Our knowledge of the brain has evolved over millennia in philosophical, experimental and theoretical phases. We suggest that the next phase is simulation neuroscience. The main drivers of simulation neuroscience are big data generated at multiple levels of brain organization and the need to integrate these data to trace the causal chain of interactions within and across all these levels. Simulation neuroscience is currently the only methodology for systematically approaching the multiscale brain. In this review, we attempt to reconstruct the deep historical paths leading to simulation neuroscience, from the first observations of the nerve cell to modern efforts to digitally reconstruct and simulate the brain. Neuroscience began with the identification of the neuron as the fundamental unit of brain structure and function and has evolved towards understanding the role of each cell type in the brain, how brain cells are connected to each other, and how the seemingly infinite networks they form give rise to the vast diversity of brain functions. Neuronal mapping is evolving from subjective descriptions of cell types towards objective classes, subclasses and types. Connectivity mapping is evolving from loose topographic maps between brain regions towards dense anatomical and physiological maps of connections between individual genetically distinct neurons. Functional mapping is evolving from psychological and behavioral stereotypes towards a map of behaviors emerging from structural and functional connectomes. We show how industrialization of neuroscience and the resulting large disconnected datasets are generating demand for integrative neuroscience, how the scale of neuronal and connectivity maps is driving digital atlasing and digital reconstruction to piece together the multiple levels of brain organization, and how the complexity of the interactions between molecules, neurons, microcircuits and brain regions is driving brain simulation to understand the interactions in the multiscale brain.

No abstract

No abstract

In accurately diagnosing Alzheimer’s disease (AD) and distinguishing AD from other dementia, the concentration ratio of amyloid-beta 42 (Aβ42) to Aβ40 is more reliable than the concentration of Aβ42 alone. For the multiplex PEC assay, generating an independent photocurrent of multiple targets on a single interface is a great challenge. Herein, an i-motif-based switchable sensing approach is proposed to construct a pH-regulated multiplex PEC immunosensor for Aβ42 and Aβ40 by using Bi-TBAPy as an efficient photoactive cathode material. An independent photocurrent signal of Aβ42 and Aβ40 is produced through the regulation of the electron-transfer tunneling distance by a pH-dependent configuration transition of the i-motif DNA. In a 96-well plate, immunological recognition of Aβ42 (or Aβ40) coupled with an enzymatic catalytic reaction produces an acidic (or alkaline) lysis solution, which triggers the formation and unravelment of the i-motif structure. The above configuration transition regulates the distance between Au NPs labeled SH-DNA and Bi-TBAPy, leading to PEC signal switching. Smart integration of the pH-responsive switchable DNA probe with a high-efficiency photocathode enables the precise monitoring of Aβ42 and Aβ40 at a single interface in a wide detection range (10 fg/mL ∼ 1 μg/mL and 1 pg/mL ∼ 1 μg/mL) with detection limit of 4.5 fg/mL and 0.52 pg/mL, respectively. The proposed i-motif-based switchable sensing strategy paves a new avenue for a multiplex PEC assay on a single interface, showing great prospects in bioanalysis and early disease diagnosis.

A new Monte Carlo transport code RMC has been being developed by Department of Engineering Physics, Tsinghua University, Beijing as a tool for reactor core analysis on high-performance computing platforms. To meet the requirements of reactor analysis, RMC now has such functions as criticality calculation, fixed-source calculation, burnup calculation and kinetics simulations. Some techniques for geometry treatment, new burnup algorithm, source convergence acceleration, massive tally and parallel calculation, and temperature dependent cross sections processing are researched and implemented in RMC to improve the effciency. Validation results of criticality calculation, burnup calculation, source convergence acceleration, tallies performance and parallel performance shown in this paper prove the capabilities of RMC in dealing with reactor analysis problems with good performances.

This paper, based on a survey of manufacturing firms in south and central China regions, examines the relationship among green supply chain management, competitive advantage and firm performance. By factor analyses, the scale of manufacturing firm’s green supply chain management is obtained. The results show that green supply chain management can be conceptualized as a four dimensional variable: green manufacturing, green purchasing, green distribution and green logistics. Furthermore, after testing research hypotheses by LISREL, we find that these four constructs all have significant effects on competitive advantage, and that green manufacturing and green logistics have significant effects on firm performance. The implications for our findings are also presented.

No abstract

No abstract