MnCoGe-based compounds undergo a giant negative thermal expansion (NTE) during the martensitic structural transition from Ni2In-type hexagonal to TiNiSi-type orthorhombic structure. High-resolution neutron diffraction experiments revealed that the expansion of unit cell volume can be as large as ΔV/V ∼ 3.9%. The optimized compositions with concurrent magnetic and structural transitions have been studied for magnetocaloric effect. However, these materials have not been considered as NTE materials partially due to the limited temperature window of phase transition. The as-prepared MnCoGe-based compounds are quite brittle and naturally collapse into powders. By using a few percents (3-4%) of epoxy to bond the powders, we introduced residual stress in the bonded samples and thus realized the broadening of structural transition by utilizing the specific characteristics of lattice softening enforced by the stress. As a result, giant NTE (not only the linear NTE coefficient α but also the operation-temperature window) has been achieved. For example, the average α̅ as much as -51.5 × 10(-6)/K with an operating temperature window as wide as 210 K from 122 to 332 K has been observed in a bonded MnCo0.98Cr0.02Ge compound. Moreover, in the region between 250 and 305 K near room temperature, the α value (-119 × 10(-6)/K) remains nearly independent of temperature. Such an excellent performance exceeds that of most other materials reported previously, suggesting it can potentially be used as a NTE material, particularly for compensating the materials with large positive thermal expansions.

One major theory in learning and memory posits that the NR2B gene is a universal genetic factor that acts as rate-limiting molecule in controlling the optimal NMDA receptor's coincidence-detection property and subsequent learning and memory function across multiple animal species. If so, can memory function be enhanced via transgenic overexpression of NR2B in another species other than the previously reported mouse species? To examine these crucial issues, we generated transgenic rats in which NR2B is overexpressed in the cortex and hippocampus and investigated the role of NR2B gene in NMDA receptor-mediated synaptic plasticity and memory functions by combining electrophysiological technique with behavioral measurements. We found that overexpression of the NR2B subunit had no effect on CA1-LTD, but rather resulted in enhanced CA1-LTP and improved memory performances in novel object recognition test, spatial water maze, and delayed-to-nonmatch working memory test. Our slices recordings using NR2A- and NR2B-selective antagonists further demonstrate that the larger LTP in transgenic hippocampal slices was due to contribution from the increased NR2B-containing NMDARs. Therefore, our genetic experiments suggest that NR2B at CA1 synapses is not designated as a rate-limiting factor for the induction of long-term synaptic depression, but rather plays a crucial role in initiating the synaptic potentiation. Moreover, our studies provide strong evidence that the NR2B subunit represents a universal rate-limiting molecule for gating NMDA receptor's optimal coincidence-detection property and for enhancing memory function in adulthood across multiple mammalian species.

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Tractography based on Diffusion tensor imaging (DTI) data has been used as a tool by a large number of recent studies to investigate structural connectome. Despite its great success in offering unique 3D neuroanatomy information, DTI is an indirect observation with limited resolution and accuracy and its reliability is still unclear. Thus, it is essential to answer this fundamental question: how reliable is DTI tractography in constructing large-scale connectome? To answer this question, we employed neuron tracing data of 1772 experiments in the mouse brain released by the Allen Mouse Brain Connectivity Atlas (AMCA) as the ground-truth to assess the performance of DTI tractography in inferring white matter fiber pathways and inter-regional connections. For the first time in the neuroimaging field, the performance of whole brain DTI tractography in constructing large-scale connectome has been evaluated by comparison with tracing data. Our results suggested that only with the optimized tractography parameters and the appropriate scale of brain parcellation scheme, DTI can produce relatively reliable fiber pathways and large-scale connectome. Meanwhile, a considerable amount of errors were also identified in optimized DTI tractography results, which we believe could be potentially alleviated by effort in developing better DTI tractography approaches. In this scenario, our framework could serve as a reliable and quantitative test bed to identify errors in tractography results which will facilitate the development of such novel tractography algorithms and the selection of optimal parameters.