Using Coh-Metrix to Analyse Writing Skills of Students: A Case Study in a Technological Common Core Curriculum Course

Pedagogy with learning analytics is shown to facilitate the teaching-learning process through analysing student’s behaviours. In this paper, we explore the possibility of using a computational linguistic tool Coh-Metrix for analyzing and improving writing skills of students in a technological common core curriculum course. In this study, we mainly focused on the investigation of syntactic simplicity, word concreteness, referential cohesion, and deep cohesion of student’s essays. We studied 25 essays from the three-year curriculum students and 26 essays from the four-year curriculum students. Results illustrate the necessity of improving student’s writing skills in their university learning, so that they can effectively circulate their ideas to the public in the future.published_or_final_versio

Effect of anthropogenic sulphate aerosol in China on the drought in the western-to-central US

In recent decades, droughts have occurred in the western-to-central United States (US), significantly affecting food production, water supplies, ecosystem health, and the propagation of vector-borne diseases. Previous studies have suggested natural sea surface temperature (SST) forcing in the Pacific as the main driver of precipitation deficits in the US. Here, we show that the aerosol forcing in China, which has been known to alter the regional hydrological cycle in East Asia, may also contribute to reducing the precipitation in the western-to-central US through atmospheric teleconnections across the Pacific. Our model experiments show some indications that both the SST forcing and the increase in regional sulphate forcing in China play a similar role in modulating the western-to-central US precipitation, especially its long-term variation. This result indicates that regional air quality regulations in China have important implications for hydrological cycles in East Asia, as well as in the USopen1

Evolving an optimal decision template for combining classifiers.

In this paper, we aim to develop an effective combining algorithm for ensemble learning systems. The Decision Template method, one of the most popular combining algorithms for ensemble systems, does not perform well when working on certain datasets like those having imbalanced data. Moreover, point estimation by computing the average value on the outputs of base classifiers in the Decision Template method is sometimes not a good representation, especially for skewed datasets. Here we propose to search for an optimal decision template in the combining algorithm for a heterogeneous ensemble. To do this, we first generate the base classifier by training the pre-selected learning algorithms on the given training set. The meta-data of the training set is then generated via cross validation. Using the Artificial Bee Colony algorithm, we search for the optimal template that minimizes the empirical 0–1 loss function on the training set. The class label is assigned to the unlabeled sample based on the maximum of the similarity between the optimal decision template and the sample’s meta-data. Experiments conducted on the UCI datasets demonstrated the superiority of the proposed method over several benchmark algorithms

Confidence in prediction: an approach for dynamic weighted ensemble.

Combining classifiers in an ensemble is beneficial in achieving better prediction than using a single classifier. Furthermore, each classifier can be associated with a weight in the aggregation to boost the performance of the ensemble system. In this work, we propose a novel dynamic weighted ensemble method. Based on the observation that each classifier provides a different level of confidence in its prediction, we propose to encode the level of confidence of a classifier by associating with each classifier a credibility threshold, computed from the entire training set by minimizing the entropy loss function with the mini-batch gradient descent method. On each test sample, we measure the confidence of each classifier’s output and then compare it to the credibility threshold to determine whether a classifier should be attended in the aggregation. If the condition is satisfied, the confidence level and credibility threshold are used to compute the weight of contribution of the classifier in the aggregation. By this way, we are not only considering the presence but also the contribution of each classifier based on the confidence in its prediction on each test sample. The experiments conducted on a number of datasets show that the proposed method is better than some benchmark algorithms including a non-weighted ensemble method, two dynamic ensemble selection methods, and two Boosting methods

Multiple Imputation Ensembles (MIE) for dealing with missing data

Missing data is a significant issue in many real-world datasets, yet there are no robust methods for dealing with it appropriately. In this paper, we propose a robust approach to dealing with missing data in classification problems: Multiple Imputation Ensembles (MIE). Our method integrates two approaches: multiple imputation and ensemble methods and compares two types of ensembles: bagging and stacking. We also propose a robust experimental set-up using 20 benchmark datasets from the UCI machine learning repository. For each dataset, we introduce increasing amounts of data Missing Completely at Random. Firstly, we use a number of single/multiple imputation methods to recover the missing values and then ensemble a number of different classifiers built on the imputed data. We assess the quality of the imputation by using dissimilarity measures. We also evaluate the MIE performance by comparing classification accuracy on the complete and imputed data. Furthermore, we use the accuracy of simple imputation as a benchmark for comparison. We find that our proposed approach combining multiple imputation with ensemble techniques outperform others, particularly as missing data increases

Yeast axial-element protein, Red1, binds SUMO chains to promote meiotic interhomologue recombination and chromosome synapsis

The synaptonemal complex (SC) is a tripartite protein structure consisting of two parallel axial elements (AEs) and a central region. During meiosis, the SC connects paired homologous chromosomes, promoting interhomologue (IH) recombination. Here, we report that, like the CE component Zip1, Saccharomyces cerevisiae axial-element structural protein, Red1, can bind small ubiquitin-like modifier (SUMO) polymeric chains. The Red1–SUMO chain interaction is dispensable for the initiation of meiotic DNA recombination, but it is essential for Tel1- and Mec1-dependent Hop1 phosphorylation, which ensures IH recombination by preventing the inter-sister chromatid DNA repair pathway. Our results also indicate that Red1 and Zip1 may directly sandwich the SUMO chains to mediate SC assembly. We suggest that Red1 and SUMO chains function together to couple homologous recombination and Mec1–Tel1 kinase activation with chromosome synapsis during yeast meiosis

Characterization of NLRP12 during the Development of Allergic Airway Disease in Mice

Among the 22 members of the nucleotide binding-domain, leucine rich repeat-containing (NLR) family, less than half have been functionally characterized. Of those that have been well studied, most form caspase-1 activating inflammasomes. NLRP12 is a unique NLR that has been shown to attenuate inflammatory pathways in biochemical assays and mediate the lymph node homing of activated skin dendritic cells in contact hypersensitivity responses. Since the mechanism between these two important observations remains elusive, we further evaluated the contribution of NLRP12 to organ specific adaptive immune responses by focusing on the lung, which, like skin, is exposed to both exogenous and endogenous inflammatory agents. In models of allergic airway inflammation induced by either acute ovalbumin (OVA) exposure or chronic house dust mite (HDM) antigen exposure, Nlrp12−/− mice displayed subtle differences in eosinophil and monocyte infiltration into the airways. However, the overall development of allergic airway disease and airway function was not significantly altered by NLRP12 deficiency. Together, the combined data suggest that NLRP12 does not play a vital role in regulating Th2 driven airway inflammation using common model systems that are physiologically relevant to human disease. Thus, the allergic airway inflammation models described here should be appropriate for subsequent studies that seek to decipher the contribution of NLRP12 in mediating the host response to agents associated with asthma exacerbation

Effective Interventions and Decline of Antituberculosis Drug Resistance in Eastern Taiwan, 2004–2008

BACKGROUND: The Taiwan health authority recently launched several tuberculosis (TB) control interventions, which may have an impact on the epidemic of drug-resistant TB. We conducted a population-based antituberculosis drug resistance surveillance program in Eastern Taiwan to measure the proportions of notified TB patients with anti-TB drug resistance and the trend from 2004 to 2008. METHODS AND FINDINGS: All culture-positive TB patients were enrolled. Drug susceptibility testing results of the first isolate of each TB patient in each treatment course were analyzed. In total, 2688 patients were included, of which 2176 (81.0%) were new TB cases and 512 (19.0%) were previously treated cases. Among the 2176 new TB cases, 97 (4.5%) were retreated after the first episode of TB treatment within the study period. The proportion of new patients with any resistance, isoniazid resistance but not multidrug-resistant TB (resistant to at least isoniazid and rifampin, MDR-TB), and MDR-TB was 16.4%, 7.5%, and 4.0%, respectively, and that among previously treated cases was 30.9%, 7.9%, and 17.6%, respectively. The combined proportion of any resistance decreased from 23.3% in 2004 to 14.3% in 2008, and that of MDR-TB from 11.5% to 2.4%. CONCLUSIONS: The proportion of TB patients with drug-resistant TB in Eastern Taiwan remains substantial. However, an effective TB control program has successfully driven the proportion of drug resistance among TB patients downward

Transcriptional control of the multi-drug transporter ABCB1 by transcription factor Sp3 in different human tissues

The ATP-binding cassette (ABC) transporter ABCB1, encoded by the multidrug resistance gene MDR1, is expressed on brain microvascular endothelium and several types of epithelium, but not on endothelia outside the CNS. It is an essential component of the blood-brain barrier. The aim of this study was to identify cell-specific controls on the transcription of MDR1 in human brain endothelium. Reporter assays identified a region of 500bp around the transcription start site that was optimally active in brain endothelium. Chromatin immunoprecipitation identified Sp3 and TFIID associated with this region and EMSA (electrophoretic mobility shift assays) confirmed that Sp3 binds preferentially to an Sp-target site (GC-box) on the MDR1 promoter in brain endothelium. This result contrasts with findings in other cell types and with the colon carcinoma line Caco-2, in which Sp1 preferentially associates with the MDR1 promoter. Differences in MDR1 transcriptional control between brain endothelium and Caco-2 could not be explained by the relative abundance of Sp1:Sp3 nor by the ratio of Sp3 variants, because activating variants of Sp3 were present in both cell types. However differential binding of other transcription factors was also detected in two additional upstream regions of the MDR1 promoter. Identification of cell-specific controls on the transcription of MDR1 indicates that it may be possible to modulate multi-drug resistance on tumours, while leaving the blood brain barrier intact

A Forward Chemical Screen in Zebrafish Identifies a Retinoic Acid Derivative with Receptor Specificity

Background: Retinoids regulate key developmental pathways throughout life, and have potential uses for differentiation therapy. It should be possible to identify novel retinoids by coupling new chemical reactions with screens using the zebrafish embryonic model. Principal Findings: We synthesized novel retinoid analogues and derivatives by amide coupling, obtaining 80–92% yields. A small library of these compounds was screened for bioactivity in living zebrafish embryos. We found that several structurally related compounds significantly affect development. Distinct phenotypes are generated depending on time of exposure, and we characterize one compound (BT10) that produces specific cardiovascular defects when added 1 day post fertilization. When compared to retinoic acid (ATRA), BT10 shows similar but not identical changes in the expression pattern of embryonic genes that are known targets of the retinoid pathway. Reporter assays determined that BT10 interacts with all three RAR receptor sub-types, but has no activity for RXR receptors, at all concentrations tested. Conclusions: Our screen has identified a novel retinoid with specificity for retinoid receptors. This lead compound may be useful for manipulating components of retinoid signaling networks, and may be further derivatized for enhanced activity