Ultrasensitive Surface Plasmon Resonance Biosensor Using Blue Phosphorus–Graphene Architecture

This study theoretically proposed a novel surface plasmon resonance biosensor by incorporating emerging two dimensional material blue phosphorus and graphene layers with plasmonic gold film. The excellent performances employed for biosensing can be realized by accurately tuning the thickness of gold film and the number of blue phosphorus interlayer. Our proposed plasmonic biosensor architecture designed by phase modulation is much superior to angular modulation, providing 4 orders of magnitude sensitivity enhancement. In addition, the optimized stacked configuration is 42 nm Au film/2-layer blue phosphorus /4-layer graphene, which can produce the sharpest differential phase of 176.7661 degrees and darkest minimum reflectivity as low as 5.3787 × 10−6. For a tiny variation in local refractive index of 0.0012 RIU (RIU, refractive index unit) due to the binding interactions of aromatic biomolecules, our proposed biosensor can provide an ultrahigh detection sensitivity up to 1.4731 × 105 °/RIU, highly promising for performing ultrasensitive biosensing application.

Resolving cellular systems by ultra-sensitive and economical single-cell transcriptome filtering

Single-cell transcriptomics has sensitivity limits that restrict low abundance transcript identification, affects clustering and introduce artefact. Here, we describe Constellation DropSeq (C-DropSeq), a molecular transcriptome filter that delivers two orders of magnitude sensitivity gains by maximising read utility while reducing sequencing depth and costs. The simple and powerful method is broadly compatible with library preparation routines and was demonstrated by identifying and characterizing the activation of rare dendritic cell sub-populations.

Optimization and Entropy Production: Application to Carnot-Like Refrigeration Machines

Several optimization models of irreversible reverse cycle machines have been developed based on different optimization criteria in the literature, most of them using linear heat transfer laws at the source and sink. This raises the issue how close to actual operation conditions they are, since the heat transfer law on the phase-change processes is dependent on ΔT3. This paper addresses this issue by proposing a general model for study and optimization of thermal machines with two heat reservoirs applied to a Carnot-like refrigerator, with non-linear heat transfer laws and internal and external irreversibility. The optimization was performed using First and Second Law of Thermodynamics and the Lagrange multipliers method. Thus, several constraints were imposed to the system, also different objective functions were considered, allowing finding the optimum operating conditions, as well as the limited variation ranges of the system parameters. Results show that the nature of the heat transfer laws affects the optimum values of system parameters for obtaining maximum performances and also their magnitude. Sensitivity studies with respect to system several parameters are presented. The results contribute to the understanding of the system limits in operation under different constraints and allow choosing the most convenient variables in given circumstances.

Comparative Studies of Chemoresistive Gas Sensors Based on Multiple Randomly Connected Wires and Arrays of Single-Wires

Chemoresitive gas sensors based on multiple nanowires (M-NWs) randomly grown and electrically inter-connected on the top of interdigitated electrodes (IDEs) and arrays of single nanowires connected between faced nanoelectrodes (A-S-NWs) are developed in this work. These systems, consisting of gas sensitive tungsten oxide nanowires (NWs), are tested to NO2, and their performance regarding the response magnitude, sensitivity and response rate are evaluated here.

Optimization and Entropy Production: Application to Carnot-Like Refrigeration Machines

Several optimization models of irreversible reverse cycle machines have been developed based on different optimization criteria in the literature, most of them using linear heat transfer laws at the source and sink. This raises the issue how close to actual operation conditions they are, since the heat transfer law on the phase-change processes is dependent on ΔT3. This paper addresses this issue by proposing a general model for study and optimization of thermal machines with two heat reservoirs applied to a Carnot-like refrigerator, with non-linear heat transfer laws and internal and external irreversibility. The optimization was performed using First and Second Law of Thermodynamics and Lagrange multipliers method. Thus, several constraints were imposed to the system, also different objective functions were considered, allowing finding the optimum operating conditions, as well as the limited variation ranges of the system parameters. Results show that the nature of the heat transfer laws affects the optimum values of system parameters for obtaining maximum performances and also their magnitude. Sensitivity studies with respect to system several parameters are presented. The results contribute to the understanding of the system limits in operation under different constraints and allow choosing the most convenient variables in given circumstances.

Empirical noise-mean fitness landscapes and the evolution of gene expression

The effects of cell-to-cell variation (noise) in gene expression have proven difficult to quantify, in part due to the mechanistic coupling of noise to mean expression. To independently evaluate the effects of changes in expression mean and noise we determined the fitness landscapes in mean-noise expression space for 33 genes in yeast. The landscapes can be decomposed into two principal topologies: the fitness effects of protein shortage and surplus. For most genes, the fitness impact of sustained (mean) and short-lived (noise) deviations away from the expression optimum are linked and of similar magnitude. Sensitivity to both protein shortage and surplus creates a fitness landscape in which an epistatic ratchet uncouples the evolution of noise from mean expression, promoting noise minimization. These results demonstrate that noise is detrimental for many genes and reveal non-trivial consequences of mean-noise-fitness topologies for the evolution of gene expression systems.

Modulation of impulsivity and reward sensitivity in intertemporal choice by striatal and midbrain dopamine synthesis in healthy adults

Converging evidence links individual differences in mesolimbic and mesocortical dopamine (DA) to variation in the tendency to choose immediate rewards (“ Now”) over larger, delayed rewards (“ Later”), or “ Now bias.” However, to date, no study of healthy young adults has evaluated the relationship between Now bias and DA with positron emission tomography (PET). Sixteen healthy adults (ages 24–34 yr; 50% women) completed a delay-discounting task that quantified aspects of intertemporal reward choice, including Now bias and reward magnitude sensitivity. Participants also underwent PET scanning with 6-[18F]fluoro-l- m-tyrosine (FMT), a radiotracer that measures DA synthesis capacity. Lower putamen FMT signal predicted elevated Now bias, a more rapidly declining discount rate with increasing delay time, and reduced willingness to accept low-interest-rate delayed rewards. In contrast, lower FMT signal in the midbrain predicted greater sensitivity to increasing magnitude of the Later reward. These data demonstrate that intertemporal reward choice in healthy humans varies with region-specific measures of DA processing, with regionally distinct associations with sensitivity to delay and to reward magnitude.