The effect of spice principles on body composition and lipogenesis in rats

Inclusion of a spice like red pepper fruit and kokum powder in the diet led to a lowering of total lipids, particularly triglycerides in the liver. The total body fat was lowered in animals fed red pepper or capsaicin but not in animals fed kokum powder which had less effect on the total animal body wt. content. Hyperlipogensis and hypertriglyceridemia caused by fructose feeding were significantly decreased in capsaicin-​fed animals. Lipogenesis was decreased as reflected by the reduced activities of the key lipogenic enzymes obsd. in albino rats

Plant Profile, Phytochemistry and Pharmacology of Garcinia indica: A Review

Pre-Menstrual Syndrome (PMS) is defined as the recurrence of psychological and physical symptoms in the luteal phase, which remit in the follicular phase of the menstrual cycle. Symptoms of which fall in three domains: Emotional, Physical and Behavioural, eg: depression, irritability, tension, crying, abdominal cramps, fatigue, bloating, food cravings, poor concentration, social withdrawal etc. Premenstrual symptoms can be managed if diagnosed at right time with suitable pharmacological and non pharmacological treatment. Therefore it is suggested that life style modification & counselling are essential. If neglected, may even be life threatening in patients with severe symptoms can be occur. Non-pharmacologic interventions for PMS include patient education, supportive therapy, and behavioural changes. Behavioural measures include keeping a symptom diary, getting adequate rest and exercise, and making dietary changes. Dietary supplements in women with PMS should include vitamins (A, E and B6), calcium,magnesium, multivitamins/mineral supplements and evening primrose oil. Pharmacological treatment includes anti-depressants and hormonal therapy. Surgery may be considered in severely affected patients who fail to respond to other therapies and also have significant gynaecologic problems for which surgery would be appropriate

Curcumin, garcinol and dietary N-​3 fatty acids, lower the release of lysosomal enzymes in rat peritoneal macrophages

Male Wistar rats (12rats​/group) were fed a diet contg. 8 wt % coconut oil or groundnut oil or cod-​liver oil for a total period of 8 wk. The diets were also supplemented with 2 wt % groundnut oil for providing essential fatty acids. During the last 2 wk, 6 rats form each group were addnl. given curcumin (30 mg​/kg body wt​/day) or garcinol (5 mg​/kg body wt​/day) in 1 mL groundnut oil. The peritoneal macrophages from rats fed cod-​liver oil diet secreted lower levels of lysosomal enzymes collagenase, elastase and hyaluronidase as compared to those from rats fed coconut oil or groundnut oil diets. Curcumin and garcinol significantly lowered the secretion of these lysosomal enzymes from macrophages in animals given coconut oil or groundnut oil diet. These studies indicated that dietary cod-​liver oil (rich in n-​3 fatty acids)​, and spice principles curcumin and garcinol can lower the secretory functions of macrophages in a beneficial manner. (Mol Cell Biochem 203:153-​161, 2000)​

Palm Oil and Rice Bran Oil: Current Status and Future Prospects

The continued demand for edible oils by the ever increasing population makes it pertinent to explore new sources. In this direction, two new edible oils namely palm oil and rice bran oil have been subjected to nutritional and toxicological evaluations of their chemicals constituents. An attempt has been made in this article to assess the acceptability of the two oils based on the various investigations that have been carried out so far

A recurrent-neural-network-based generalized ground-motion model for the Chilean subduction seismic environment

This paper proposes a deep learning-based generalized ground motion model (GGMM) for interface and intraslab subduction earthquakes recorded in Chile. A total of ∼7000 ground-motion records from ∼1700 events are used to train the proposed GGMM. Unlike common ground-motion models (GMMs), which generally consider individual ground-motion intensity measures such as peak ground acceleration and spectral accelerations at given structural periods, the proposed GGMM is based on a data-driven framework that coherently uses recurrent neural networks (RNNs) and hierarchical mixed-effects regression to output a cross-dependent vector of 35 ground-motion intensity measures (denoted as IM). The IM vector includes geometric mean of Arias intensity, peak ground velocity, peak ground acceleration, and significant duration (denoted as Iageom, PGVgeom, PGAgeom, and D5-95geom, respectively), and RotD50 spectral accelerations at 31 periods between 0.05 and 5 s for a 5 % damped oscillator (denoted as Sa(T)). The inputs to the GGMM include six causal seismic source and site parameters, including fault slab mechanism, moment magnitude, closest rupture distance, Joyne-Boore distance, soil shear-wave velocity, and hypocentral depth. The statistical evaluation of the proposed GGMM shows high prediction power with R2 > 0.7 for most IMs while maintaining the cross-IM dependencies. Furthermore, the GGMM is carefully compared against two state-of-the-art Chilean GMMs, showing that the proposed GGMM leads to better goodness of fit for all periods of Sa(T) compared to the two considered GMMs (on average 0.2 higher R2). Finally, the GGMM is implemented to select hazard-consistent ground motions for nonlinear time history analysis of a sophisticated finite-element model of a 20-story steel special moment-resisting frame. Results of this analysis are statistically compared against those for hazard-consistent ground motions selected based on the conditional mean spectrum (CMS) approach. In general, it is observed that the drift demands computed using the two approaches cannot be considered statistically similar and the GGMM leads to higher demands

A Deep Learning based Generalized Ground Motion Model for the Chilean Subduction Seismic Environment

This paper proposes a deep learning-based generalized ground motion model (GGMM) for interface and inslab subduction earthquakes recorded in Chile. A total of ~7000 ground-motion records from ~1700 events are used to train the GGMM. Unlike common ground-motion models (GMM), which generally consider individual ground-motion intensity measures such as spectral acceleration at a given period, the proposed GGMM is a data-driven framework that coherently uses recurrent neural networks (RNN) and hierarchical mixed-effects regression to output a cross-dependent vector of 35 ground-motion intensity measures (IM). The IM vector includes geomean of Arias intensity, peak ground velocity, peak ground acceleration, and significant duration, and RotD50 spectral accelerations at 32 periods between 0.05 to 5 seconds (denoted as Sa(T)). The inputs to the GMM include six causal seismic source and site parameters. The statistical evaluation of the proposed GGMM shows that the proposed framework results in high prediction power with coefficient of determination R2 > 0.7 for most IMs while maintaining the cross-IM dependencies. Furthermore, it is observed that the proposed GGMM leads to better goodness of fit for all periods of Sa(T) compared to two state-of-the-art Chilean GMMs (on average 0.2 higher R2)

Control Plane Compression

We develop an algorithm capable of compressing large networks into a smaller ones with similar control plane behavior: For every stable routing solution in the large, original network, there exists a corresponding solution in the compressed network, and vice versa. Our compression algorithm preserves a wide variety of network properties including reachability, loop freedom, and path length. Consequently, operators may speed up network analysis, based on simulation, emulation, or verification, by analyzing only the compressed network. Our approach is based on a new theory of control plane equivalence. We implement these ideas in a tool called Bonsai and apply it to real and synthetic networks. Bonsai can shrink real networks by over a factor of 5 and speed up analysis by several orders of magnitude.Comment: Extended version of the paper appearing in ACM SIGCOMM 201

Prevalence of chronic pain in the UK : a systematic review and meta-analysis of population studies

Acknowledgements The authors are grateful for the input of Professor Blair Smith (University of Dundee): his counsel early in the project, and his advice and comments regarding the search strategy; and Professor Danielle van der Windt (Keele University) for helpful advice and comments. Funding The British Pain Society provided financial assistance to AF with the costs of this project. PC was partly supported by an Arthritis Research UK Primary Care Centre grant (reference: 18139).Peer reviewedPublisher PD

Effects of temperature and load during hot impression behavior of Cr-Ni stainless steel

Austenitic Stainless steels are majorly used because of their high resistance to aqueous corrosion and high temperature properties. Some major applications of stainless steels at high temperatures include engine and exhaust components in aircrafts, recuperators in steel mills, and pulverized coal injection lances for blast furnaces. In all the above said applications, the components are constantly subjected to loads and high temperatures. This makes the study of their creep behavior very important to decide the life of the component. Cr-Ni stainless steel was used as a starting material, and hot impression creep test was performed on cylindrical samples of 10 mm height and 15 mm diameter for a dwell time of 150 min at two different loads of 84 and 98 MPa and at two different temperatures 450 and 500 °C. The time vs. indentation depth was plotted, and creep rate was calculated in each case. It was observed that with an increase in time, creep rate increased in the primary creep region and remained almost constant in the secondary creep region irrespective of temperature and load. The indentation depth and creep rate increased with an increase in load and temperature

Stability analysis of surface ion traps

Motivated by recent developments in ion trap design and fabrication, we investigate the stability of ion motion in asymmetrical, planar versions of the classic Paul trap. The equations of motion of an ion in such a trap are generally coupled due to a nonzero relative angle $\theta$ between the principal axes of RF and DC fields, invalidating the assumptions behind the standard stability analysis for symmetric Paul traps. We obtain stability diagrams for the coupled system for various values of $\theta$, generalizing the standard $q$-$a$ stability diagrams. We use multi-scale perturbation theory to obtain approximate formulas for the boundaries of the primary stability region and obtain some of the stability boundaries independently by using the method of infinite determinants. We cross-check the consistency of the results of these methods. Our results show that while the primary stability region is quite robust to changes in $\theta$, a secondary stability region is highly variable, joining the primary stability region at the special case of $\theta=45^{\circ}$, which results in a significantly enlarged stability region for this particular angle. We conclude that while the stability diagrams for classical, symmetric Paul traps are not entirely accurate for asymmetric surface traps (or for other types of traps with a relative angle between the RF and DC axes), they are safe in the sense that operating conditions deemed stable according to standard stability plots are in fact stable for asymmetric traps, as well. By ignoring the coupling in the equations, one only underestimates the size of the primary stability region