Multinomial logistic regression coefficient and confidence interval of BMI by socioeconomic and demographic characteristics.

<p>Multinomial logistic regression coefficient and confidence interval of BMI by socioeconomic and demographic characteristics.</p

Socioeconomic patterns of underweight and its association with self-rated health, cognition and quality of life among older adults in India

<div><p>Background</p><p>Underweight defined as body mass index (BMI) < 18.5 is associated with negative health and quality of life outcomes including mortality. Yet, little is known about the socioeconomic differentials in underweight and its association with health and well-being among older adults in India. This study examined the socioeconomic differentials in underweight among respondents aged ≥50 in India. Consequently, three outcomes of the association of underweight were studied. These are poor self-rated health, cognition and quality of life.</p><p>Methods</p><p>Cross-sectional data on 6,372 older adults derived from the first wave of the WHO’s Study on global AGEing and adult health (SAGE), a nationally representative survey conducted in six states of India during 2007–8, were used. Bivariate and multivariate regression analyses were applied to fulfil the objectives.</p><p>Results</p><p>The overall prevalence of underweight was 38 percent in the study population. Further, socioeconomic status showed a significant and negative association with underweight. The association of underweight with poor self-rated health (OR = 1.60; <i>p</i> < .001), cognition (β = –0.95; <i>p</i> < .001) and quality of life (β = –1.90; <i>p</i> < .001) were remained statistically significant after adjusting for age, sex, place of residence, marital status, years of schooling, wealth quintile, sleep problems, chronic diseases, low back pain and state/province.</p><p>Conclusion</p><p>The results indicated significant socioeconomic differentials in underweight and its association with poor self-rated health, cognition and quality of life outcomes. Interventions focussing on underweight older adults are important to enhance the overall wellbeing of the growing older population in India.</p></div

Multivariable regression analysis of the association of underweight with poor self-rated health, cognition and quality of life.

<p>Multivariable regression analysis of the association of underweight with poor self-rated health, cognition and quality of life.</p

Sociodemographic characteristics of the study population by body mass index category (Weighted %).

<p>Sociodemographic characteristics of the study population by body mass index category (Weighted %).</p

Weighted prevalence of poor self-rated health (SRH), mean cognition and quality of life (QoL) scores by body mass index category.

<p>Weighted prevalence of poor self-rated health (SRH), mean cognition and quality of life (QoL) scores by body mass index category.</p

Experiences with Occupancy Based Building Management Systems

<p>—Buildings are one of the largest consumers of electricity. Dominant electricity consumption within the buildings,<br>contributed by plug loads, lighting and air conditioning, can be<br>significantly improved using Occupancy-based Building Management Systems (Ob-BMS). In this paper, we address three critical<br>aspects of Ob-BMS i.e. 1) Modular sensor node design to support<br>diverse deployment scenarios; 2) Building architecture to support<br>and scale fine resolution monitoring; and 3) Detailed analysis of<br>the collected data for smarter actuation. We present key learning<br>across these three aspects evolved over more than one year of<br>design and deployment experiences.<br>The sensor node design evolved over a period of time to<br>address specific deployment requirements. With an opportunity<br>at the host institute where two dorm buildings were getting<br>constructed, we planned for the support infrastructure required<br>for fine resolution monitoring embedded in the design phase<br>and share our preliminary experiences and key learning thereof.<br>Prototype deployment of the sensing system as per the planned<br>support infrastructure was performed at two faculty offices with<br>effective data collection worth 45 days. Collected data is analyzed<br>accounting for efficient switching of appliances, in addition to<br>energy conservation and user comfort as performed in the earlier<br>occupancy based frameworks. Our analysis shows that occupancy<br>prediction using simple heuristic based modeling can achieve<br>similar performance as more complex Hidden Markov Models,<br>thus simplifying the analytic framework.</p> <p> </p

Efficient Strategy for the Construction of Both Enantiomers of the Octahydropyrroloquinolinone Ring System: Total Synthesis of (+)-Aspidospermidine

An efficient and highly stereoselective intramolecular [3 + 2] cycloaddition of nonstabilized azomethine ylide generated from a designed bicyclic aminal precursor is reported for the synthesis of both (−)- and (+)-octahydropyrroloquinolinone. One of the enantiomers is further advanced to accomplish the total synthesis of (+)-aspidospermidine

Appendix A from The Fourier decomposition method for nonlinear and non-stationary time series analysis

For many decades, there has been a general perception in the literature that Fourier methods are not suitable for the analysis of nonlinear and non-stationary data. In this paper, we propose a novel and adaptive Fourier decomposition method (FDM), based on the Fourier theory, and demonstrate its efficacy for the analysis of nonlinear and non-stationary time series. The proposed FDM decomposes any data into a small number of 'Fourier intrinsic band functions' (FIBFs). The FDM presents a generalized Fourier expansion with variable amplitudes and variable frequencies of a time series by the Fourier method itself. We propose an idea of zero-phase filter bank-based multivariate FDM (MFDM), for the analysis of multivariate nonlinear and non-stationary time series, using the FDM. We also present an algorithm to obtain cut-off frequencies for MFDM. The proposed MFDM generates a finite number of bandlimited multivariate FIBFs (MFIBFs). The MFDM preserves some intrinsic physical properties of the multivariate data, such as scale alignment, trend and instantaneous frequency. The proposed methods provide a time–frequency–energy (TFE) distribution that reveals the intrinsic structure of a data. Numerical computations and simulations have been carried out and comparison is made with the empirical mode decomposition algorithms

Metabolic replenishment of cholesterol restores the cell cycle distribution of lovastatin or triparanol-treated cells.

<p>In order to monitor the reversibility of lovastatin or triparanol treatment on the G1 arrest of cells, we utilized two approaches. In the first approach, cells treated with lovastatin (2.5 µM) or triparanol (7.5 µM) were further grown for 24 h in the presence of either 10 or 20% serum (shown in panels (A) and (B), respectively). In the second approach, cells treated with lovastatin (2.5 µM) or triparanol (7.5 µM) were grown for additional 24 h in 20% serum in the presence of respective inhibitors (see panels (A) and (B)). Cell numbers in G1, S and G2 phases are represented by blue, maroon and cyan bars, respectively. Values represent means ± SE of at least four independent experiments. See Materials and Methods for more details.</p

Neutral lipid content increases with cell cycle progression.

<p>(A) A representative confocal image shows the presence of neutral (green) and polar (red) lipids in cells, as visualized after labeling with Nile Red. The scale bar represents 20 µm. Neutral lipids in F111 cells were quantified utilizing Nile Red labeling followed by flow cytometric analysis. Typical Nile Red labeling profile of cells is shown in panel (B). A dot plot depicting Nile Red labeling of cells in G1 (blue), S (red) and G2 (green) phases of cell cycle is shown as an inset. (C) Total cellular neutral lipid content demonstrated an increase as cells progressed from G1 to G2 <i>via</i> S phase of cell cycle. Data represent means ± SE of at least four independent experiments. See Materials and Methods for more details.</p