Series Of Optimization Steps Research Articles

Human adipose and skeletal muscle tissue DNA, RNA, and protein content.

The purpose of this project was to provide a profile of DNA, RNA, and protein content in adipose tissue, which is relatively understudied in humans, to gain more insight into the amount of tissue that may be required for various analyses. Skeletal muscle tissue was also investigated to provide a direct comparison into potential differences between these two highly metabolically active tissues. Basal adipose and skeletal muscle tissue samples were obtained from 10 (7 M, 3 W) recreationally active participants [25 ± 1 yr; 84 ± 3 kg, maximal oxygen consumption (V̇o2max): 3.5 ± 0.2 L/min, body fat: 29 ± 2%]. DNA, RNA, and protein were extracted and subsequently analyzed for quantity and quality. DNA content of adipose and skeletal muscle tissue was 52 ± 14 and 189 ± 44 ng DNA·mg tissue-1, respectively (P < 0.05). RNA content of adipose and skeletal muscle tissue was 46 ± 14 and 537 ± 72 ng RNA·mg tissue-1, respectively (P < 0.05). Protein content of adipose and skeletal muscle tissue was 4 ± 1 and 177 ± 10 µg protein·mg tissue-1, respectively (P < 0.05). In summary, human adipose had 28% of the DNA, 9% of the RNA, and 2% of the protein found in skeletal muscle per mg of tissue. This information should be useful across a wide range of human clinical investigation designs and various laboratory analyses.NEW & NOTEWORTHY This investigation studied DNA, RNA, and protein contents of adipose and skeletal muscle tissues from young active individuals. A series of optimization steps were investigated to aid in determining the optimal approach to extract high-yield and high-quality biomolecules. These findings contribute to the knowledge gap in adipose tissue requirements for molecular biology assays, which is of increasing importance due to the growing interest in adipose tissue research involving human exercise physiology research.

Twitter Geolocation and Regional Classification via Sparse Coding

We present a data-driven approach for Twitter geolocation and regional classification. Our method is based on sparse coding and dictionary learning, an unsupervised method popular in computer vision and pattern recognition. Through a series of optimization steps that integrate information from both feature and raw spaces, and enhancements such as PCA whitening, feature augmentation, and voting-based grid selection, we lower geolocation errors and improve classification accuracy from previously known results on the GEOTEXT dataset.

Smart air supply terminal for floor-standing room air conditioners based on the identification of human positions

Floor-standing air conditioners (FSACs) have been widely used in civil and office buildings due to their high cooling capacity and easy installation in recent years. Increasing attention has been given to reducing the draft sensation of FSACs. This study introduces a new form of smart terminal for air conditioners that can be automatically adjusted by identifying the positions of humans in the room. The smart terminal has certain practical value given its simple structure and relatively low cost. A target air supply evaluation index for regional thermal comfort evaluation named the air velocity target value is proposed. A computational fluid dynamics model is established, and an orthogonal experiment of 4 factors and 4 levels is designed. The optimal parameters of the terminal are further determined through a series of optimization steps. The effect of the smart terminal is validated with numerical simulation and experimental measures, and the local velocities of the occupied zone are mostly below 0.3 m/s, showing that the draft level in the room is acceptable. Compared with the case of an air conditioner only, the air velocity target value of the room with a smart terminal decreases by 21.3%–63.3%. Compared with other existing manually controlled terminals purchased online, the smart terminal satisfies the minimum air velocity target value in the occupied zone. This study can also be used to guide the positioning and setting of air supplies for different positions of humans in a room under other air distributions.

Processing optimization of SiO2-capped aluminum-doped ZnO thin films for transparent heater and near-infrared reflecting applications

In this study, a series of optimization steps were performed in the production of Al-doped ZnO (AZO) thin films to tailor their properties as efficient transparent heaters and for near-infrared (NIR) reflectance. The films were produced on 50 × 75 mm2 glass substrates via magnetron sputtering and capped with a protective SiO2 layer. Processing parameters such as deposition temperature, film thickness, and annealing conditions were all optimized in terms of structure, morphology, optical/electrical properties, and heating/deicing behavior. Electro-thermal characteristics of the films were investigated using a thermal imaging infrared camera under various input voltages. The optimized AZO/SiO2 coatings displayed impressive room-temperature electrical conductivity (σ) of nearly 3774 S/cm with a sheet resistance (Rs) of 3.53 Ω/□, carrier concentration (η) of 1.14 × 1021, and Hall mobility (µ) of 20.48 cm2/Vs. These films exhibited very high optical transmittance (above 96%) in the visible range and reflectance (73% at 2500 nm) in the NIR region. The highest figure of merit (FOM) was achieved as 237 (× 10–3 Ω−1). Deicing tests were performed with samples cooled to − 40 °C and resulted with complete removal of ice/water only within 3 min. In addition, the heater exhibited a high surface temperature of 161 °C (12 V), a good thermal resistance value (219 °C cm2/Watts) with stable and reversible heating behavior. More importantly, these results reveal the potential of optimized AZO/SiO2 coatings as alternatives to transparent tin-doped indium oxide heaters and NIR reflecting mirrors for vehicular applications.

Design of a compact shielding envelope and elements of radiological protection at the TRIUMF-ARIEL facility

The Advanced Rare Isotope Laboratory (ARIEL) is currently under construction at TRIUMF. ARIEL's mission is to supply existing experiments with more diverse and more intense radioactive ion beams (RIB) produced via the Isotope Separation On-Line (ISOL) method. The drivers inducing the nuclear reactions at ARIEL include a 500 MeV proton beam from the main cyclotron as well as a 35 MeV electron beam with the intent of simultaneous operation using two separate additional target stations. The generated RIB will be made available to experiments in condensed matter and subatomic physics as well as practical applications such as medical isotopes for life sciences research.This work details the various simulations performed using the Monte Carlo (MC) particle transport and interaction code FLUKA. The code was systematically employed to predict prompt and residual radiation levels to characterize the radiation fields generated via the irradiation of various types of targets. An iterative combined development process between CAD-based engineering of the shielding envelope, nuclear physics optimization of the target system and particle tracing simulations was established.Preliminary simulation studies resulted in the selection of shielding materials and respective thicknesses with the goal of maximizing attenuation while mitigating residual activation. The results provide clear operational constraints and aid in determination of expected dose rates outside of shielding in high occupancy areas. A series of optimization steps followed the preliminary work, testing various parameters including beam power, target material and shielding configurations. These results were evaluated considering technical feasibility, safety, and economic impacts on ARIEL construction and eventual operation.Although the project, in its infancy, is expected to proceed through stages of increasing complexity, the shielding and radiation transport modeling completed to date helps to define and validate nominal operation and failure scenario procedures for all future project stages.

Through the Looking Glass: Adventures in Kinase Inhibitor Design and Optimization

Developing a viable new drug candidate is difficult. Developing one that is a small molecule kinase inhibitor that binds competitively with respect to ATP with superb selectivity is even more difficult, which makes the design and optimization work described by Jimenez et al. (J. Med. Chem., DOI: 10.1021/jm301465a) particularly remarkable. They took a lead from a high-throughput screen against protein kinase C θ (PKCθ) through a series of optimization steps, culminating in the demonstration of in vivo activity in mice. Having identified and improved the hinge-binding "warhead" at one end of their lead molecule, they proceeded to use structure-based design tools to guide modification of the other end to enhance selectivity over a closely related isoform of the kinase. With that accomplished, they used a series of protection and deprotection maneuvers to modify the central portion of the series scaffold to further enhance potency against the target while also improving pharmacokinetic properties. The project was a success at the preclinical level: oral administration of the ultimate analogue obtained was effective at suppressing interleukin-2 induction in mice.

TU‐EE‐A1‐05: Exploring the Spatial Trade‐Off in Treatment Planning

Purpose: To include spatial information during multi‐criteria treatment planning. Particularly, to study whether constrained optimization on the voxel level allows to deliberately trade‐off the dose delivered to one region of a volume of interest (VOI) with respect to other clinical goals. Method and Materials: We extended a stepwise optimization method for robotic radiosurgery to interactively modify dose constraints on a voxel level. The optimization problem is solved using linear programming, and every term in the objective function is matched by a corresponding constraint. Clinical goals are addressed separately and maintained using the constraints. A trade‐off among the clinical goals is then explored by a series of optimization steps. For visualization, VOIs are represented by a 3D grid of spheres, where each sphere represents a voxel and can be selected in a 3D scene. Constraints on the dose in the selected voxels can be considered independently for subsequent optimization steps. The method was applied to a prostate case, where we studied trade‐offs with respect to the maximum dose in the rectum. Results: Relaxing the upper dose bound on a set of voxels in the prostate lobes by 150 cGy allowed to reduce the maximum rectum dose by 100 cGy. Likewise, a relaxation of the lower dose bound on a few voxels on the prostate surface by 100 cGy allowed to further reduce the maximum dose in th rectum by 157 cGy. Conclusion: Spatial information is not available from cumulative statistics typically used as criteria for treatment planning. Our results indicate that it is possible to include spatial information in interactive multi‐criteria optimization. The proposed method can be used when clinical goals can be expressed with respect to a subregion of a VOI. Conflict of Interest: Research partially sponsored by Accuray Inc.

Proteome profile of the MCF7 cancer cell line: a mass spectrometric evaluation

The development of novel proteomic technologies that will enable the discovery of disease specific biomarkers is essential in the clinical setting to facilitate early diagnosis and increase survivability rates. We are reporting a shotgun two-dimensional (2D) strong cationic exchange/reversed-phase liquid chromatography/electrospray ionization tandem mass spectrometry (SCX/RPLC/ESI-MS/MS) protocol for the analysis of proteomic constituents in cancerous cells. The MCF7 breast cancer cell line was chosen as a model system. A series of optimization steps were performed to improve the LC/MS experimental setup, sample preparation, data acquisition and database search protocols, and a data filtering strategy was developed to enable confident identification of a large number of proteins and potential biomarkers. This research has resulted in the identification of >2000 proteins using multiple filtering and p-value sorting. Approximately 1600-1900 proteins had p < 0.001, and, of these, approximately 60% were matched by >or=2 unique peptides. Alternatively, >99% of the proteins identified by >or=2 unique peptides had p < 0.001. When searching the data against a reversed database of proteins, the rate of false positive identifications was 0.1% at the peptide level and 0.4% at the protein level. The typical reproducibility in detecting overlapping proteins across replicate runs exceeded 90% for proteins matched by >or=2 unique peptides. According to their biological function, approximately 200 proteins were involved in cancer-relevant cellular processes, and over 25 proteins were previously described in the literature as putative cancer biomarkers, as they were found to be differentially expressed between normal and cancerous cell states. Among these, biomarkers such PCNA, cathepsin D, E-cadherin, 14-3-3-sigma, antigen Ki-67, TP53RK, and calreticulin were identified. These data were generated by subjecting to MS analysis approximately 42 microg of sample, analyzing 16 SCX peptide fractions, and interpreting approximately 55,000 MS2 spectra. Total MS time required for analysis was 40 h.

Micellar electrokinetic chromatography with laser‐induced fluorescence detection for rapid and sensitive analysis of two new bioactive reagents using dynamic covalent coating

A simple, rapid, selective, and sensitive micellar electrokinetic chromatography (MEKC) with laser-induced fluorescence detection (LIF) method was developed, using hexamethyldisilazane (HMDS) as dynamic covalent coating (DCC), for the analysis of two new bioactive agents N-n-hexyl-N'-(sodium p-aminobenzenesulfonate) thiourea (HXPT) and N-n-undecyl-N'-(sodium p-aminobenzenesulfonate) thiourea (UPT) derivatized with 4-chloro-7-nitrobenzo-2-oxa-1,3-diazole. MEKC methods both not using DCC and using DCC were investigated. In a series of optimization steps, DCC and a running buffer of 20 mM Na2B4O7 + 16 mM SDS + 8% acetonitrile were applied for determination of the derivatives. Linear relationships for HXPT and UPT were obtained in the range of 5 to 100 microM (correlation coefficient: 0.9986 for HXPT, 0.9978 for UPT), and the detection limits for HXPT and UPT were 16.5 and 39.0 ng mL(-1). The sensitivity was improved over that of fluorescence spectroscopy methods. The method was applied to the analysis of the two reagents in lab water waste with recoveries in the range of 95.6-107.5%.

Optimization of a fixed solar thermal collector

Criteria are presented for optimizing solar thermal energy collection. These criteria are then used in setting the design of a fixed solar thermal energy collector. This design is obtained by proceeding carefully through a series of optimization steps. While seeking near optimum performance, features have been retained which should lead to low cost. Initial optimization steps lead to an all glass vacuum collector tube whose side and lower walls are internally silvered to provide optimal Winston concentration on an interior glass tube coated with a selective absorber. Heat transfer calculations, performed for an array module of these collector tubes, produce values for the radiation, heat conduction and pumping losses and indicate operating conditions which minimize these losses. Near this minimum, heat conduction and pumping losses are small and can usually be neglected. Liquids provide much better heat transfer than gases. For liquid heat transfer fluids, the minimum loss collector tube window width (setting the transverse scale) is ∼3 cm and tube length ∼4 m, depending somewhat upon array area and the weighting used for the various losses. A window width of∼5 cm and tube length∼2 m should provide lower cost fabrication, while still allowing operation near minimum loss. Skills now used in the glass and lighting industry are expected to lead to low cost production of these tubes.