Miracle-dz/UniFAR-FTUs huggingface dataset:
Ecological Theory and Community Restoration Ecology

Community ecological theory may play an important role in the development of a science of restoration ecology. Not only will the practice of restoration benefit from an increased focus on theory, but basic research in community ecology will also benefit. We pose several major thematic questions that are relevant to restoration from the perspective of community ecological theory and, for each, identify specific areas that are in critical need of further research to advance the science of restoration ecology. We ask, what are appropriate restoration endpoints from a community ecology perspective? The problem of measuring restoration at the community level, particularly given the high amount of variability inherent in most natural communities, is not easy, and may require a focus on restoration of community function (e.g., trophic structure) rather than a focus on the restoration of particular species. We ask, what are the benefits and limitations of using species composition or biodiversity measures as endpoints in restoration ecology? Since reestablishing all native species may rarely be possible, research is needed on the relationship between species richness and community stability of restored sites and on functional redundancy among species in regional colonist “pools. ” Efforts targeted at restoring system function must take into account the role of individual species, particularly if some species play a disproportionate role in processing material or are strong interactors. We ask, is restoration of habitat a sufficient approach to reestablish species and function? Many untested assumptions concerning the relationship between physical habitat structure and restoration ecology are being made in practical restoration efforts. We need rigorous testing of these assumptions, particularly to determine how generally they apply to different taxa and habitats. We ask, to what extent can empirical and theoretical work on community succession and dispersal contribute to restoration ecology? We distinguish systems in which succession theory may be broadly applicable from those in which it is probably not. If community development is highly predictable, it may be feasible to manipulate natural succession processes to accelerate restoration. We close by stressing that the science of restoration ecology is so intertwined with basic ecological theory that practical restoration efforts should rely heavily on what is known from theoretical and empirical research on how communities develop and are structured over time.
A Scheme for Face Recognition in Complex Environments

In this paper, the authors propose a scheme for human face recognition in complex environments. The proposed scheme consists of three phases: moving object removal, face detection and face recognition. It could be applied to certain specific environments such as computer users in office, shopping mall, and reception or pokie machine gamblers in casinos. In these environments, the target human face for recognizing will be considered as the foreground and the moving objects such as cars, walking persons etc as the background. The objective of this paper is to implement a scheme for human face recognition so as to improve recognition precision and reduce false alarms. The scheme can be applied to prevent computer users or gamblers from sitting too long in front of the screens in offices or pokie machines in casinos. To the best of the authors' knowledge, this is the first time face recognition in complex environments has been taken into consideration.
Metal Nanoparticles/Porous Silicon Microcavity Enhanced Surface Plasmon Resonance Fluorescence for the Detection of DNA

A porous silicon microcavity (PSiMC) with resonant peak wavelength of 635 nm was fabricated by electrochemical etching. Metal nanoparticles (NPs)/PSiMC enhanced fluorescence substrates were prepared by the electrostatic adherence of Au NPs that were distributed in PSiMC. The Au NPs/PSiMC device was used to characterize the target DNA immobilization and hybridization with its complementary DNA sequences marked with Rhodamine red (RRA). Fluorescence enhancement was observed on the Au NPs/PSiMC device substrate; and the minimum detection concentration of DNA ran up to 10 pM. The surface plasmon resonance (SPR) of the MC substrate; which is so well-positioned to improve fluorescence enhancement rather the fluorescence enhancement of the high reflection band of the Bragg reflector; would welcome such a highly sensitive in biosensor.
P&D Graph Cube: Model and Parallel Materialization for Multidimensional Heterogeneous Network

We consider extending decision support facilities toward large-scale sophisticated networks, upon which multidimensional attributes are associated variety of entities, forming the so-called Multidimensional heterogeneous network. Multidimensional heterogeneous network has become important tool for modelling information networks, meanwhile, OLAP (Online Analytical Processing) has proven to beeffective tool on relational data, however, it is computationally an enormous challenge to manage and analyse Multidimensional heterogeneous network to support effective decision making and OLAP operations. In this paper, we enrich the semantics of the traditional OLAP and propose a P&D (Path and Dimension) Graph Cube Model framework, which can support multi-type queries and graph OLAP operations. Furthermore, on the basis of P&D Graph Cube Model, we divide the graph cube materialization into two parts described as Path related Materialization and Dimensionrelated Materialization. On Path Materialization, by taking account of structure summarization, we design the Pathrelated Materialization Algorithm based on the definition of relation path set, thus resulting in a more insightful and structure-enriched network. On Dimension Materialization, we provide a Graph Frag-shell Algorithm to compute graph shell fragments for fast high-dimension Graph OLAP. Finally, we implement the related algorithms on Spark. The results of experiments on real data set confirm the effectiveness and scalable of P&D Graph Cube Model with materialization algorithms.
Regularizing face verification nets for pain intensity regression

Limited labeled data are available for the research of estimating facial expression intensities. For instance, the ability to train deep networks for automated pain assessment is limited by small datasets with labels of patient-reported pain intensities. Fortunately, fine-tuning from a data-extensive pre-trained domain, such as face verification, can alleviate this problem. In this paper, we propose a network that fine-tunes a state-of-the-art face verification network using a regularized regression loss and additional data with expression labels. In this way, the expression intensity regression task can benefit from the rich feature representations trained on a huge amount of data for face verification. The proposed regularized deep regressor is applied to estimate the pain expression intensity and verified on the widely-used UNBC-McMaster Shoulder-Pain dataset, achieving the state-of-the-art performance. A weighted evaluation metric is also proposed to address the imbalance issue of different pain intensities.
Co‐ordinated programme of gene expression during asexual intraerythrocytic development of the human malaria parasite Plasmodium falciparum revealed by microarray analysis

Plasmodium falciparum is a protozoan parasite responsible for the most severe forms of human malaria. All the clinical symptoms and pathological changes seen during human infection are caused by the asexual blood stages of Plasmodium. Within host red blood cells, the parasite undergoes enormous developmental changes during its maturation. In order to analyse the expression of genes during intraerythrocytic development, DNA microarrays were constructed and probed with stage‐specific cDNA. Developmental upregulation of specific mRNAs was found to cluster into functional groups and revealed a co‐ordinated programme of gene expression. Those involved in protein synthesis (ribosomal proteins, translation factors) peaked early in development, followed by those involved in metabolism, most dramatically glycolysis genes. Adhesion/invasion genes were turned on later in the maturation process. At the end of intraerythrocytic development (late schizogony), there was a general shut‐off of gene expression, although a small set of genes, including a number of protein kinases, were turned on at this stage. Nearly all genes showed some regulation over the course of development. A handful of genes remained constant and should be useful for normalizing mRNA levels between stages. These data will facilitate functional analysis of the P. falciparum genome and will help to identify genes with a critical role in parasite progression and multiplication in the human host.
Preparation and properties of polyethylene/montmorillonite nanocomposites by in situ polymerization

Polyethylene (PE)/montmorillonite (MMT) nanocomposites were prepared by in situ coordination polymerization using a MMT/MgCl2/TiCl4 catalyst activated by AI(Et),. The catalyst was prepared by first diffusing MgCl2 into the swollen MMT layers, followed by loading TiCl4 on the inner/outer layer surfaces of MMT where MgCl2 was already deposited. The intercalation of MMT layers by MgCl2 and TiCl, was demonstrated by the enlarged interlayer spacing determined by WAXD. The nanoscale dispersion of MMT layers in the polyethylene matrix was characterized by WAXD and TEM. As a consequence, the crystallinity of the nanocomposite decreased sharply, whereas the tensile strength was significantly improved compared to that of virgin polyethylene of comparable molecular weight. The confinement of the nanodispersed MMT layers to molecular chain and the strong interaction between the nanoscale MMT layers and the resin matrix were thought to account for the decrease of crystallinity and the remarkable enhancement of strength.
Photocrosslinked electrospun chitosan-based biocompatible nanofibers

The electrospun fibers chitosan/polyvinyl alcohol (CS/PVA) were crosslinked by the incorporation of photocrosslinking agent poly(ethyleneglycol)-600-dimethacrylate (PEGDMA) and photoinitiator 2-hydroxy-1-[4-(2-hydroxyethoxy)phenyl]-2-methyl-1-propanone (HEPK) into the spinning solutions and a subsequent irradiation by UV light. The photocrosslinking process of CS-based nanofibers was studied by the Fourier transform infrared spectra, and the water resistance was investigated by scanning electron microscopy (SEM) and water swelling measurement. The results indicated that the water resistance of photocrosslinked CS-based nanofibers was improved obviously and the swelling ratio decreased with the increasing content of PEGDMA/HEPK. When the concentration of crosslinking agent was 20 wt %, the swelling ratio value decreased to half of that in none crosslinking CS/PVA nanofiber membranes, further increasing the concentration of crosslinking agent to 50 wt %, the swelling ratio value was one eighth of the none crosslinking CS/PVA nanofiber membranes. Cytotoxicity evaluation by 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide assay indicated that the photocrosslinked CS-based fiber membranes were nontoxic to L929 cells. Results from cell culture SEM imaging showed that cells which exhibited the spindle shape could grow properly on the surface of nanofibrous structure of the CS/PVA. © 2008 Wiley Periodicals, Inc. J Appl Polym Sci, 2008
Distributed Multisensor Data Fusion under Unknown Correlation and Data Inconsistency

The paradigm of multisensor data fusion has been evolved from a centralized architecture to a decentralized or distributed architecture along with the advancement in sensor and communication technologies. These days, distributed state estimation and data fusion has been widely explored in diverse fields of engineering and control due to its superior performance over the centralized one in terms of flexibility, robustness to failure and cost effectiveness in infrastructure and communication. However, distributed multisensor data fusion is not without technical challenges to overcome: namely, dealing with cross-correlation and inconsistency among state estimates and sensor data. In this paper, we review the key theories and methodologies of distributed multisensor data fusion available to date with a specific focus on handling unknown correlation and data inconsistency. We aim at providing readers with a unifying view out of individual theories and methodologies by presenting a formal analysis of their implications. Finally, several directions of future research are highlighted.
Communications, caching, and computing oriented small cell networks with interference alignment

In future generation mobile systems, hyperdense small cell networks will be deployed to meet the tremendous growth of mobile traffic, in which interference is a key limitation. Interference alignment, IA, is a potential technique for interference management of small cell networks. However, there are still some practical challenges that hinder the development of IA-based small cell networks. On the other hand, to better cope with the shift from the sender-driven end-to-end communication paradigm to the receiver-driven content retrieval paradigm, content-centric communication has attracted great attention. Together with recent advances in computing, a communications, caching, and computing - 3C - oriented design paradigm for mobile networks is emerging. In this article, we propose a framework of 3C-oriented small cell networks with IA, in which caching and computing are exploited to simplify the network topology, improve the throughput, reduce the backhaul load, and guarantee the quality of experience of users. Furthermore, some research challenges are discussed for 3C-oriented small cell networks with IA.
Achievement Goals in CS1: Replication and Extension

Replication research is rare in CS education. For this reason, it is often unclear to what extent our findings generalize beyond the context of their generation. The present paper is a replication and extension of Achievement Goal Theory research on CS1 students. Achievement goals are cognitive representations of desired competence (e.g., topic mastery, outperforming peers) in achievement settings, and can predict outcomes such as grades and interest. We study achievement goals and their effects on CS1 students at six institutions in four countries. Broad patterns are maintained --- mastery goals are beneficial while appearance goals are not --- but our data additionally admits fine-grained analyses that nuance these findings. In particular, students' motivations for goal pursuit can clarify relationships between performance goals and outcomes.
18F-FDG/PET May Help to Identify a Subgroup of Patients with T1-T2 Breast Cancer and 1-3 Positive Lymph Nodes Who Are at a High Risk of Recurrence after Mastectomy

Purpose The purpose of this study is to assess the utility of positron emission tomography (PET) for predicting recurrence among patients with T1-T2/N1 breast cancer who were treated with mastectomy. Materials and Methods Of 712 consecutive patients with T1-T2/N1 breast cancer treated during 2003-2012, 109 had undergone preoperative 18F-fluorodeoxyglucose/PET and were included. Metabolic (maximum standardized uptake value [SUVmax]), volumetric (metabolic tumor volume [MTV]), and combined (total lesion glycolysis [TLG]) indices were measured. The resulting values were analyzed and compared with clinical outcome. Results At the median follow-up of 46.7 months, the 3-year relapse-free survival (RFS) rate was 95.2%. SUVmax (area under curve, 0.824) was more useful than MTV or TLG as a means of identifying patients at high risk for any recurrence. In multivariate analysis, SUVmax remained an independent risk factor for RFS (p=0.006). Using the method of Contal and O’Quigley, a SUVmax threshold of 5.36 showed the best predictive performance. The PET-based high-risk group (≥ 5.36 in either breast or nodes) had more T1c-T2, high-grade, hormone-receptor negative, and invasive ductal carcinoma tumors than the low-risk group (< 5.36 in both breast and nodes). The prognosis was much worse when high SUVmax (≥ 5.36) was detected in nodes (p < 0.001). In the no-radiotherapy cohort, the PET-based high-risk group had increased risk of locoregional recurrence when compared to the low-risk group (p=0.037). Conclusion High SUVmax on preoperative PET showed association with elevated risk of locoregional recurrence and any recurrence. Pre-treatment PET may improve assessments of recurrence risk and clarify indications for post-mastectomy radiotherapy in this subset of patients.
The experimental parameters that affect attentional modulation of the ERP C1 component

ABSTRACT There is current debate as to whether spatial attention can modulate V1 activity during the initial wave of visual processing. Research on this topic has focused on the event-related potential (ERP) C1 component, which primarily reflects activity in V1. The purpose of the present selective review was to compare experimental parameters across spatial attention studies to determine whether certain stimulus, task, or analysis conditions were more likely to produce significant C1 attention effects. Specifically, C1 attention effects were evaluated as a function of visual field location, presence or absence of distractors, load (perceptual or attentional), cue type (endogenous or exogenous), and electrode location. As the C1 component has its peak magnitude at midline parietal-occipital electrodes, only studies that measured C1 attention effects at these electrode locations were considered. Furthermore, only studies that manipulated spatial attention, and no other factors, were considered. The current analysis indicated that to maximize sensitivity to C1 attention effects, stimuli should be in the upper visual field, there should be distractors, conditions should be high perceptual or attentional load, there should be exogenous cuing, and effects should be measured at midline parietal-occipital electrodes POz, Pz, and CPz.
Scratching the surface of digital literacy… but we need to go deeper

The popularization of digital educational devices with low barriers to entry has encouraged the development of many training activities oriented towards the incorporation of technology in schools. In some cases, the introduction of technology has led to the development of new educational practices that support the development of computational thinking. However, the supposed benefits of these approaches have not been properly assessed. Moreover, having taught over 30 workshops with the Scratch programming tool to teachers at different educational levels (primary, secondary, university), we found no evidence of subsequent methodological changes in schools. This study tries to understand the dissonance between the alleged success of initiatives around digital literacy and the lack of continuity in the use of user-friendly programming environments like Scratch. For this purpose, we analyzed the evolution of the grades of Scratch programming achieved by the students of the School of Education of Vitoria-Gasteiz and their engagement with Scratch. We also interviewed teachers from different schools who have participated in Scratch workshops with us. After this study, we identified some of the circumstances that facilitate and hinder the development of computational thinking. Since we consider Scratch as a resource that allows the development of new methodological approaches in the classroom as well as the acquisition of skills related to computational thinking, we propose a framework that will help to overcome the current status.
A Method on Dynamic Path Planning for Robotic Manipulator Autonomous Obstacle Avoidance Based on an Improved RRT Algorithm

In a future intelligent factory, a robotic manipulator must work efficiently and safely in a Human–Robot collaborative and dynamic unstructured environment. Autonomous path planning is the most important issue which must be resolved first in the process of improving robotic manipulator intelligence. Among the path-planning methods, the Rapidly Exploring Random Tree (RRT) algorithm based on random sampling has been widely applied in dynamic path planning for a high-dimensional robotic manipulator, especially in a complex environment because of its probability completeness, perfect expansion, and fast exploring speed over other planning methods. However, the existing RRT algorithm has a limitation in path planning for a robotic manipulator in a dynamic unstructured environment. Therefore, an autonomous obstacle avoidance dynamic path-planning method for a robotic manipulator based on an improved RRT algorithm, called Smoothly RRT (S-RRT), is proposed. This method that targets a directional node extends and can increase the sampling speed and efficiency of RRT dramatically. A path optimization strategy based on the maximum curvature constraint is presented to generate a smooth and curved continuous executable path for a robotic manipulator. Finally, the correctness, effectiveness, and practicability of the proposed method are demonstrated and validated via a MATLAB static simulation and a Robot Operating System (ROS) dynamic simulation environment as well as a real autonomous obstacle avoidance experiment in a dynamic unstructured environment for a robotic manipulator. The proposed method not only provides great practical engineering significance for a robotic manipulator’s obstacle avoidance in an intelligent factory, but also theoretical reference value for other type of robots’ path planning.
Template‐based protein structure prediction in CASP11 and retrospect of I‐TASSER in the last decade

We report the structure prediction results of a new composite pipeline for template‐based modeling (TBM) in the 11th CASP experiment. Starting from multiple structure templates identified by LOMETS based meta‐threading programs, the QUARK ab initio folding program is extended to generate initial full‐length models under strong constraints from template alignments. The final atomic models are then constructed by I‐TASSER based fragment reassembly simulations, followed by the fragment‐guided molecular dynamic simulation and the MQAP‐based model selection. It was found that the inclusion of QUARK‐TBM simulations as an intermediate modeling step could help improve the quality of the I‐TASSER models for both Easy and Hard TBM targets. Overall, the average TM‐score of the first I‐TASSER model is 12% higher than that of the best LOMETS templates, with the RMSD in the same threading‐aligned regions reduced from 5.8 to 4.7 Å. Nevertheless, there are nearly 18% of TBM domains with the templates deteriorated by the structure assembly pipeline, which may be attributed to the errors of secondary structure and domain orientation predictions that propagate through and degrade the procedures of template identification and final model selections. To examine the record of progress, we made a retrospective report of the I‐TASSER pipeline in the last five CASP experiments (CASP7‐11). The data show no clear progress of the LOMETS threading programs over PSI‐BLAST; but obvious progress on structural improvement relative to threading templates was witnessed in recent CASP experiments, which is probably attributed to the integration of the extended ab initio folding simulation with the threading assembly pipeline and the introduction of atomic‐level structure refinements following the reduced modeling simulations. Proteins 2016; 84(Suppl 1):233–246. © 2015 Wiley Periodicals, Inc.