Bayesian Peak Picking for NMR Spectra

AbstractProtein structure determination is a very important topic in structural genomics, which helps people to understand varieties of biological functions such as protein-protein interactions, protein–DNA interactions and so on. Nowadays, nuclear magnetic resonance (NMR) has often been used to determine the three-dimensional structures of protein in vivo. This study aims to automate the peak picking step, the most important and tricky step in NMR structure determination. We propose to model the NMR spectrum by a mixture of bivariate Gaussian densities and use the stochastic approximation Monte Carlo algorithm as the computational tool to solve the problem. Under the Bayesian framework, the peak picking problem is casted as a variable selection problem. The proposed method can automatically distinguish true peaks from false ones without preprocessing the data. To the best of our knowledge, this is the first effort in the literature that tackles the peak picking problem for NMR spectrum data using Bayesian method

Development of Empirical Equations to Predict Sweating Skin Surface Temperature for Thermal Manikins in Warm Environments.

Clothing evaporative resistance is one of the most important parameters for clothing comfort. The clothing evaporation resistance can be measured on a sweating guarded hotplate, a sweating thermal manikin or a human subject. The sweating thermal manikin gives the most accurate value on evaporative resistance of the whole garment ensemble compared to the other two methods. The determination of clothing evaporative resistance on a thermal manikin requires sweating simulation. This can be achieved by either a pre-wetted fabric skin on top of the manikin (TORE), or a waterproof but permeable Gore-tex skin filled with water inside. The addition of a fabric skin can introduce a temperature difference between the manikin surface and the sweating skin surface. However, calculations on clothing evaporative resistance have often been based on the thermal manikin surface temperature. A previous study showed that the temperature differences can cause an error up to 35.9 % on the clothing evaporative resistance. In order to reduce such an error, an empirical equation to predict the skin surface temperature might be helpful. In this study, a cotton knit fabric skin and a Gore-tex skin were used to simulate two types of sweating. The cotton fabric skin was rinsed with tap water and centrifuged in a washing machine for 4 seconds to ensure no water drip. A Gore-tex skin was put on top of the pre-wetted cotton skin on a dry heated thermal manikin ‘Tore’ in order to simulate senseless sweating, similar to thermal manikins ‘Coppelius’ and ‘Walter’. Another simulation involved the pre-wetted fabric skin covered on top of the Gore-tex skin in order to simulate sensible sweating. This type of sweating simulation can be widely found on many thermal manikins worldwide, e.g. ‘Newton’. Six temperature sensors (Sensirion Inc, Switzerland) were attached on six sites of the skin outer surface by white thread rings to record the skin surface temperature. Twelve skin tests for each skin combination were performed at three different ambient temperatures: 34, 25 and 20 oC. Two empirical equations to predict the skin surface temperature were developed based on the mean manikin surface temperature, mean fabric skin surface temperature and the total heat loss. The prediction equations for the senseless sweating and sensible sweating on the thermal manikin ‘Tore’ were Tsk=34.0-0.0146HL and Tsk=34.0-0.0190HL, respectively. Further study should validate these two empirical equations, however

A review of technology of personal heating garments.

Modern technology makes garments smart, which can help a wearer to manage in specific situations by improving the functionality of the garments. The personal heating garment (PHG) widens the operating temperature range of the garment and improves its protection against the cold. This paper describes several kinds of PHGs worldwide; their advantages and disadvantages are also addressed. Some challenges and suggestions are finally addressed with regard to the development of PHGs

Does PHS Model Predict Acceptable Skin and Core Temperatures While Wearing Protective Clothing.

Mathematical modeling is very important when experimental settings with human subjects are restricted to thermal limits necessary to protect the individual. The predicted heat strain (PHS) model has been published AS ISO 7933 for about six years. It describes a method for predicting the sweat rate and internal core temperature that the human body will develop in response to the working conditions. The PHS model was developed based on thousands of laboratory and field experiments collected from eight European laboratories. However, most of the laboratory and field tests were performed on human subjects with light clothing ensembles (0.38±0.34 clo < Icl < 0.77±0.18 clo). The prediction of physiological responses while human wearing highly insulating protective clothing might be weak. In order to check the prediction accuracy of current PHS model while using protective clothing, we conducted totally series of human subject tests at a simulated hot environment. The results of 18 tests involving the high visibility (HV), military (MIL) and firefighting (FIRE) clothing are reported here. Six human subjects were asked to walk on a treadmill at 4.5 km/h at 40 oC for 70 min. Two humidity levels were chosen: 2 kPa (RH = 27 %) and 3 kPa (RH = 41 %) depending on the garment. The rectal temperature, skin temperature, heart rate and metabolic rate were measured. The clothing and the subjects were weighed before and after the exposure in order to calculate the sweat and evaporation rate. The observed and predicted rectal temperatures and mean skin temperatures were compared. The PHS model failed to predict the final rectal temperature in FIRE and the predicted estimate was 1.83 oC higher than the observed value after 63-min exposure. The predicted curve showed a much deeper linear increase during the whole exercise. None of the predicted mean skin temperatures during the three testing scenarios were accurately predicted. The PHS model was consistently providing conservative mean skin temperature evaluations. The predicted curve in HV and MIL showed a much shallower increase during the early portion of the exposure and plateaued at temperatures lower than ever achieved by the subjects. The observed sweat rates were 556±110 g/h in HV, 717±200 g/h in MIL, and 834±274 g/h in FIRE. There was no significant difference between the predicted total sweat values and the experimental data (P=0.073). In summary, the PHS model produce prediction of core temperature which has an unacceptable error when human wore thick protective clothing. The weak prediction on the mean skin temperature in HV and MIL was in agreement with the empirical prediction equation in the source codes has the poorest and lowest correlation when a clothed human subject exercised at the humidity level above 2 kPa. It is therefore recommended that the PHS model should be amended to development and validated by manipulation of individual algorithms or physical (or physiological) parameters

Development and validation of an empirical equation to predict wet fabric skin surface temperature of thermal manikins

Thermal manikins are useful tools to study clothing comfort and environmental ergonomics. The simulation of sweating can be achieved by putting a highly wicking stretchable knit fabric “skin” on top of the manikin. However, the addition of such a fabric skin makes it difficult to accurately measure the skin surface temperature. Moreover, it takes considerable amount of time to measure the fabric skin surface temperature at each test. At present the attachment of temperature sensors to the wet fabric skin is still a challenge. The distance of the sensors to the fabric skin could significantly influence the temperature and relative humidity values of the wet skin surface. Hence, we conducted an intensive skin study on a dry thermal manikin to investigate the relationships among the nude manikin surface temperature, heat losses and the fabric skin surface temperature. An empirical equation was developed and validated on the thermal manikin „Tore‟ at Lund University. The empirical equation at an ambient temperature 34.0 ºC is Tsk =34.00-0.0103HL. This equation can be used to enhance the prediction accuracy of wet fabric skin surface temperature and the calculation of clothing evaporative resistance

Energetic stability, structural transition, and thermodynamic properties of ZnSnO[sub 3]

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/98679/1/ApplPhysLett_98_091914.pd