Search

A new method is presented for quantifying proteomic and metabolomic profile data by liquid chromatography-mass spectrometry (LC-MS) with electrospray ionization. This biotechnology provides differential expression measurements and enables the discovery of biological markers (biomarkers). Work presented here uses human serum but is applicable to any fluid or tissue. The approach relies on linearity of signal versus molecular concentration and reproducibility of sample processing. There is no use of isotopic labeling or chemically similar standard materials. Linear standard curves are reported for a variety of compounds introduced into human serum. As a measure of analytical reproducibility for proteome and metabolome sampling, median coefficients of variation of 25.7 and 23.8%, respectively, were determined for ~3400 molecular ions (not counting their numerous isotopes) from 25 independently processed human serum samples, corresponding to a total of 85 000 individual molecular ion measurements.

Studies have shown that some peptides and small molecules can induce non IgE-mediated anaphylactoid reactions through mast cell activation. Upon activation, mast cells degranulate and release vasoactive and proinflammatory mediators, from cytoplasmic granules into the extracellular environment which can induce a cascade of severe adverse reactions. This study describes a lead optimization strategy to select NaV1.7 inhibitor peptides that minimize acute mast cell degranulation (MCD) toxicities. Various in vitro, in vivo, and PKPD models were used to screen candidates and guide peptide chemical modifications to mitigate this risk. Anesthetized rats dosed with peptides demonstrated treatment-related decreases in blood pressure and increases in plasma histamine concentrations which were reversible with a mast cell stabilizer, supporting the MCD mechanism. In vitro testing in rat mast cells with NaV1.7 peptides demonstrated a concentration-dependent increase in histamine. Pharmacodynamic modeling facilitated establishing an in vitro to in vivo correlation for histamine as a biomarker for blood pressure decline via the MCD mechanism. These models enabled assessment of structure-activity relationship (SAR) to identify substructures that contribute to peptide-mediated MCD. Peptides with hydrophobic and cationic characteristics were determined to have an elevated risk for MCD, which could be reduced or avoided by incorporating anionic residues into the protoxin II scaffold. Our analyses support that in vitro MCD assessment in combination with PKPD modeling can guide SAR to improve peptide lead optimization and ensure an acceptable early in vivo tolerability profile with reduced resources, cycle time, and animal use. [ABSTRACT FROM AUTHOR]

Methods for detecting mycotoxins are very important because of the great health hazards of mycotoxins. However, there is a high background and low signal-to-noise ratio in real-time sensing, and therefore it is difficult to meet the fast, accurate, and convenient requirements for control of food quality. Here we constructed a quantitative fluorescence image analysis based on multicolor upconversion nanocrystal (UCN)-encoded microspheres for detection of ochratoxin A and zearalenone. The background-free encoding image signal of UCN-doped microspheres was captured by fluorescence microscopy under near-infrared excitation, whereas the detection image signal of phycoerythrin-labeled secondary antibodies conjugated to the microspheres was captured under blue light excitation. We custom-wrote an algorithm to analyze the two images for the same sample in 10 s, and only the gray value in the red channel of the secondary probe confirmed the quantity. The results showed that this novel detection platform performed feasible and reliable fluorescence image measurements by this method. Additionally, the limit of detection of was 0.34721 ng/mL for ochratoxin A and 0.41162 ng/mL for zearalenone. We envision that this UCN encoding strategy will be usefully applied for fast, accurate, and convenient testing of multiple food contaminants to ensure the safety of the food. [ABSTRACT FROM AUTHOR]

Objective: Antihyperglycemic agents, such as empagliflozin, stimulate proximal tubular natriuresis and improve cardiovascular and renal outcomes in patients with type 2 diabetes. Because dipeptidyl peptidase 4 (DPP-4) inhibitors are used in combination with sodium-glucose cotransporter 2 (SGLT2) inhibitors, we examined whether and how sitagliptin modulates fractional sodium excretion and renal and systemic hemodynamic function.Research Design and Methods: We studied 32 patients with type 2 diabetes in a prospective, double-blind, randomized, placebo-controlled trial. Measurements of renal tubular function and renal and systemic hemodynamics were obtained at baseline, then hourly after one dose of sitagliptin or placebo, and repeated at 1 month. Fractional excretion of sodium and lithium and renal hemodynamic function were measured during clamped euglycemia. Systemic hemodynamics were measured using noninvasive cardiac output monitoring, and plasma levels of intact versus cleaved stromal cell-derived factor (SDF)-1α were quantified using immunoaffinity and tandem mass spectrometry.Results: Sitagliptin did not change fractional lithium excretion but significantly increased total fractional sodium excretion (1.32 ± 0.5 to 1.80 ± 0.01% vs. 2.15 ± 0.6 vs. 2.02 ± 1.0%, P = 0.012) compared with placebo after 1 month of treatment. Moreover, sitagliptin robustly increased intact plasma SDF-1α1-67 and decreased truncated plasma SDF-1α3-67. Renal hemodynamic function, systemic blood pressure, cardiac output, stroke volume, and total peripheral resistance were not adversely affected by sitagliptin.Conclusions: DPP-4 inhibition promotes a distal tubular natriuresis in conjunction with increased levels of intact SDF-1α1-67. Because of the distal location of the natriuretic effect, DPP-4 inhibition does not affect tubuloglomerular feedback or impair renal hemodynamic function, findings relevant to using DPP-4 inhibitors for treating type 2 diabetes. [ABSTRACT FROM AUTHOR]

Abstract: Dynamic voltage scaling (DVS) has been a very effective technique for processor energy reduction. It adjusts processor voltage and frequency level during runtime. In this article, we propose a general and flexible processor voltage scaling algorithm for real-time multitasking systems. Our approach focuses on exploiting dynamic slack that is created when a task finishes earlier than its estimated worst-case execution time (WCET). Our algorithm is efficient enough to execute at runtime and can be configured flexibly to make tradeoffs between running time and energy savings. By rescheduling tasks effectively, we can achieve almost as much energy savings as if there is no arrival time constraints. Furthermore, our approach can effectively incorporate both leakage power consumption as well as variable scaling overhead. Also, it is relatively independent of task characteristics and scheduling policy. Experimental results show that our technique can achieve significant energy savings at runtime over statically generated schedules and up to 12% more savings compared to the state-of-art techniques. [Copyright &y& Elsevier]

The ongoing scaling of semiconductor technology is causing severe increase of on-chip power density and temperature in microprocessors. This urgently requires both power and thermal management during system design. In this paper, we propose a model checking-based technique using extended timed automata to solve the processor frequency assignment problem in a temperature and energy-constrained multitasking system. We also develop a polynomial time-approximation algorithm to address the state-space explosion problem caused by symbolic model checker. Our approximation scheme is guaranteed to not generate any false-positive answer, while it may return false-negative answer in rare cases. Our method is universally applicable since it is independent of any system and task characteristics. Experimental results demonstrate the usefulness of our approach. [ABSTRACT FROM AUTHOR]

In recent years, efficient dynamic reconfiguration techniques have been widely employed for system optimization. Dynamic cache reconfiguration is a promising approach for reducing energy consumption as well as for improving overall system performance. It is a major challenge to introduce cache reconfiguration into real-time multitasking systems, since dynamic analysis may adversely affect tasks with timing constraints. This article presents a novel approach for implementing cache reconfiguration in soft real-time systems by efficiently leveraging static analysis during runtime to minimize energy while maintaining the same service level. To the best of our knowledge, this is the first attempt to integrate dynamic cache reconfiguration in real-time scheduling techniques. Our experimental results using a wide variety of applications have demonstrated that our approach can significantly reduce the cache energy consumption in soft real-time systems (up to 74%). [ABSTRACT FROM AUTHOR]

System optimization techniques are widely used to improve energy efficiency as well as overall performance. Dynamic voltage scaling (DVS) is well studied and known to be successful in reducing processor energy consumption. Due to the increasing significance of the memory subsystem's energy consumption, dynamic cache reconfiguration (DCR) techniques are recently proposed at the aim of improving cache subsystem's energy efficiency. As the manufacturing technology scales into the order of nanometers, leakage current, which leads to static power consumption, becomes a significant contributor in the overall power dissipation. In this paper, we consider various system components and study their impact on system-wide energy consumption under different processor voltage levels as well as cache configurations. Based on the observation, we efficiently integrate DVS and DCR techniques together to make decisions judiciously so that the total energy consumption is minimized. Our studies show that considering only DVS or DCR and ignoring the impact from other system components may lead to incorrect conclusions in overall energy savings. Experimental results demonstrate that our approach outperforms existing leakage-aware DVS techniques by 47.6% and leakage-oblivious DVS + DCR technique by up to 23.5%. [ABSTRACT FROM AUTHOR]

Abstract: System optimization techniques based on dynamic reconfiguration are widely adopted for energy conservation. While dynamic voltage scaling (DVS) techniques have been extensively studied for processor energy conservation, dynamic cache reconfiguration (DCR) for reducing cache energy consumption in multitasking systems is still in its infancy. In this paper, we propose a general and flexible algorithm for energy optimization based on dynamic reconfiguration in multitasking systems. Our algorithm is flexibly parameterized and can be used to provide tradeoffs between running time and solution quality. Furthermore, it can easily incorporate variable reconfiguration overhead. Experimental results show that our technique can generate near-optimal solutions with significantly low running time and memory requirements. [Copyright &y& Elsevier]

Focuses on a study which created a combinatorial library of protein sequences de novo to probe the relationship between amino acid sequence and the propensity to form amyloid. Methodology; Design of sequences; Biochemical and structural characterization of de novo amyloid proteins; Amyloidogenic propensity of alternating polar/nonpolar patterns.

Background: Eosinophilic gastritis (EoG) has rarely been reported in conjunction with gluten-sensitive enteropathy (GSE). When this does occur, patients typically present with gastrointestinal symptoms. To our knowledge, hypoproteinemia has not been reported as the primary manifestation. Anti-IgE therapy, such as omalizumab, lowers eosinophil counts in the blood, lungs, and gut. Its efficiency in treating active EoG remain unknown. Case presentation: We report a 33-month-old boy with a history of food allergy and atopic dermatitis who developed recurrent edema, hypoproteinemia, and eosinophilia at the age of 14 months. The diagnoses of EoG and GSE were confirmed based on the clinical presentation and results of gastrointestinal biopsies and serological testing. Although prednisone and dietary intervention were initially effective, the boy developed prednisone-related facial swelling. After stopping prednisone, his symptoms relapsed. Subsequent treatment with omalizumab, combined with dietary intervention, showed good efficacy and safety. Conclusions: To our knowledge, this is the first case of concurrent EoG and GSE that presented primarily with hypoproteinemia. We highlight the rare manifestations of these two diseases to raise clinical suspicion and prevent missed and delayed diagnoses. The pathogenesis of EoG is heterogeneous and complex. Omalizumab showed good efficacy, indicating that IgE-mediated processes may be involved in the pathogenesis of this patient's diseases. [ABSTRACT FROM AUTHOR]

At present, it is known that when there is clay in concrete, polycarboxylates (PCE) will preferably adsorb in the clay, so that PCE cannot be fully combined with cement particles, which reduces the workability of the cement slurry. In this paper, a new type of maltitol–ammonium salt cationic (KN-lm) sacrificial agent (SA) has been successfully developed via a simple method, which makes PCE easier to bond with cement particles in the cement slurry containing clay. The effect of KN-lm on the fluidity of clay-containing cement paste is studied, and the experimental results show that KN-lm, as an efficient SA of cement slurry, makes PCE more compatible with clay-containing cement slurry, and increases the initial fluidity of cement slurry by about 19%. Further investigations of TOC, XRD, and zeta potential measurements reveal that a KN-lm ion is only preferably adsorbed into clay compared to PCE through electrostatic adsorption but without having any crystal structure change, thus resulting in good dispersion of cement particles. The addition of KN-lm plays an important role in hindering the hydration expansion of the clay by preferential electrostatic adsorption, which means PCE cannot easily insert into the interlayer of the clay. [ABSTRACT FROM AUTHOR]

Lipids are structurally diverse molecules that play a pivotal role in a plethora of biological processes. However, deciphering the biological roles of the specific lipids is challenging due to the existence of numerous isomers. This high chemical complexity of the lipidome is one of the major challenges in lipidomics research, as the traditional liquid chromatography–mass spectrometry (LC-MS) based approaches are often not powerful enough to resolve these isomeric and isobaric nuances within complex samples. Thus, lipids are uniquely suited to the benefits provided by multidimensional liquid chromatography–ion mobility–mass spectrometry (LC-IM-MS) analysis. However, many forms of lipid isomerism, including double-bond positional isomers and regioisomers, are structurally similar such that their collision cross section (CCS) differences are unresolvable via conventional IM approaches. Here we evaluate the performance of a high resolution ion mobility (HRIM) system based on structures for lossless ion manipulation (SLIM) technology interfaced to a high resolution quadrupole time-of-flight (QTOF) analyzer to address the noted lipidomic isomerism challenge. SLIM implements the traveling wave ion mobility technique along an ∼13 m ion path, providing longer path lengths to enable improved separation of isomeric features. We demonstrate the power of HRIM-MS to dissect isomeric PC standards differing only in double bond (DB) and stereospecific number (SN) positions. The partial separation of protonated DB isomers is significantly enhanced when they are analyzed as metal adducts. For sodium adducts, we achieve close to baseline separation of three different PC 18:1/18:1 isomers with different cis-double bond locations. Similarly, PC 18:1/18:1 (cis-9) can be resolved from the corresponding PC 18:1/18:1 (trans-9) form. The separation capacity is further enhanced when using silver ion doping, enabling the baseline separation of regioisomers that cannot be resolved when measured as sodium adducts. The sensitivity and reproducibility of the approach were assessed, and the performance for more complex mixtures was benchmarked by identifying PC isomers in total brain and liver lipid extracts. [ABSTRACT FROM AUTHOR]

The creep life prediction of austenitic heat-resistant steel is necessary to guarantee the safe operation of the high-temperature components in thermal power plants. This work presents a machine learning model that can be applied to predict the creep life of austenitic steels, offering a novel method and approach for such predictions. In this paper, creep life data from six typical austenitic heat-resistant steels are used to predict their creep life using various machine learning models. Moreover, the dissimilarities between the machine learning model and the conventional lifetime prediction method are compared. Finally, the influence of different input characteristics on creep life is discussed. The results demonstrate that the prediction accuracy of machine learning depends on both the model and the dataset used. The Gaussian model based on the second dataset achieves the highest level of prediction accuracy. Additionally, the accuracy and the generalization ability of the machine learning model prediction are significantly better than those of the traditional model. Lastly, the effect of the input characteristics on creep life is generally consistent with experimental observations and theoretical analyses. [ABSTRACT FROM AUTHOR]

Objective: To establish a pre-operative acute ischemic stroke risk (AIS) prediction model using the deep neural network in patients with acute type A aortic dissection (ATAAD). Methods: Between January 2015 and February 2019, 300 ATAAD patients diagnosed by aorta CTA were analyzed retrospectively. Patients were divided into two groups according to the presence or absence of pre-operative AIS. Pre-operative AIS risk prediction models based on different machine learning algorithm was established with clinical, transthoracic echocardiography (TTE) and CTA imaging characteristics as input. The performance of the difference models was evaluated using the receiver operating characteristic (ROC), precision-recall curve (PRC) and decision curve analysis (DCA). Results: Pre-operative AIS was detected in 86 of 300 patients with ATAAD (28.7%). The cohort was split into a training (211, 70% patients) and validation cohort (89, 30% patients) according to stratified sampling strategy. The constructed deep neural network model had the best performance on the discrimination of AIS group compare with other machine learning model, with an accuracy of 0.934 (95% CI: 0.891–0.963), 0.921 (95% CI: 0.845–0.968), sensitivity of 0.934, 0.960, specificity of 0.933, 0.906, and AUC of 0.982 (95% CI: 0.967–0.997), 0.964 (95% CI: 0.932–0.997) in the training and validation cohort, respectively. Conclusion: The established risk prediction model based on the deep neural network method may have the big potential to evaluate the risk of pre-operative AIS in patients with ATAAD. [ABSTRACT FROM AUTHOR]