Stomatal responses of terrestrial plants to global change

Quantifying the stomatal responses of plants to global change factors is crucial for modeling terrestrial carbon and water cycles. Here we synthesize worldwide experimental data to show that stomatal conductance (gs) decreases with elevated carbon dioxide (CO2), warming, decreased precipitation, and tropospheric ozone pollution, but increases with increased precipitation and nitrogen (N) deposition. These responses vary with treatment magnitude, plant attributes (ambient gs, vegetation biomes, and plant functional types), and climate. All two-factor combinations (except warming + N deposition) significantly reduce gs, and their individual effects are commonly additive but tend to be antagonistic as the effect sizes increased. We further show that rising CO2 and warming would dominate the future change of plant gs across biomes. The results of our meta-analysis provide a foundation for understanding and predicting plant gs across biomes and guiding manipulative experiment designs in a real world where global change factors do not occur in isolation

Applications of stochastic models and geostatistical analyses to study sources and spatial patterns of soil heavy metals in a metalliferous industrial district of China

An extensive soil survey was conducted to study pollution sources and delineate contamination of heavy metals in one of the metalliferous industrial bases, in the karst areas of southwest China. A total of 597 topsoil samples were collected and the concentrations of five heavy metals, namely Cd, As (metalloid), Pb, Hg and Cr were analyzed. Stochastic models including a conditional inference tree (CIT) and a finite mixture distribution model (FMDM) were applied to identify the sources and partition the contribution from natural and anthropogenic sources for heavy metal in topsoils of the study area. Regression trees for Cd, As, Pb and Hg were proved to depend mostly on indicators of anthropogenic activities such as industrial type and distance from urban area, while the regression tree for Cr was found to be mainly influenced by the geogenic characteristics. The FMDM analysis showed that the geometric means of modeled background values for Cd, As, Pb, Hg and Cr were close to their background values previously reported in the study area, while the contamination of Cd and Hg were widespread in the study area, imposing potentially detrimental effects on organisms through the food chain. Finally, the probabilities of single and multiple heavy metals exceeding the threshold values derived from the FMDM were estimated using indicator kriging (IK) and multivariate indicator kriging (MVIK). The high probabilities exceeding the thresholds of heavy metals were associated with metalliferous production and atmospheric deposition of heavy metals transported from the urban and industrial areas. Geostatistics coupled with stochastic models provide an effective way to delineate multiple heavy metal pollution to facilitate improved environmental management. 2014 Elsevier B.V

Experimental Study and Numerical Simulation of the Tensile Properties of Corroded Bolt-Sphere Joints

In order to study the mechanical behavior of corroded bolt-sphere joints and predict the bearing capacity of the joints in a corrosive environment, bolt-sphere-connection and bolt-sphere-joint specimens with differing degrees of corrosion were obtained by accelerated corrosion. The tensile properties of the corroded bolt-sphere connections and the bolt-sphere joints with members were tested, respectively, and the effects of different degrees of corrosion on the tensile properties of bolted spherical joints were studied. Finally, a numerical simulation of the corroded bolt-sphere connections and bolt-sphere joints with members was carried out, and the main factors affecting the tensile performance of the corroded bolt-sphere joints was clarified; the degradation law of the tensile properties of the bolted sphere joints with service time was established

Exploring latent weight factors and global information for food-oriented cross-modal retrieval

Food-oriented cross-modal retrieval aims to retrieve relevant recipes given food images or vice versa. The modality semantic gap between recipes and food images (text and image modalities) is the main challenge. Though several studies are introduced to bridge this gap, they still suffer from two major limitations: 1) The simple embedding concatenation only can capture the simple interactions rather than complex interactions between different recipe components. 2) The image feature extraction based on convolutional neural networks only considers the local features and ignores the global features of an image, as well as the interactions between different extracted features. This paper proposes a novel method based on Latent Component Weight Factors and Global Information (LCWF-GI) to learn the robust recipe and image representations for food-oriented cross-modal retrieval. This proposed method integrates the textual embeddings of different recipe components into a compact embedding to represent the recipes with the latent component-specific weight factors. A transformer encoder is utilised to capture the intra-modality interactions and the importance of different extracted image features for enhanced image representations. Finally, the bi-directional triplet loss is further used to perform retrieval learning. Experimental results on the Recipe 1M dataset show that our LCWF-GI method achieves competent improvements

Study on tetracycline degradation in wastewater based on zero-valent nano iron assisted micro-nano bubbles

The presence of antibiotics in wastewater has become a significant concern due to their potential environmental impact and contribution to antibiotic resistance. In this study, we investigated the degradation of tetracycline, a commonly used antibiotic, in wastewater using a system based on zero-valent nano iron assisted micro-nano bubbles (MB/nZVI). The synthesized nZVI-BC composite, consisting of nano zero-valent iron particles loaded onto phosphoric acid-activated biochar, served as an efficient adsorbent for tetracycline removal. Our findings revealed that the combination of MBs and nZVI significantly enhanced the degradation efficiency of tetracycline. The MB/nZVI system exhibited the highest removal rate of 82.81% after a 2 h reaction, surpassing the performance of MB alone, nZVI-BC alone, and conventional bubble (CB)/nZVI-BC systems. Furthermore, the MB/nZVI system showed superior degradation performance at a dosage of 25 g/L and an MB flow rate of 30 mL/min. The pH condition had no significant effect on tetracycline degradation in the MB/nZVI system. Our results demonstrate that the use of MB/nZVI has the potential to be a sustainable and efficient approach for the remediation of tetracycline-contaminated wastewater

QoS‐aware web service recommendation via exploring the users' personalized diversity preferences

Abstract With the popularity and wide adoption of SOA (service‐oriented architecture), a massive amount of Web services emerge on the Internet. It is difficult for users to find the desired services from a large number of services. Thus, service recommendation becomes an effective means to improve the efficiency of using service. Considering that the users' QoS (quality of service) preferences are often unknown or uncertain, the recent QoS‐aware service recommendation methods recommend QoS‐diversified services for users to increase the probability of fulfillment of the service list with a limited number of services on users' potential QoS preferences. However, the existing QoS‐diversified service recommendation methods recommend services with a uniform diversity degree for different users, while the diversified preference requirements are not considered. To this end, this article proposes a service diversity adjustment algorithm, which selects more diversified services outside of the original service recommendation list to replace the services in the present recommendation list to approximate the QoS diversity preference of the active user. In this way, the probability of meeting the user's potential QoS preference requirements is improved. Comprehensive experimental results show that the proposed approach can not only provide personalized and diversified services but also ensure the overall accuracy of the recommendation results

What is the advance of extent of resection in glioblastoma surgical treatment—a systematic review

Abstract Glioblastoma multiform (GBM) is the most common malignant brain tumor characterized by poor prognosis, increased invasiveness, and high relapse rates. The relative survival estimates are quite low in spite of the standard treatment for GBM in recent years. Now, it has been gradually accepted that the amount of tumor mass removed correlates with longer survival rates. Although new technique advances allowing intraoperative analysis of tumor and normal brain tissue and functional paradigms based on stimulation techniques to map eloquent areas have been used for GBM resection, visual identification of tumor margins still remains a challenge for neurosurgeons. This article attempts to review and summarize the evolution of surgical resection for glioblastomas

Comparative Transcriptome Analysis Reveals Substantial Tissue Specificity in Human Aortic Valve

RNA sequencing (RNA-seq) has revolutionary roles in transcriptome identification and quantification of different types of tissues and cells in many organisms. Although numerous RNA-seq data derived from many types of human tissues and cell lines, little is known on the transcriptome repertoire of human aortic valve. In this study, we sequenced the total RNA prepared from two calcified human aortic valves and reported the whole transcriptome of human aortic valve. Integrating RNA-seq data of 13 human tissues from Human Body Map 2 Project, we constructed a transcriptome repertoire of human tissues, including 19,505 protein-coding genes and 4,948 long intergenic noncoding RNAs (lincRNAs). Among them, 263 lincRNAs were identified as novel noncoding transcripts in our data. By comparing transcriptome data among different human tissues, we observed substantial tissue specificity of RNA transcripts, both protein-coding genes and lincRNAs, in human aortic valve. Further analysis revealed that aortic valve-specific lincRNAs were more likely to be recently derived from repetitive elements in the primate lineage, but were less likely to be conserved at the nucleotide level. Expression profiling analysis showed significant lower expression levels of aortic valve-specific protein-coding genes and lincRNA genes, when compared with genes that were universally expressed in various tissues. Isoform-level expression analysis also showed that a majority of mRNA genes had a major isoform expressed in the human aortic valve. To our knowledge, this is the first comparative transcriptome analysis between human aortic valve and other human tissues. Our results are helpful to understand the transcriptome diversity of human tissues and the underlying mechanisms that drive tissue specificity of protein-coding genes and lincRNAs in human aortic valve