Comparative efficacy of shilajeet and gum guggal (commiphora mukul) in preventing diet induced hypercholesterolemia in wistar rats

Shilajeet, a polycrest ayurvedic drug hitherto unknown for its hypolipidemic effect has been shown to lower serum cholesterol, liver cholesterol, serum triglycerides and serum phosphlipids of wistar rats fed on a cholesterol (1%) supplemented diet, by 39, 55, 47 and 25 per cent respectively, when 0.2 per cent of it was incorporated in the diet. In comparison, gum guggal (2.0 per cent in the diet) lowered the serum cholesterol, liver cholesterol, serum triglycerides and serum phospholipids by 36, 60, 49 and 8 per cent respectively. The increase in HDL-cholesterol by shilajeet was much higher (53%) compared to that by gum guggal (29%).     

“Can I do well in mathematics reasoning?” Comparing US and Finnish students’ attitude and reasoning via TIMSS 2011

This study investigated differences between the US and Finland in terms of how students’ attitude is related to mathematical reasoning skills through the Trends in International Mathematics and Science Study (TIMSS) 2011. Attitude towards mathematics was observed via 3 TIMSS contextual variables: liking mathematics, valuing mathematics, and confidence in mathematics. Scores for mathematical reasoning were collected from the TIMSS 2011 database. We used hierarchical linear modelling to construct multilevel models with interactions of the attitude variables. Findings showed that confidence in mathematics had the strongest positive relationships with mathematical reasoning in both countries. Finnish students generally reported stronger positive relationships between confidence in mathematics and reasoning than US students. Strong relationships between confidence and reasoning remained visible when examining valuing and liking mathematics. Findings provide important implications regarding the complex interactions between attitude towards mathematics and reasoning, critical for mathematics educators and policymakers to consider in an increasingly competitive international environment.     

Skeletal Muscle Strength and Endurance in Recipients of Lung Transplants

Purpose: Exercise limitation in recipients of lung transplant may be a result of abnormalities in the skeletal muscle. However, it is not clear whether these abnormalities are merely a reflection of the changes observed in the pretransplant condition. The purpose of this paper was to compare thigh muscle volume and composition, strength, and endurance in lung transplant recipients to people with chronic obstructive pulmonary disease (COPD).Methods: Single lung transplant recipients (n=6) and people with COPD (n=6), matched for age, sex, and BMI participated in the study. Subjects underwent MRI to determine muscle size and composition, lower extremity strength testing and an isometric endurance test of the quadriceps.Results: Lung transplant recipients had similar muscle volumes and intramuscular fat infiltration of their thigh muscles and similar strength of the quadriceps and hamstrings to people with COPD who had not undergone transplant. However, quadriceps endurance tended to be lower in transplant recipients compared to people with COPD (15 ± 7 seconds in transplant versus 31 ± 12 seconds in COPD, p = 0.08).Conclusions: Recipients of lung transplant showed similar changes in muscle size and strength as people with COPD, however muscle endurance tended to be lower in people with lung transplants. Impairments in muscle endurance may reflect the effects of immunosuppressant medications on skeletal muscle in people with lung transplant.Key Words: lung transplant, muscle function, MRI     

Delayed Onset Muscle Soreness After Inspiratory Threshold Loading in Healthy Adults

Purpose: Skeletal muscle damage occurs following high-intensity or unaccustomed exercise; however, it is difficult to monitor damage to the respiratory muscles, particularly in humans. The aim of this study was to use clinical measures to investigate the presence of skeletal muscle damage in the inspiratory muscles. Methods: Ten healthy subjects underwent 60 minutes of voluntary inspiratory threshold loading (ITL) at 70% of maximal inspiratory pressure. Maximal inspiratory and expiratory mouth pressures, delayed onset muscle soreness on a visual analogue scale and plasma creatine kinase were measured prior to ITL, and at repeated time points after ITL (4, 24 and 48 hours post-ITL). Results: Delayed onset muscle soreness was present in all subjects 24 hours following ITL (intensity = 22 ± 6 mm; significantly higher than baseline p = 0.02). Muscle soreness was reported primarily in the anterior neck region, and was correlated to the amount of work done by the inspiratory muscles during ITL (r = 0.72, p = 0.02). However, no significant change was observed in maximal inspiratory or expiratory pressures or creatine kinase. Conclusions: These findings suggest that an intense bout of ITL results in muscle soreness primarily in the accessory muscles of inspiration, however, may be insufficient to cause significant muscle damage in healthy adults.Key Words: delayed onset muscle soreness, respiratory muscles, skeletal muscle damage     

Discriminative probabilistic models for expert search in heterogeneous information sources

In many realistic settings of expert finding, the evidence for expertise often comes from heterogeneous knowledge sources. As some sources tend to be more reliable and indicative than the others, different information sources need to receive different weights to reflect their degrees of importance. However, most previous studies in expert finding did not differentiate data sources, which may lead to unsatisfactory performance in the settings where the heterogeneity of data sources is present. In this paper, we investigate how to merge and weight heterogeneous knowledge sources in the context of expert finding. A relevance-based supervised learning framework is presented to learn the combination weights from training data. Beyond just learning a fixed combination strategy for all the queries and experts, we propose a series of discriminative probabilistic models which have increasing capability to associate the combination weights with specific experts and queries. In the last (and also the most sophisticated) proposed model, the combination weights depend on both expert classes and query topics, and these classes/topics are derived from expert and query features. Compared with expert and query independent combination methods, the proposed combination strategy can better adjust to different types of experts and queries. In consequence, the model yields much flexibility of combining data sources when dealing with a broad range of expertise areas and a large variation in experts. To the best of our knowledge, this is the first work that designs discriminative learning models to rank experts. Empirical studies on two real world faculty expertise testbeds demonstrate the effectiveness and robustness of the proposed discriminative learning models.     

Neither Cited nor Foundational: Niklas Luhmann’s Ecological Communication; A Critical Exegesis and Some Theoretical Suggestions for the Future of a Field

In this paper I critically explicate Niklas Luhmann’s treatise Ecological Communication (1989). Locating this rather neglected treatise against the backdrop of the embryonic and amorphous academic field of environmental (or ecological) communication (EC), I argue that the treatise provides the only comprehensive conceptual definition of ecological or environmental communication—and it thus deserves attention as well as scrutiny. Past my detailed explication of Luhmann’s key concepts, I register my deep disagreements with his philosophy and speculate about why his treatise may have suffered neglect within EC. Briefly mentioning critical traditionalism, I also suggest alternatives to Luhmann’s theoretical framework. I devote the last section of the paper to my vision for the future of EC.     

Piecewise Random-Effects Modeling Software Programs

In this software review, we provide a brief overview of four different functions to fit a piecewise random-effects model with unknown changepoints (knots). Specifically, the four functions are: FitPMM from the R routine developed by (Zopluoglu, Harring, & Kohli, 2014), PROC NLMIXED from SAS, BayesPGM from the BayesianPGMM package in R developed by (Lock, Kohli, and Bose, 2018), and stancode_randomchangecorr function developed by (Brilleman, Howe, Wolfe, & Tilling, 2017) implemented to interface with Stan from R using the rstan package. We illustrate the estimation of the piecewise random-effects model using each of these functions by using a sample dataset. We provide appropriate commented code for the four functions, and briefly discuss the strengths and weaknesses of each function.