulation datasets and a real dataset of age-related macular degeneration (AMD). The results of the simulation experiments showed that the SFMOABC has better detection power and efficiency than seven other epistasis detection methods. In the real AMD data experiment, most of the single nucleotide polymorphism combinations detected by the SFMOABC have been shown to be associated with AMD disease. Therefore, SFMOABC is a promising method for epistasis detection. [ABSTRACT FROM AUTHOR]

Kink deformation is often observed in materials with laminated layers. Graphite composed of stacked graphene layers has the unique laminated structure of carbon nanomaterials. In this study, we performed the interlayer deformation of graphite under compression using a simulation of molecular dynamics and proposed a differential geometrical method to evaluate the kink deformation. We employed "mean curvature" for the representativeness of the geometrical properties to explore the mechanism of kink deformation and the mechanical behaviors of graphite in nanoscale. The effect of the number of graphene layers and the lattice chirality of each graphene layer on kink deformation and stress–strain diagrams of compressed graphite are discussed in detail. The results showed that kink deformation occurred in compressed graphite when the strain was approximately equal to 0.02, and the potential energy of the compressed graphite proportionately increased with the increasing compressive strain. The proposed differential geometric method can not only be applied to kink deformation in nanoscale graphite, but could also be extended to solving and predicting interlayer deformation that occurs in micro- and macro-scale material structures with laminated layers. [ABSTRACT FROM AUTHOR]

Detecting genomes with similar expression patterns using clustering techniques plays an important role in gene expression data analysis. Non-negative matrix factorization (NMF) is an effective method for clustering the analysis of gene expression data. However, the NMF-based method is performed within the Euclidean space, and it is usually inappropriate for revealing the intrinsic geometric structure of data space. In order to overcome this shortcoming, Cai et al. proposed a novel algorithm, called graph regularized non-negative matrices factorization (GNMF). Motivated by the topological structure of the GNMF-based method, we propose improved graph regularized non-negative matrix factorization (GNMF) to facilitate the display of geometric structure of data space. Robust manifold non-negative matrix factorization (RM-GNMF) is designed for cancer gene clustering, leading to an enhancement of the GNMF-based algorithm in terms of robustness. We combine the l2,1-norm NMF with spectral clustering to conduct the wide-ranging experiments on the three known datasets. Clustering results indicate that the proposed method outperforms the previous methods, which displays the latest application of the RM-GNMF-based method in cancer gene clustering. [ABSTRACT FROM AUTHOR]

Black locust (Robinia pseudoacacia L. of the family Fabaceae) is an ecologically and economically important deciduous tree. However, few genomic resources are available for this forest species, and few effective expressed sequence tag-derived simple sequence repeat (EST-SSR) markers have been developed to date. In this study, paired-end sequencing was used to sequence transcriptomes of R. pseudoacacia by the Illumina HiSeq TM2000 platform, and EST-SSR loci were identified by de novo assembly. Furthermore, a total of 1697 primer pairs were successfully designed, from which 286 primers met the selection screening criteria; 94 pairs were randomly selected and tested for validation using polymerase chain reaction amplification. Forty-five primers were verified as polymorphic, with clear bands. The polymorphism information content values were 0.033-0.765, the number of alleles per locus ranged from 2 to 10, and the observed and expected heterozygosities were 0.000-0.931 and 0.035-0.810, respectively, indicating a high level of informativeness. Subsequently, 45 polymorphic EST-SSR loci were tested for amplification efficiency, using the verified primers, in an additional nine species of Leguminosae, 23 loci were amplified in more than three species, of which two loci were amplified successfully in all species. These EST-SSR markers provide a valuable tool for investigating the genetic diversity and population structure of R. pseudoacacia, constructing a DNA fingerprint database, performing quantitative trait locus mapping, and preserving genetic information. [ABSTRACT FROM AUTHOR]

China is planning to launch more and more optical remote-sensing satellites with high spatial resolution and multistep gains. Field calibration, the current operational method of satellite in-flight radiometric calibration, still does not have enough capacity to meet these demands. Gaofen-1 (GF-1), as the first satellite of the Chinese High-resolution Earth Observation System, has been specially arranged to obtain 22 images over clean ocean areas using the Wide Field Viewing camera. Following this, Rayleigh scattering calibration was carried out for the visible channels with these images after the appropriate data processing steps. To guarantee a high calibration precision, uncertainty was analyzed in advance taking into account ozone, aerosol optical depth (AOD), seawater salinity, chlorophyll concentration, wind speed and solar zenith angle. AOD and wind speed were found to be the biggest error sources, which were also closely coupled to the solar zenith angle. Therefore, the best sample data for Rayleigh scattering calibration were selected at the following solar zenith angle of 19-22º and wind speed of 5-13 m/s to reduce the reflection contributed by the water surface. The total Rayleigh scattering calibration uncertainties of visible bands are 2.44% (blue), 3.86% (green), and 4.63% (red) respectively. Compared with the recent field calibration results, the errors are -1.69% (blue), 1.83% (green), and -0.79% (red). Therefore, the Rayleigh scattering calibration can become an operational in-flight calibration method for the high spatial resolution satellites. [ABSTRACT FROM AUTHOR]

Compared with hardware upgrading, pansharpening is a low-cost way to acquire high-quality images, which usually combines multispectral images (MS) in low spatial resolution with panchromatic images (PAN) in high spatial resolution. This paper proposes a pixel-dependent spatial-detail injection network (PDSDNet). Based on a dynamic filter network, PDSDNet constructs nonlinear mapping of the simulated panchromatic band from low-resolution multispectral bands through filtering convolution regression. PDSDNet reduces the possibility of spectral distortion and enriches spatial details by improving the similarity between the simulated panchromatic band and the real panchromatic band. Moreover, PDSDNet assumes that if an ideal multispectral image that has the same resolution with the panchromatic image exists, each band of it should have the same spatial details as in the panchromatic image. Thus, the details we fill into each multispectral band are the same and they can be extracted effectively in one pass. Experimental results demonstrate that PDSDNet can generate high-quality fusion images with multispectral images and panchromatic images. Compared with BDSD, MTF-GLP-HPM-PP, and PanNet, which are widely applied on IKONOS, QuickBird, and WorldView-3 datasets, pansharpened images of the proposed method have rich spatial details and present superior visual effects without noticeable spectral and spatial distortion. [ABSTRACT FROM AUTHOR]

Evaluation of impact characteristics of carbon nanomaterials is very important and helpful for their application in nanoelectromechanical systems (NEMS). Furthermore, disclination lattice defects can generate out-of-plane deformation to control the mechanical behavior of carbon nanomaterials. In this study, we design novel stable wavy graphene sheets (GSs) using a technique based on origami and kirigami to control the exchange of carbon atoms and generate appropriate disclinations. The impact characteristics of these GSs are evaluated using molecular dynamics (MD) simulation, and the accuracy of the simulation results is verified via a theoretical analysis based on continuum mechanics. In the impact tests, the C60 fullerene is employed as an impactor, and the effects of the different shapes of wavy GSs with different disclinations, different impact sites on the curved surface, and different impact velocities are examined to investigate the impact characteristics of the wavy GSs. We find that the newly designed wavy GSs increasingly resist the kinetic energy (KE) of the impactor as the disclination density is increased, and the estimated KE propagation patterns are significantly different from those of the ideal GS. Based on their enhanced performance in the impact tests, the wavy GSs possess excellent impact behavior, which should facilitate their potential application as high-impact-resistant components in advanced NEMS. [ABSTRACT FROM AUTHOR]

Carbon nanomaterials, such as carbon nanotubes (CNTs) and graphene sheets (GSs), have been adopted as resonators in vibration-based nanomechanical sensors because of their extremely high stiffness and small size. Diamond nanothreads (DNTs) are a new class of one-dimensional carbon nanomaterials with extraordinary physical and chemical properties. Their structures are similar to that of diamond in that they possess sp 3 -bonds formed by a covalent interaction between multiple benzene molecules. In this study, we focus on investigating the mechanical properties and vibration behaviors of DNTs with and without lattice defects and examine the influence of density and configuration of lattice defects on the two them in detail, using the molecular dynamics method and a continuum mechanics approach. We find that Young's modulus and the natural frequency can be controlled by alternating the density of the lattice defects. Furthermore, we investigate and explore the use of DNTs as resonators in nanosensors. It is shown that applying an additional extremely small mass or strain to all types of DNTs significantly changes their resonance frequencies. The results show that, similar to CNTs and GSs, DNTs have potential application as resonators in nano-mass and nano-strain sensors. In particular, the vibration behaviors of DNT resonators can be controlled by alternating the density of the lattice defects to achieve the best sensitivities. [ABSTRACT FROM AUTHOR]

The intestinal flora is recognized as a significant contributor to the immune system. In this research, the protective effects of oyster peptides on immune regulation and intestinal microbiota were investigated in mice treated with cyclophosphamide. The results showed that oyster peptides restored the indexes of thymus, spleen and liver, stimulated cytokines secretion and promoted the relative mRNA levels of Th1/Th2 cytokines (IL-2, IFN-γ, IL-4 and IL-10). The mRNA levels of Occludin, Claudin-1, ZO-1, and Mucin-2 were up-regulated, and the NF-κB signaling pathway was also activated after oyster peptides administration. Furthermore, oyster peptides treatment reduced the proportion of Firmicutes/Bacteroidetes, increased the relative abundance of Alistipes, Lactobacillus, Rikenell and the content of short-chain fatty acids, and reversed the composition of intestinal microflora similar to that of normal mice. In conclusion, oyster peptides effectively ameliorated cyclophosphamide-induced intestinal damage and modified gut microbiota structure in mice, and might be utilized as a beneficial ingredient in functional foods for immune regulation. [ABSTRACT FROM AUTHOR]

Remote-sensing time-series datasets are significant for global change research and a better understanding of the Earth. However, remote-sensing acquisitions often provide sparse time series due to sensor resolution limitations and environmental factors such as cloud noise for optical data. Image transformation is the method that is often used to deal with this issue. This paper considers the deep convolution networks to learn the complex mapping between sequence images, called adaptive filter generation network (AdaFG), convolution long short-term memory network (CLSTM), and cycle-consistent generative adversarial network (CyGAN) for construction of sequence image datasets. AdaFG network uses a separable 1D convolution kernel instead of 2D kernels to capture the spatial characteristics of input sequence images and then is trained end-to-end using sequence images. CLSTM network can map between different images using the state information of multiple time-series images. CyGAN network can map an image from a source domain to a target domain without additional information. Our experiments, which were performed with unmanned aerial vehicle (UAV) and Landsat-8 datasets, show that the deep convolution networks are effective to produce high-quality time-series image datasets, and the data-driven deep convolution networks can better simulate complex and diverse nonlinear data information. [ABSTRACT FROM AUTHOR]

On a crowdsourcing platform, in order to cheat for rewards or sabotage the crowdsourcing processes, spam workers may submit numerous erroneous answers to the tasks published by requesters. This type of behavior extremely reduces the completion rate of tasks and the enthusiasm of honest users, which may lead a crowdsourcing platform to a failure. Defending against malicious attacks is an important issue in crowdsourcing, which has been extensively addressed by existing methods, e.g., verification-based defense mechanisms, data analysis solutions, trust-based defense models, and workers' properties matching mechanisms. However, verification-based defense mechanisms will consume a lot of resources, and data analysis solutions cannot motivate workers to provide high-quality services. Trust-based defense models and workers' properties matching mechanisms cannot guarantee the authenticity of information when collusion requesters publish shadow tasks to help malicious workers get more participation opportunities. To defend such collusion attacks in crowdsourcing platforms, we propose a new defense model named TruthTrust. Firstly, we define a complete life cycle system that from users' interaction to workers' recommendation, and separately define the trust value of each worker and the credence of each requester. Secondly, in order to ensure the authenticity of the information, we establish a trust model based on the CRH framework. The calculated truth value and weight are used to define the global properties of workers and requesters. Moreover, we propose a reverse mechanism to improve the resistance under attacks. Finally, extensive experiments demonstrate that TruthTrust significantly outperforms the state-of-the-art approaches in terms of effective task completion rate. [ABSTRACT FROM AUTHOR]

Carbon nanomaterials, such as carbon nanotubes (CNTs), graphene sheets (GSs), and carbyne, are an important new class of technological materials, and have been proposed as nano-mechanical sensors because of their extremely superior mechanical, thermal, and electrical performance. The present work reviews the recent studies of carbon nanomaterials-based nano-force and nano-mass sensors using mechanical analysis of vibration behavior. The mechanism of the two kinds of frequency-based nano sensors is firstly introduced with mathematical models and expressions. Afterward, the modeling perspective of carbon nanomaterials using continuum mechanical approaches as well as the determination of their material properties matching with their continuum models are concluded. Moreover, we summarize the representative works of CNTs/GSs/carbyne-based nano-mass and nano-force sensors and overview the technology for future challenges. It is hoped that the present review can provide an insight into the application of carbon nanomaterials-based nano-mechanical sensors. Showing remarkable results, carbon nanomaterials-based nano-mass and nano-force sensors perform with a much higher sensitivity than using other traditional materials as resonators, such as silicon and ZnO. Thus, more intensive investigations of carbon nanomaterials-based nano sensors are preferred and expected. [ABSTRACT FROM AUTHOR]

Remote-sensing time-series data are significant for global environmental change research and a better understanding of the Earth. However, remote-sensing acquisitions often provide sparse time series due to sensor resolution limitations and environmental factors, such as cloud noise for optical data. Image interpolation is the method that is often used to deal with this issue. This paper considers the deep learning method to learn the complex mapping of an interpolated intermediate image from predecessor and successor images, called separable convolution network for sequence image interpolation. The separable convolution network uses a separable 1D convolution kernel instead of 2D kernels to capture the spatial characteristics of input sequence images and then is trained end-to-end using sequence images. Our experiments, which were performed with unmanned aerial vehicle (UAV) and Landsat-8 datasets, show that the method is effective to produce high-quality time-series interpolated images, and the data-driven deep model can better simulate complex and diverse nonlinear image data information. [ABSTRACT FROM AUTHOR]

Community detection is a hot research direction of network science, which is of great importance to complex system analysis. Therefore, many community detection methods have been developed. Among them, evolutionary computation based ones with a single-objective function are promising in either benchmark or real data sets. However, they also encounter resolution limit problem in several scenarios. In this paper, a Multi-Objective Pigeon-Inspired Optimization (MOPIO) method is proposed for community detection with Negative Ratio Association (NRA) and Ratio Cut (RC) as its objective functions. In MOPIO, the genetic operator is used to redefine the representation and updating of pigeons. In each iteration, NRA and RC are calculated for each pigeon, and Pareto sorting scheme is utilized to judge non-dominated solutions for later crossover. A crossover strategy based on global and personal bests is designed, in which a compensation coefficient is developed to stably complete the work transition between the map and compass operator, and the landmark operator. When termination criteria were met, a leader selection strategy is employed to determine the final result from the optimal solution set. Comparison experiments of MOPIO, with MOPSO, MOGA-Net, Meme-Net and FN, are performed on real-world networks, and results indicate that MOPIO has better performance in terms of Normalized Mutual information and Adjusted Rand Index. [ABSTRACT FROM AUTHOR]

Cooperative vehicular networking has been widely studied in recent years. Existing evolution game theoretic approaches to study cooperative behavior in Vehicular Ad hoc Network (VANET) are mainly based on the assumption that VANET is constructed as a homogeneous network. This modeling method only extracts part attributes of vehicles and does not distinguish the differences between strategy and attribute. In this paper, we focus on the heterogeneous network model based on the public goods game theory for VANET. Then we propose a Dynamic Altruism Public Goods Game (DAPGG) model consisting of rational nodes, altruistic nodes, and zealots to more realistically characterize the real VANET. Rational nodes only care about their own benefits, altruistic nodes comprehensively consider the payoffs in the neighborhood, while zealots insist on behaving cooperatively. Finally, we explore the impacts of these attributes on the evolution of cooperation under different network conditions. The simulation results show that only adding altruistic nodes can effectively improve the proportion of cooperators, but it may cause conflicts between individual benefits and neighborhood benefits. Altruistic nodes together with zealots can better improve the proportion of cooperators, even if the network conditions are not suitable for the spread of cooperative behavior. [ABSTRACT FROM AUTHOR]

The production of bacteriocin is considered to be a probiotic trait of lactic acid bacteria (LAB). However, not all strains of LAB harbour bacteriocin genes, even within the same species. Moreover, the effects of bacteriocins on the host gut microbiota and on host physiological indicators are rarely studied. This study evaluated the effects of the bacteriocin-producing Lactobacillus acidophilus strain JCM1132 and its non-producing spontaneous mutant, L. acidophilus CCFM720, on the physiological statuses and gut microbiota of healthy mice. Mice that received the bacteriocin-producing strain JCM1132 exhibited reduced water and food intake. Furthermore, the administration of these strains induced significant changes in the compositional abundance of faecal microbiota at the phylum and genus levels, and some of these changes were more pronounced after one week of withdrawal. The effects of CCFM720 treatment on the gut microbiota seemed to favour the prevention of metabolic diseases to some extent. However, individuals that received JCM1132 treatment exhibited weaker inflammatory responses than those that received CCFM720 treatment. Our results indicate that treatment with bacteriocin-producing or non-producing strains can have different effects on the host. Accordingly, this trait should be considered in the applications of LAB. [ABSTRACT FROM AUTHOR]

The intumescent flame retardant ethylene–propylene–diene rubber (EPDM) was prepared using intumescent flame retardant (IFR), including ammonium polyphosphate (APP) /pentaerythrotol (PER) and expandable graphite (EG), as the flame retardant agent. The effects of IFR and EG on the flame retardancy, fire behavior, and thermal stability of the EPDM were investigated. The results show that IFR and EG have excellent synergistic flame retardant effects. When the mass ratio of IFR to EG is 3:1 and the total addition content is 40 phr, the limiting oxygen index (LOI) value of the EPDM material (EPDM/IFR/EG) can reach 30.4%, and it can pass a V-0 rating in the vertical combustion (UL-94) test. Meanwhile, during the cone calorimetry test, the heat release rate and total heat release of EPDM/IFR/EG are 69.0% and 33.3% lower than that of the pure EPDM, respectively, and the smoke release of the material also decreases significantly, suggesting that the sample shows good fire safety. In addition, the flame retardant mechanism of IFR and EG is systematically investigated by thermogravimetric analysis/infrared spectrometry (TG-IR), Fourier transform infrared spectroscopy (FTIR), X-ray photoelectron spectroscopy (XPS), and scanning electron microscopy (SEM), and the results indicate that IFR and EG have only physical interaction. Moreover, the reason why IFR exhibits a poor flame retardant effect in EPDM materials is explained. [ABSTRACT FROM AUTHOR]

There are some special merits for the orthogonal frequency division multiplexing (OFDM) chirp waveform as multiple input multiple output (MIMO) signals. This signal has high range resolution, good Doppler tolerance, and constant modulus superiority since it exploits a full bandwidth and is based on chirp signals. The correlation sidelobe peaks level are critical for the detection requirement of MIMO radar signals, however conventional OFDM chirp signals produce high autocorrelation sidelobe peaks (ASP) and cross-correlation peaks (CP), which reduces detection performance. In this paper, we explore the structure of OFDM chirp signals' autocorrelation function and proposed a scheme to reduce the designed signal's ASP by a designing suitable range of subchirp bandwidth and a segmented transmit-receive mode. Next, we explore a suitable range of interval between the chirp rates of each two signals to reduce the CP. The simulation of designed signals verifies the effectiveness of the proposed methods in the reduction of ASP and CP, with the correlation performance being compared with recent relate studies. In addition, the multiple signals detection and one-dimensional range image simulation show the good detection performance of a designed signal in MIMO radar detection. [ABSTRACT FROM AUTHOR]

We explicitly present a generalized quantum teleportation of a two-qubit entangled state protocol, which uses two pairs of partially entangled particles as quantum channel. We verify that the optimal probability of successful teleportation is determined by the smallest superposition coefficient of these partially entangled particles. However, the two-qubit entangled state to be teleported will be destroyed if teleportation fails. To solve this problem, we show a more sophisticated probabilistic resumable quantum teleportation scheme of a two-qubit entangled state, where the state to be teleported can be recovered by the sender when teleportation fails. Thus the information of the unknown state is retained during the process. Accordingly, we can repeat the teleportion process as many times as one has available quantum channels. Therefore, the quantum channels with weak entanglement can also be used to teleport unknown two-qubit entangled states successfully with a high number of repetitions, and for channels with strong entanglement only a small number of repetitions are required to guarantee successful teleportation. [ABSTRACT FROM AUTHOR]

Efficient extraction of Ni, Co, Cu, and Mn from low-grade and refractory ores is a common technical challenge. The present study proposes an Acidithiobacillus ferrooxidans-coupled leaching of Ni, Cu, Co, and Mn from oceanic polymetallic nodules and low-grade nickel sulfide ore, and focuses on the electrochemical behavior of the ores in simulated bio-leaching solutions. In the dissolution of polymetallic nodules, A. ferrooxidans facilitates the diffusion of H+ and accelerates electron transfer, producing a decrease in charge transfer resistance and promoting the Mn(IV)-preceding reaction. The use of A. ferrooxidans is beneficial for lower impedance of sulfur-nickel ore, faster diffusion rate of product layer, and better transformation of the Fe3+/Fe2+ couple and S0/S2− couple. A. ferrooxidans increases the potential difference between the nodule cathode and sulfide anode, and increases electron liberation from the sulfide ore. This motivates a significant increase in the average extraction rates of Ni, Co, Cu, and Mn in the bacterial solution. The bio-leaching efficiencies of Ni, Co, Cu, and Mn were as high as 95.4%, 97.8%, 92.2% and 97.3%, respectively, representing improvements of 17.1%, 11.5%, 14.3% and 12.9% relative to that of the germ- and Fe(III)-free acidic 9 K basic system. [ABSTRACT FROM AUTHOR]

The performance gap of buildings is commonly defined as the difference between the performance value predicted in the design stage and that measured in the post-occupancy stage. Knowledge about the performance gap of buildings is valuable in many aspects and thus is a research subject drawing much attention. Important questions that should be asked include: (1) Definition: what is the performance gap of buildings? (2) Magnitude: how large is the performance gap of buildings? (3) Techniques: how to determine the performance gap of buildings? (4) Causes: what are the reasons leading to the performance gap of buildings? (5) Solutions: how to bridge the performance gap of buildings. By collecting and analyzing more than 20 published works with reported data on the performance gap of buildings and other research articles, these important questions are addressed. Through this review state-of-the-art knowledge regarding the performance gap of buildings is presented. Major conclusions are drawn and future research directions are pointed out. [ABSTRACT FROM AUTHOR]

Assessing the performance of algorithms in solving building energy optimization (BEO) problems with different properties is essential for selecting appropriate algorithms to achieve the best design solution. This study begins with a classification of the properties of BEO problems from three perspectives, namely, design variables, objective functions, and constraints. An analytical approach and a numerical approach are proposed to determine the properties of BEO problems. Six BEO test problems with different properties, namely, continuous vs. discrete, convex vs. non-convex, linear vs. non-linear, uni-modal vs. multimodal, and single-dimensional vs. multi-dimensional, are composed to evaluate the performance of algorithms. The selected optimization algorithms for performance assessment include the discrete Armijo gradient, Particle Swarm Optimization (PSO), Hooke-Jeeves, and hybrid PSO and Hooke-Jeeves. The assessment results indicate that multimodality can cause Hooke-Jeeves and discrete Armijo gradient algorithms to fall into local optima traps. The convex, non-convex, linear and non-linear properties of uni-modal BEO problems have little impact on the performance behavior of the algorithms. The discrete Armijo gradient and Hooke-Jeeves are not recommended for solving discrete and multi-dimensional BEO problems. [ABSTRACT FROM AUTHOR]

Papillary thyroid carcinoma (PTC) is the most common type of thyroid cancer. Identifying characteristic genes of PTC are of great importance to reveal its potential genetic mechanisms. In this paper, we proposed a framework, as well as a measure named Normalized Centrality Measure (NCM), to identify characteristic genes of PTC. The framework consisted of four steps. First, both up-regulated genes and down-regulated genes, collectively called differentially expressed genes (DEGs), were screened and integrated together from four datasets, that is, GSE3467, GSE3678, GSE33630, and GSE58545; second, an interaction network of DEGs was constructed, where each node represented a gene and each edge represented an interaction between linking nodes; third, both traditional measures and the NCM measure were used to analyze the topological properties of each node in the network. Compared with traditional measures, more genes related to PTC were identified by the NCM measure; fourth, by mining the high-density subgraphs of this network and performing Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis, several meaningful results were captured, most of which were demonstrated to be associated with PTC. The experimental results proved that this network framework and the NCM measure are useful for identifying more characteristic genes of PTC. [ABSTRACT FROM AUTHOR]