Nonpolar resistive switching in Ag@TiO2 core-shell nanowires

Nonpolar resistive switching (RS), a combination of bipolar and unipolar RS, is demonstrated for the first time in a single nanowire (NW) system. Exploiting Ag@TiO2 core–shell (CS) NWs synthesized by postgrowth shell formation, the switching mode is controlled by adjusting the current compliance effectively, tailoring the electrical polarity response. We demonstrate ON/OFF ratios of 105 and 107 for bipolar and unipolar modes, respectively. In the bipolar regime, retention times could be controlled up to 103 s, and in the unipolar mode, >106 s was recorded. We show how the unique dual-mode switching behavior is enabled by the defect-rich polycrystalline material structure of the TiO2 shell and the interaction between the Ag core and the Ag electrodes. These results provide a foundation for engineering nonpolar RS behaviors for memory storage and neuromorphic applications in CSNW structures

Neuromorphic-inspired behaviour in core-shell nanowire networks

Engineering smart-materials with emergent properties requires designing and characterizing systems with desirable behaviours. Neuromorphic (brain-like) architectures require plasticity, where the strength of the connections and the time with which they decay can be modulated based on the magnitude and the repetition of the applied stimuli. This functionality is emulated in our complex nanowire network material through electrical resistive switching. The formation of nano-sized filamentary connections between overlapping wires across the network facilitates a controllable transition from a high resistance state to one (or more) lower resistance states with corresponding memory retention times. We report on the neuromorphic inspired behaviors that emerge from networks of metal nanowires coated with TiO 2 shells

Predicting Cognitive Decline in Nondemented Elders Using Baseline Metrics of AD Pathologies, Cerebrovascular Disease, and Neurodegeneration

BACKGROUND AND OBJECTIVES: Dementia is a growing socio-economic challenge that requires early intervention. Identifying biomarkers that reliably predict clinical progression early in the disease process would better aid selection of individuals for future trial participation. Here we compared the ability of baseline, single time-point biomarkers (CSF amyloid 1-42, CSF ptau-181, white matter hyperintensities (WMH), cerebral microbleeds (CMB), whole-brain volume, and hippocampal volume) to predict decline in cognitively normal individuals who later converted to mild cognitive impairment (MCI) (CNtoMCI), and those with MCI who later converted to an Alzheimer's disease (AD) diagnosis (MCItoAD). METHODS: Standardised baseline biomarker data from ADNI2/Go, and longitudinal diagnostic data (including ADNI3), were used. Cox regression models assessed biomarkers in relation to time to change in clinical diagnosis using all follow-up timepoints available. Models were fit for biomarkers univariately, and together in a multivariable model. Hazard Ratios (HR) were compared to evaluate biomarkers. Analyses were performed separately in CNtoMCI and MCItoAD groups. RESULTS: For CNtoMCI (n = 189), there was strong evidence that higher WMH volume (individual model: HR 1.79, p = .002; fully-adjusted model: HR 1.98, p = .003), and lower hippocampal volume (individual: HR 0.54, p = .001; fully-adjusted: HR 0.40, p < .001) were associated with conversion to MCI individually and independently. For MCItoAD (n = 345), lower hippocampal (individual model: HR 0.45, p < .001; fully-adjusted model: HR 0.55, p < .001) and whole-brain volume (individual: HR 0.31, p < .001; fully-adjusted: HR 0.48, p = .02), increased CSF ptau (individual: HR 1.88, p < .001; fully-adjusted: HR 1.61, p < .001), and lower CSF amyloid (individual: HR 0.37, p < .001, fully-adjusted: HR 0.62, p = .008) were most strongly associated with conversion to AD individually and independently. DISCUSSION: Lower hippocampal volume was a consistent predictor of clinical conversion to MCI and AD. CSF and brain volume biomarkers were predictive of conversion to AD from MCI, while WMH were predictive of conversion to MCI from cognitively normal. The predictive ability of WMH in the CNtoMCI group may be interpreted as some being on a different pathological pathway, such as vascular cognitive impairment

The Climate and Environment of Byzantine Anatolia: Integrating Science, History and Archaeology

The integration of high-resolution archaeological, textual, and environmental data with longer-term, low-resolution data affords greater precision in identifying some of the causal relationships underlying societal change. Regional and microregional case studies about the Byzantine world—in particular, Anatolia, which for several centuries was the heart of that world—reveal many of the difficulties that researchers face when attempting to assess the influence of environmental factors on human society. The Anatolian case challenges a number of assumptions about the impact of climatic factors on socio-political organization and medium-term historical evolution, highlighting the importance of further collaboration between historians, archaeologists, and climate scientists

Emergence of winner-takes-all connectivity paths in random nanowire networks

Nanowire networks are promising memristive architectures for neuromorphic applications due to their connectivity and neurosynaptic-like behaviours. Here, we demonstrate a self-similar scaling of the conductance of networks and the junctions that comprise them. We show this behavior is an emergent property of any junction-dominated network. A particular class of junctions naturally leads to the emergence of conductance plateaus and a “winner-takes-all” conducting path that spans the entire network, and which we show corresponds to the lowest-energy connectivity path. The memory stored in the conductance state is distributed across the network but encoded in specific connectivity pathways, similar to that found in biological systems. These results are expected to have important implications for development of neuromorphic devices based on reservoir computing

Automated White Matter Hyperintensity Segmentation Using Bayesian Model Selection: Assessment and Correlations with Cognitive Change.

Accurate, automated white matter hyperintensity (WMH) segmentations are needed for large-scale studies to understand contributions of WMH to neurological diseases. We evaluated Bayesian Model Selection (BaMoS), a hierarchical fully-unsupervised model selection framework for WMH segmentation. We compared BaMoS segmentations to semi-automated segmentations, and assessed whether they predicted longitudinal cognitive change in control, early Mild Cognitive Impairment (EMCI), late Mild Cognitive Impairment (LMCI), subjective/significant memory concern (SMC) and Alzheimer's (AD) participants. Data were downloaded from the Alzheimer's disease Neuroimaging Initiative (ADNI). Magnetic resonance images from 30 control and 30 AD participants were selected to incorporate multiple scanners, and were semi-automatically segmented by 4 raters and BaMoS. Segmentations were assessed using volume correlation, Dice score, and other spatial metrics. Linear mixed-effect models were fitted to 180 control, 107 SMC, 320 EMCI, 171 LMCI and 151 AD participants separately in each group, with the outcomes being cognitive change (e.g. mini-mental state examination; MMSE), and BaMoS WMH, age, sex, race and education used as predictors. There was a high level of agreement between BaMoS' WMH segmentation volumes and a consensus of rater segmentations, with a median Dice score of 0.74 and correlation coefficient of 0.96. BaMoS WMH predicted cognitive change in: control, EMCI, and SMC groups using MMSE; LMCI using clinical dementia rating scale; and EMCI using Alzheimer's disease assessment scale-cognitive subscale (p < 0.05, all tests). BaMoS compares well to semi-automated segmentation, is robust to different WMH loads and scanners, and can generate volumes which predict decline. BaMoS can be applicable to further large-scale studies

Differential modes of DNA binding by mismatch uracil DNA glycosylase from Escherichia coli: implications for abasic lesion processing and enzyme communication in the base excision repair pathway

Mismatch uracil DNA glycosylase (Mug) from Escherichia coli is an initiating enzyme in the base-excision repair pathway. As with other DNA glycosylases, the abasic product is potentially more harmful than the initial lesion. Since Mug is known to bind its product tightly, inhibiting enzyme turnover, understanding how Mug binds DNA is of significance when considering how Mug interacts with downstream enzymes in the base-excision repair pathway. We have demonstrated differential binding modes of Mug between its substrate and abasic DNA product using both band shift and fluorescence anisotropy assays. Mug binds its product cooperatively, and a stoichiometric analysis of DNA binding, catalytic activity and salt-dependence indicates that dimer formation is of functional significance in both catalytic activity and product binding. This is the first report of cooperativity in the uracil DNA glycosylase superfamily of enzymes, and forms the basis of product inhibition in Mug. It therefore provides a new perspective on abasic site protection and the findings are discussed in the context of downstream lesion processing and enzyme communication in the base excision repair pathway

Crystallographically controlled synthesis of SnSe nanowires: potential in resistive memory devices

Here the controlled growth of SnSe nanowires by a liquid injection chemical vapor deposition (CVD) method employing a distorted octahedral [SnCl4{n BuSe(CH2)3Sen Bu}] single‐source diselenoether precursor is reported. CVD with this single‐source precursor allows morphological and compositional control of the SnSex nanostructures formed, including the transformation of SnSe2 nanoflakes into SnSe nanowires and again to SnSe nanoflakes with increasing growth temperature. Significantly, highly crystalline SnSe nanowires with an orthorhombic Pnma 62 crystal structure can be controllably synthesized in two growth directions, either or . The ability to tune the growth direction of SnSe will have important implications for devices constructed using these nanocrystals. The SnSe nanowires with a growth direction display a reversible polarity‐dependent memory switching ability, not previously reported for nanoscale SnSe. A resistive switching on/off ratio of 103 without the use of a current compliance limit is seen, illustrating the potential use of SnSe nanowires for low‐power nonvolatile memory applications

Shifting senses in lexical semantic development

Most words are associated with multiple senses. A DVD can be round (when describing a disc), and a DVD can be an hour long (when describing a movie), and in each case DVD means something different. The possible senses of a word are often predictable, and also constrained, as words cannot take just any meaning: for example, although a movie can be an hour long, it cannot sensibly be described as round (unlike a DVD). Learning the scope and limits of word meaning is vital for the comprehension of natural language, but poses a potentially difficult learnability problem for children. By testing what senses children are willing to assign to a variety of words, we demonstrate that, in comprehension, the problem is solved using a productive learning strategy. Children are perfectly capable of assigning different senses to a word; indeed they are essentially adult-like at assigning licensed meanings. But difficulties arise in determining which senses are assignable: children systematically overestimate the possible senses of a word, allowing meanings that adults rule unlicensed (e.g., taking round movie to refer to a disc). By contrast, this strategy does not extend to production, in which children use licensed, but not unlicensed, senses. Children’s productive comprehension strategy suggests an early emerging facility for using context in sense resolution (a difficult task for natural language processing algorithms), but leaves an intriguing question as to the mechanisms children use to learn a restricted, adult-like set of senses

Predicting Cognitive Decline in Older Adults Using Baseline Metrics of AD Pathologies, Cerebrovascular Disease, and Neurodegeneration

BACKGROUND AND OBJECTIVES: Dementia is a growing socioeconomic challenge that requires early intervention. Identifying biomarkers that reliably predict clinical progression early in the disease process would better aid selection of individuals for future trial participation. Here, we compared the ability of baseline, single time-point biomarkers (CSF amyloid 1-42, CSF ptau-181, white matter hyperintensities (WMH), cerebral microbleeds, whole-brain volume, and hippocampal volume) to predict decline in cognitively normal individuals who later converted to mild cognitive impairment (MCI) (CNtoMCI) and those with MCI who later converted to an Alzheimer disease (AD) diagnosis (MCItoAD). METHODS: Standardized baseline biomarker data from AD Neuroimaging Initiative 2 (ADNI2)/GO and longitudinal diagnostic data (including ADNI3) were used. Cox regression models assessed biomarkers in relation to time to change in clinical diagnosis using all follow-up time points available. Models were fit for biomarkers univariately and together in a multivariable model. Hazard ratios (HRs) were compared to evaluate biomarkers. Analyses were performed separately in CNtoMCI and MCItoAD groups. RESULTS: For CNtoMCI (n = 189), there was strong evidence that higher WMH volume (individual model: HR 1.79, p = 0.002; fully adjusted model: HR 1.98, p = 0.003) and lower hippocampal volume (individual: HR 0.54, p = 0.001; fully adjusted: HR 0.40, p < 0.001) were associated with conversion to MCI individually and independently. For MCItoAD (n = 345), lower hippocampal (individual model: HR 0.45, p < 0.001; fully adjusted model: HR 0.55, p < 0.001) and whole-brain volume (individual: HR 0.31, p < 0.001; fully adjusted: HR 0.48, p = 0.02), increased CSF ptau (individual: HR 1.88, p < 0.001; fully adjusted: HR 1.61, p < 0.001), and lower CSF amyloid (individual: HR 0.37, p < 0.001; fully adjusted: HR 0.62, p = 0.008) were most strongly associated with conversion to AD individually and independently. DISCUSSION: Lower hippocampal volume was a consistent predictor of clinical conversion to MCI and AD. CSF and brain volume biomarkers were predictive of conversion to AD from MCI, whereas WMH were predictive of conversion to MCI from cognitively normal. The predictive ability of WMH in the CNtoMCI group may be interpreted as some being on a different pathologic pathway, such as vascular cognitive impairment