Examining online discourse using the knowledge connection analyzer framework and collaborative tools in knowledge building

This study examines the problem of the fragmentation of asynchronous online discourse by using the Knowledge Connection Analyzer (KCA) framework and tools and explores how students could use the KCA data in classroom reflections to deepen their knowledge building (KB) inquiry. We applied the KCA to nine Knowledge Forum® (KF) databases to examine the framework, identify issues with online discourse that may inform further development, and provide data on how the tools work. Our comparisons of the KCA data showed that the databases with more sophisticated teacher–researcher co-design had higher KCA indices than those with regular KF use, validating the framework. Analysis of KF discourse using the KCA helped identify several issues including limited collaboration among peers, underdeveloped practices of synthesizing and rising above of collective ideas, less analysis of conceptual development of discussion threads, and limited collaborative reflection on individual contribution and promising inquiry direction. These issues that open opportunities for further development cannot be identified by other present analytics tools. The exploratory use of the KCA in real classroom revealed that the KCA can support students’ productive reflective assessment and KB. This study discusses the implications for examining and scaffolding online discussions using the KCA assessment framework, with a focus on collective perspectives regarding community knowledge, synthesis, idea improvement, and contribution to community understanding

Causal Discovery in Linear Structural Causal Models with Deterministic Relations

Linear structural causal models (SCMs) -- in which each observed variable is generated by a subset of the other observed variables as well as a subset of the exogenous sources -- are pervasive in causal inference and casual discovery. However, for the task of causal discovery, existing work almost exclusively focus on the submodel where each observed variable is associated with a distinct source with non-zero variance. This results in the restriction that no observed variable can deterministically depend on other observed variables or latent confounders. In this paper, we extend the results on structure learning by focusing on a subclass of linear SCMs which do not have this property, i.e., models in which observed variables can be causally affected by any subset of the sources, and are allowed to be a deterministic function of other observed variables or latent confounders. This allows for a more realistic modeling of influence or information propagation in systems. We focus on the task of causal discovery form observational data generated from a member of this subclass. We derive a set of necessary and sufficient conditions for unique identifiability of the causal structure. To the best of our knowledge, this is the first work that gives identifiability results for causal discovery under both latent confounding and deterministic relationships. Further, we propose an algorithm for recovering the underlying causal structure when the aforementioned conditions are satisfied. We validate our theoretical results both on synthetic and real datasets.Comment: Accepted at 1st Conference on Causal Learning and Reasoning (CLeaR 2022

Causal Discovery in Linear Latent Variable Models Subject to Measurement Error

We focus on causal discovery in the presence of measurement error in linear systems where the mixing matrix, i.e., the matrix indicating the independent exogenous noise terms pertaining to the observed variables, is identified up to permutation and scaling of the columns. We demonstrate a somewhat surprising connection between this problem and causal discovery in the presence of unobserved parentless causes, in the sense that there is a mapping, given by the mixing matrix, between the underlying models to be inferred in these problems. Consequently, any identifiability result based on the mixing matrix for one model translates to an identifiability result for the other model. We characterize to what extent the causal models can be identified under a two-part faithfulness assumption. Under only the first part of the assumption (corresponding to the conventional definition of faithfulness), the structure can be learned up to the causal ordering among an ordered grouping of the variables but not all the edges across the groups can be identified. We further show that if both parts of the faithfulness assumption are imposed, the structure can be learned up to a more refined ordered grouping. As a result of this refinement, for the latent variable model with unobserved parentless causes, the structure can be identified. Based on our theoretical results, we propose causal structure learning methods for both models, and evaluate their performance on synthetic data.Comment: Accepted at 36th Conference on Neural Information Processing Systems (NeurIPS 2022

Saw Palmetto Extract Inhibits Metastasis and Antiangiogenesis through STAT3 Signal Pathway in Glioma Cell

Signal transducer and activator of transcription factor 3 (STAT3) plays an important role in the proliferation and angiogenesis in human glioma. Previous research indicated that saw palmetto extract markedly inhibited the proliferation of human glioma cells through STAT3 signal pathway. But its effect on tumor metastasis and antiangiogenesis is not clear. This study is to further clear the impact of saw palmetto extract on glioma cell metastasis, antiangiogenesis, and its mechanism. TUNEL assay indicated that the apoptotic cells in the saw palmetto treated group are higher than that in the control group (p<0.05). The apoptosis related protein is detected and the results revealed that saw palmetto extract inhibits the proliferation of human glioma. Meanwhile pSTAT3 is lower in the experimental group and CD34 is also inhibited in the saw palmetto treated group. This means that saw palmetto extract could inhibit the angiogenesis in glioma. We found that saw palmetto extract was an important phytotherapeutic drug against the human glioma through STAT3 signal pathway. Saw palmetto extract may be useful as an adjunctive therapeutic agent for treatment of individuals with glioma and other types of cancer in which STAT3 signaling is activated

Correlation between serum esterase polymorphism and production performance of Yuxi fat-tailed sheep

The polymorphism of serum esterase (Es) of Henan Yuxi fat-tailed sheep was detected through polyacrylamide gel electrophoresis (PAGE), and the correlation between serum esterase and productivity was analyzed. The research result indicated that there are two alleles on the Es loci of Henan Yuxi fat-tailed sheep: Es+ and Es-. The gene frequencies of Es+ and Es- were 0.55 and 0.45, respectively. Besides, the frequencies of three genotypes (Es++, Es+- and Es--) are 0.425, 0.250 and 0.325, respectively. The recommended height of Es++ genotype is significantly higher than that of Es+- genotype (P&lt;0.05), but the above two produce indistinctive difference in recommended height with Es-- genotype (P&gt;0.05). The chest circumference of Es++ genotype is significantly higher than that of Es-- (P&lt;0.05), but the above two produce indistinctive difference in chest circumference with Es+- genotype (P&gt;0.05). Es exerts no significant impact on other indexes (P&gt;0.05).Keywords: Henan Yuxi fat-tailed sheep, serum esterase (Es), polymorphismAfrican Journal of Biotechnology Vol. 12(9), pp. 986-98

The role of echocardiography in prognosis for dysfunction and abandonment of radiocephalic arteriovenous fistula in elderly Chinese patients on hemodialysis

The objective of this study was to examine the impact of cardiac structure and function at baseline on the outcomes associated with arteriovenous fistula (AVF) in patients on hemodialysis (HD). Patients who initiated HD aged ≥70 years and received a mature AVF creation were included retrospectively. Echocardiographic parameters measured within 1 week before AVF creation were acquired. The observational period for each patient was from the point of AVF creation to the last time of follow‐up unless AVF abandonment or death occurred. Kaplan‐Meier and Cox proportional hazard regression analyses were conducted. A total of 82 elderly Chinese HD patients with mature radiocephalic AVF (RCAVF) and EF ≥50% were analyzed. During the median study period of 26.8 (12‐40) months, 42 (51.2%) experienced RCAVF dysfunction and 34 (41.5%) progressed to abandonment. Primary and cumulative patencies at 6, 12, 24, and 36 months were 81%, 73%, 48%, 38%, and 84%, 81%, 68%, 55%, respectively. Left ventricle end‐diastolic volume (LVEDV) ≤103.5 mL (HR = 2.5, P = .019) and the right side of RCAVF (HR = 3.59, P = .003) significantly predicted RCAVF dysfunction. The main pulmonary artery internal diameter (MPAID) ≤21.5 mm (HR = 4.3, P = .001) as well as the right side (HR = 2.95, P = .047) were the independent predictors for RCAVF abandonment. In conclusion, LVEDV, MPAID assessed by echocardiography and the right side of RCAVF, showed significant predictive implications for the outcomes of RCAVF. Disparities among nationalities in the areas of utilization and patency of AVFs necessitate additional studies.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/156158/2/sdi12871.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/156158/1/sdi12871_am.pd