Meta Clustering for Collaborative Learning

An emerging number of learning scenarios involve a set of learners/analysts each equipped with a unique dataset and algorithm, who may collaborate with each other to enhance their learning performance. From the perspective of a particular learner, a careless collaboration with task-irrelevant other learners is likely to incur modeling error. A crucial problem is to search for the most appropriate collaborators so that their data and modeling resources can be effectively leveraged. Motivated by this, we propose to study the problem of `meta clustering', where the goal is to identify subsets of relevant learners whose collaboration will improve the performance of each individual learner. In particular, we study the scenario where each learner is performing a supervised regression, and the meta clustering aims to categorize the underlying supervised relations (between responses and predictors) instead of the raw data. We propose a general method named as Select-Exchange-Cluster (SEC) for performing such a clustering. Our method is computationally efficient as it does not require each learner to exchange their raw data. We prove that the SEC method can accurately cluster the learners into appropriate collaboration sets according to their underlying regression functions. Synthetic and real data examples show the desired performance and wide applicability of SEC to a variety of learning tasks

A sensitive electrochemical sensor based on polypyrrole/electrochemically reduced graphene oxide for the determination of imidacloprid

The glassy carbon electrode (GCE) was modified by electrochemically reduced graphene oxide (ERGO) and polypyrrole (PPy) prepared by simple cyclic voltammetry (CV) electropoly­merization. The PPy/ERGO modified electrode (PPy/ERGO/GCE) was used as a platform of electrochemical sensor to detect imidacloprid (IMI) insecticide. CV and differential pulse voltammetry (DPV) were chosen as the methods to investigate of the electrochemical behavior of IMI on PPy/ERGO/GCE surface. Scanning electron microscopy (SEM) and Raman spectra were utilized to describe the morphology and structure of the modified electrode. Experimental parameters were optimized, such as the number of polymerization cycles, scan rate and the pH value of electrolyte. Under the optimized conditions, when the concentration of IMI was in the range of 1-10 μM and 10-60 μM, the increase of reduction peak current was linear with the concentration of IMI, and the low detection limit was found to be 0.18 μM (S/N = 3). Results showed that PPy/ERGO/GCE demonstrated satisfactory reproducibility and stability, and has great potential in actual sample testing

Revealing the impact of built environment, air pollution and housing price on health inequality: an empirical analysis of Nanjing, China

IntroductionResidential segregation have become a common phenomenon in China recently. Socioeconomically disadvantaged residents were more likely to live in communities with higher PM2.5 concentrations and poorer built environment, which may ultimately lead to a higher health risk, further exacerbating health inequalities. However, the reasons for health inequalities under residential segregation remain unclear.MethodsThis study quantified the built environment, air pollution, mortality rate and housing price at 1 km × 1 km grid scale. Moderating effect model, mediating effect model, moderated mediating effect model were used to progressively clarify the relationship between the four.ResultsResults show that, in terms of spatial distribution, the central area has high housing price with good built environment, low PM2.5 concentration and low mortality rate. While the suburban area has low housing price, poor built environment, high PM2.5 concentration and high mortality rate. Additionally, built environment can not only reduce health risks through moderating effect, but also affect health through the mediating effect of PM2.5. There is heterogeneity in moderating effect of built environment in different locations. Housing prices can moderate the effect of built environment on health. This study would offer important reference for urban planning to mitigate the effect of built environment inequalities on health inequalities in China

Blood transcriptome reveals immune and metabolic-related genes involved in growth of pasteurized colostrum-fed calves

The quality of colostrum is a key factor contributing to healthy calf growth, and pasteurization of colostrum can effectively reduce the counts of pathogenic microorganisms present in the colostrum. Physiological changes in calves fed with pasteurized colostrum have been well characterized, but little is known about the underlying molecular mechanisms. In this study, key genes and functional pathways through which pasteurized colostrum affects calf growth were identified through whole blood RNA sequencing. Our results showed that calves in the pasteurized group (n = 16) had higher body height and daily weight gain than those in the unpasteurized group (n = 16) in all months tested. Importantly, significant differences in body height were observed at 3 and 4 months of age (p < 0.05), and in daily weight gain at 2, 3, and 6 months of age (p < 0.05) between the two groups. Based on whole blood transcriptome data from 6-months old calves, 630 differentially expressed genes (DEGs), of which 235 were upregulated and 395 downregulated, were identified in the pasteurized compared to the unpasteurized colostrum groups. Most of the DEGs have functions in the immune response (e.g., CCL3, CXCL3, and IL1A) and metabolism (e.g., PTX3 and EXTL1). Protein-protein interaction analyses of DEGs revealed three key subnetworks and fifteen core genes, including UBA52 and RPS28, that have roles in protein synthesis, oxidative phosphorylation, and inflammatory responses. Twelve co-expression modules were identified through weighted gene co-expression network analysis. Among them, 17 genes in the two modules that significantly associated with pasteurization were mainly involved in the tricarboxylic acid cycle, NF-kappa B signaling, and NOD-like receptor signaling pathways. Finally, DEGs that underwent alternative splicing in calves fed pasteurized colostrum have roles in the immune response (SLCO4A1, AKR1C4, and MED13L), indicative of potential roles in immune regulation. Results from multiple analytical methods used suggest that differences in calf growth between the pasteurized and unpasteurized groups may be due to differential immune activity. Our data provide new insights into the impact of pasteurization on calf immune and metabolic-related pathways through its effects on gene expression

Large expert-curated database for benchmarking document similarity detection in biomedical literature search

Document recommendation systems for locating relevant literature have mostly relied on methods developed a decade ago. This is largely due to the lack of a large offline gold-standard benchmark of relevant documents that cover a variety of research fields such that newly developed literature search techniques can be compared, improved and translated into practice. To overcome this bottleneck, we have established the RElevant LIterature SearcH consortium consisting of more than 1500 scientists from 84 countries, who have collectively annotated the relevance of over 180 000 PubMed-listed articles with regard to their respective seed (input) article/s. The majority of annotations were contributed by highly experienced, original authors of the seed articles. The collected data cover 76% of all unique PubMed Medical Subject Headings descriptors. No systematic biases were observed across different experience levels, research fields or time spent on annotations. More importantly, annotations of the same document pairs contributed by different scientists were highly concordant. We further show that the three representative baseline methods used to generate recommended articles for evaluation (Okapi Best Matching 25, Term Frequency-Inverse Document Frequency and PubMed Related Articles) had similar overall performances. Additionally, we found that these methods each tend to produce distinct collections of recommended articles, suggesting that a hybrid method may be required to completely capture all relevant articles. The established database server located at https://relishdb.ict.griffith.edu.au is freely available for the downloading of annotation data and the blind testing of new methods. We expect that this benchmark will be useful for stimulating the development of new powerful techniques for title and title/abstract-based search engines for relevant articles in biomedical research.Peer reviewe