Hardware implementation of memristor-based artificial neural networks

Artificial Intelligence (AI) is currently experiencing a bloom driven by deep learning (DL) techniques, which rely on networks of connected simple computing units operating in parallel. The low communication bandwidth between memory and processing units in conventional von Neumann machines does not support the requirements of emerging applications that rely extensively on large sets of data. More recent computing paradigms, such as high parallelization and near-memory computing, help alleviate the data communication bottleneck to some extent, but paradigm- shifting concepts are required. Memristors, a novel beyond-complementary metal-oxide-semiconductor (CMOS) technology, are a promising choice for memory devices due to their unique intrinsic device-level properties, enabling both storing and computing with a small, massively-parallel footprint at low power. Theoretically, this directly translates to a major boost in energy efficiency and computational throughput, but various practical challenges remain. In this work we review the latest efforts for achieving hardware-based memristive artificial neural networks (ANNs), describing with detail the working principia of each block and the different design alternatives with their own advantages and disadvantages, as well as the tools required for accurate estimation of performance metrics. Ultimately, we aim to provide a comprehensive protocol of the materials and methods involved in memristive neural networks to those aiming to start working in this field and the experts looking for a holistic approach.

Examining the variation in consent in general surgery

INTRODUCTION

Consent is a fundamental aspect of surgery and expectations around the consent process have changed following the Montgomery vs Lanarkshire Health Board (2015) court ruling. This study aimed to identify trends in litigation pertaining to consent, explore variation in how consent is practised among general surgeons and identify potential causes of this variation.

METHODS

This mixed-methods study examined temporal variation in litigation rates relating to consent (between 2011 and 2020), using data obtained from National Health Service (NHS) Resolutions. Semi-structured clinician interviews were then conducted to gain qualitative data regarding how general surgeons take consent, their ideologies and their outlook on the recent legal changes. The quantitative component included a questionnaire survey aiming to explore these issues with a larger population to improve the generalisability of the findings.

RESULTS

NHS Resolutions litigation data showed a significant increase in litigation pertaining to consent following the 2015 health board ruling. The interviews demonstrated considerable variation in how surgeons approach consent. This was corroborated by the survey, which illustrated considerable variation in how consent is documented when different surgeons are presented with the same case vignette.

CONCLUSION

A clear increase in litigation relating to consent was seen in the post-Montgomery era, which may be due to legal precedent being established and increased awareness of these issues. Findings from this study demonstrate variability in the information patients receive. In some cases, consent practices did not adequately meet current regulations and therefore are susceptible to potential litigation. This study identifies areas for improvement in the practice of consent.

Generalized Key-Value Memory to Flexibly Adjust Redundancy in Memory-Augmented Networks

Memory-augmented neural networks enhance a neural network with an external key-value (KV) memory whose complexity is typically dominated by the number of support vectors in the key memory. We propose a generalized KV memory that decouples its dimension from the number of support vectors by introducing a free parameter that can arbitrarily add or remove redundancy to the key memory representation. In effect, it provides an additional degree of freedom to flexibly control the tradeoff between robustness and the resources required to store and compute the generalized KV memory. This is particularly useful for realizing the key memory on in-memory computing hardware where it exploits nonideal, but extremely efficient nonvolatile memory devices for dense storage and computation. Experimental results show that adapting this parameter on demand effectively mitigates up to 44% nonidealities, at equal accuracy and number of devices, without any need for neural network retraining.

Mechanism and Impact of Bipolar Current Voltage Asymmetry in Computational Phase-Change Memory

Nanoscale resistive memory devices are being explored for neuromorphic and in-memory computing. However, non-ideal device characteristics of read noise and resistance drift pose significant challenges to the achievable computational precision. Here, it is shown that there is an additional non-ideality that can impact computational precision, namely the bias-polarity-dependent current flow. Using phase-change memory (PCM) as a model system, it is shown that this "current-voltage" non-ideality arises both from the material and geometrical properties of the devices. Further, we discuss the detrimental effects of such bipolar asymmetry on in-memory matrix-vector multiply (MVM) operations and provide a scheme to compensate for it.

Hardware-aware training for large-scale and diverse deep learning inference workloads using in-memory computing-based accelerators

Analog in-memory computing-a promising approach for energy-efficient acceleration of deep learning workloads-computes matrix-vector multiplications but only approximately, due to nonidealities that often are non-deterministic or nonlinear. This can adversely impact the achievable inference accuracy. Here, we develop an hardware-aware retraining approach to systematically examine the accuracy of analog in-memory computing across multiple network topologies, and investigate sensitivity and robustness to a broad set of nonidealities. By introducing a realistic crossbar model, we improve significantly on earlier retraining approaches. We show that many larger-scale deep neural networks-including convnets, recurrent networks, and transformers-can in fact be successfully retrained to show iso-accuracy with the floating point implementation. Our results further suggest that nonidealities that add noise to the inputs or outputs, not the weights, have the largest impact on accuracy, and that recurrent networks are particularly robust to all nonidealities.

Recent Advances and Future Prospects for Memristive Materials, Devices, and Systems

Memristive technology has been rapidly emerging as a potential alternative to traditional CMOS technology, which is facing fundamental limitations in its development. Since oxide-based resistive switches were demonstrated as memristors in 2008, memristive devices have garnered significant attention due to their biomimetic memory properties, which promise to significantly improve power consumption in computing applications. Here, we provide a comprehensive overview of recent advances in memristive technology, including memristive devices, theory, algorithms, architectures, and systems. In addition, we discuss research directions for various applications of memristive technology including hardware accelerators for artificial intelligence, in-sensor computing, and probabilistic computing. Finally, we provide a forward-looking perspective on the future of memristive technology, outlining the challenges and opportunities for further research and innovation in this field. By providing an up-to-date overview of the state-of-the-art in memristive technology, this review aims to inform and inspire further research in this field.

In-memory factorization of holographic perceptual representations

Disentangling the attributes of a sensory signal is central to sensory perception and cognition and hence is a critical task for future artificial intelligence systems. Here we present a compute engine capable of efficiently factorizing high-dimensional holographic representations of combinations of such attributes, by exploiting the computation-in-superposition capability of brain-inspired hyperdimensional computing, and the intrinsic stochasticity associated with analogue in-memory computing based on nanoscale memristive devices. Such an iterative in-memory factorizer is shown to solve at least five orders of magnitude larger problems that cannot be solved otherwise, as well as substantially lowering the computational time and space complexity. We present a large-scale experimental demonstration of the factorizer by employing two in-memory compute chips based on phase-change memristive devices. The dominant matrix-vector multiplication operations take a constant time, irrespective of the size of the matrix, thus reducing the computational time complexity to merely the number of iterations. Moreover, we experimentally demonstrate the ability to reliably and efficiently factorize visual perceptual representations.

An integrated photonics engine for unsupervised correlation detection

With more and more aspects of modern life and scientific tools becoming digitized, the amount of data being generated is growing exponentially. Fast and efficient statistical processing, such as identifying correlations in big datasets, is therefore becoming increasingly important, and this, on account of the various compute bottlenecks in modern digital machines, has necessitated new computational paradigms. Here, we demonstrate one such novel paradigm, via the development of an integrated phase-change photonics engine. The computational memory engine exploits the accumulative property of Ge₂Sb₂Te₅ phase-change cells and wavelength division multiplexing property of optics in delivering fully parallelized and colocated temporal correlation detection computations. We investigate this property and present an experimental demonstration of identifying real-time correlations in data streams on the social media platform Twitter and high-traffic computing nodes in data centers. Our results demonstrate the use case of high-speed integrated photonics in accelerating statistical analysis methods.

Memristive technologies for data storage, computation, encryption, and radio-frequency communication

Memristive devices, which combine a resistor with memory functions such that voltage pulses can change their resistance (and hence their memory state) in a nonvolatile manner, are beginning to be implemented in integrated circuits for memory applications. However, memristive devices could have applications in many other technologies, such as non-von Neumann in-memory computing in crossbar arrays, random number generation for data security, and radio-frequency switches for mobile communications. Progress toward the integration of memristive devices in commercial solid-state electronic circuits and other potential applications will depend on performance and reliability challenges that still need to be addressed, as described here.

POS0818 SOUTHEND PRE-TEST PROBABILITY SCORE AND HALO SCORE AS MARKERS FOR DIAGNOSIS AND MONITORING OF GCA: EARLY RESULTS FROM THE PROSPECTIVE HAS-GCA STUDY

Background

Ultrasound (US) is recommended as the first line imaging test in patients with suspected Giant Cell Arteritis (GCA). Traditionally, the US halo sign has been used for diagnosis. We have recently described a composite Halo Score that allows to quantify vascular inflammation on US. Prospective studies on response and disease monitoring are lacking.

Objectives

To prospectively assess the role of US in diagnosing and monitoring GCA patients. We report early baseline and 12-month data on our current recruitment in a study that has suffered disruption from the pandemic.

Methods

HAS GCA (IRAS#264294) is an ongoing, prospective, multicentre study recruiting from referrals of suspected GCA to fast-track clinics. Based on the Southend GCA clinical pre-test probability score (SPTPS)1, patients were stratified in to low, intermediate and high risk categories2. Temporal and axillary US Halo Scores were calculated from the halo thickness and extent in bilateral temporal arteries, parietal and frontal branches (TAHS) and axillary arteries (AAHS). These scores were summed (TAHS x1 plus; AAHS x3) to generate a Total Halo Score (THS)3.

Mann Whitney U test was used to compare baseline features between GCA and controls. Wilcoxon signed rank test was used to evaluate disease features at baseline and at 12 months in GCA patients. Sensitivity (Sn), Specificity (Sp) and ROC curve were calculated, where applicable. P value <0.05 is statistically significant

Results

202 patients (71 GCA, 131 controls) have been recruited thus far: 23 completed 12-month follow up assessment; 6 were lost to follow up (4 died, 2 withdrew consent due to pandemic). Demographics, clinical features, and US results are shown (Table 1).

Table 1.

Baseline features of GCA patients and controls

GCA (n=71)Controls (n=131)P-valueAge, median (IQR)75 (70-81)68 (62-76)0.001Female, n (%)38 (54)89 (68)0.05SPTPS category, n (%) Low risk0 (0)59 (45)<0.001 Intermediate risk16 (23)49 (37)0.04 High risk55 (77)23 (18)<0.001Halo score (HS), median (range) Temporal artery HS12 (0-22)2 (0-17)<0.0001 Axillary artery HS12 (0-21)6 (0-18)<0.0001 Total HS21 (2-40)8 (0-29)<0.0001Clinical features, n (%) Temporal headache53 (75)93 (71)0.62 Scalp tenderness36 (51)40 (31)0.006 Jaw claudication38 (54)9 (7)<0.001 PMR symptoms29 (41)35 (27)0.06 Constitutional symptoms42 (59)29 (22)<0.001 Visual disturbance40 (56)58 (44)0.11 Vision loss21 (30)9 (7)<0.001

AA, axillary artery; GCA, Giant cell arteritis; TA, Temporal artery

Among GCA patients, 50 had cranial, 5 large-vessel and 16 mixed phenotypes. Diseases were diagnosed by US and additional tests such as PET CT.

Jaw claudication (54%) and constitutional symptoms (59%) were the dominant features in GCA patients. Median age was 75 years in GCA (54% females) and 68 years in controls (68% females). GCA and controls were stratified by SPTPS to Low risk (0% vs 45%; Sn-undefined, Sp-98), Intermediate risk (23% vs 37%; Sn-81, Sp-98) and High risk (77% vs 18%; Sn-98, Sp-91). Optimal SPTPS cut-off point was ≥12 (Sn-89, Sp-76).

Median THS was 21 in GCA and 8 in controls. Optimal cut-off Halo Score in diagnosis was TAHS ≥5 (Sn-89, Sp-86), AAHS ≥11 (Sn-55, Sp-75), THS ≥15 (Sn-79%, Sp-86%). Baseline Halo Score and CRP levels showed positive correlation (spearman rank correlation). Among the 23 patients who completed 12-months follow up, median TAHS, AAHS and THS reduced from 12 to 2, 12 to 6 and 21 to 10, respectively (Figure 1).

Conclusion

Along with SPTPS, Halo Score successfully discriminates GCA from non GCA mimics and. HS is effective in showing 12-month response. This score may be a useful marker to monitor GCA disease activity

References

[1]Laskou F et al. Clin Exp Rheumatol. 2019

[2]Sebastian A et al. RMD Open. 2020

[3]Sebastian A et al. BMC Rheumatol. 2020

Disclosure of Interests

Alwin Sebastian: None declared, Alessandro Tomelleri: None declared, Pierluigi Macchioni: None declared, Giulia Klinowski: None declared, Carlo Salvarani: None declared, Abdul Kayani: None declared, Mohammad Tariq: None declared, Diana Prieto-Peña: None declared, Edoardo Conticini: None declared, Muhammad Khurshid: None declared, Sue Inness: None declared, Jo Jackson: None declared, Kornelis van der Geest Speakers bureau: Roche, Grant/research support from: Mandema stipend, Bhaskar Dasgupta: None declared

Structural Assessment of Interfaces in Projected Phase-Change Memory

Non-volatile memories based on phase-change materials have gained ground for applications in analog in-memory computing. Nonetheless, non-idealities inherent to the material result in device resistance variations that impair the achievable numerical precision. Projected-type phase-change memory devices reduce these non-idealities. In a projected phase-change memory, the phase-change storage mechanism is decoupled from the information retrieval process by using projection of the phase-change material’s phase configuration onto a projection liner. It has been suggested that the interface resistance between the phase-change material and the projection liner is an important parameter that dictates the efficacy of the projection. In this work, we establish a metrology framework to assess and understand the relevant structural properties of the interfaces in thin films contained in projected memory devices. Using X-ray reflectivity, X-ray diffraction and transmission electron microscopy, we investigate the quality of the interfaces and the layers’ properties. Using demonstrator examples of Sb and Sb₂Te₃ phase-change materials, new deposition routes as well as stack designs are proposed to enhance the phase-change material to a projection-liner interface and the robustness of material stacks in the devices.

Phase-change memtransistive synapses for mixed-plasticity neural computations

In the mammalian nervous system, various synaptic plasticity rules act, either individually or synergistically, over wide-ranging timescales to enable learning and memory formation. Hence, in neuromorphic computing platforms, there is a significant need for artificial synapses that can faithfully express such multi-timescale plasticity mechanisms. Although some plasticity rules have been emulated with elaborate complementary metal oxide semiconductor and memristive circuitry, device-level hardware realizations of long-term and short-term plasticity with tunable dynamics are lacking. Here we introduce a phase-change memtransistive synapse that leverages both the non-volatility of the phase configurations and the volatility of field-effect modulation for implementing tunable plasticities. We show that these mixed-plasticity synapses can enable plasticity rules such as short-term spike-timing-dependent plasticity that helps with the modelling of dynamic environments. Further, we demonstrate the efficacy of the memtransistive synapses in realizing accelerators for Hopfield neural networks for solving combinatorial optimization problems.

Experimental validation of state equations and dynamic route maps for phase change memristive devices

Phase Change Memory (PCM) is an emerging technology exploiting the rapid and reversible phase transition of certain chalcogenides to realize nanoscale memory elements. PCM devices are being explored as non-volatile storage-class memory and as computing elements for in-memory and neuromorphic computing. It is well-known that PCM exhibits several characteristics of a memristive device. In this work, based on the essential physical attributes of PCM devices, we exploit the concept of Dynamic Route Map (DRM) to capture the complex physics underlying these devices to describe them as memristive devices defined by a state-dependent Ohm's law. The efficacy of the DRM has been proven by comparing numerical results with experimental data obtained on PCM devices.

Accelerating Inference of Convolutional Neural Networks Using In-memory Computing

In-memory computing (IMC) is a non-von Neumann paradigm that has recently established itself as a promising approach for energy-efficient, high throughput hardware for deep learning applications. One prominent application of IMC is that of performing matrix-vector multiplication in O(1) time complexity by mapping the synaptic weights of a neural-network layer to the devices of an IMC core. However, because of the significantly different pattern of execution compared to previous computational paradigms, IMC requires a rethinking of the architectural design choices made when designing deep-learning hardware. In this work, we focus on application-specific, IMC hardware for inference of Convolution Neural Networks (CNNs), and provide methodologies for implementing the various architectural components of the IMC core. Specifically, we present methods for mapping synaptic weights and activations on the memory structures and give evidence of the various trade-offs therein, such as the one between on-chip memory requirements and execution latency. Lastly, we show how to employ these methods to implement a pipelined dataflow that offers throughput and latency beyond state-of-the-art for image classification tasks.

POS0337 SOUTHEND PRE-TEST PROBABILITY SCORE AND HALO SCORE AS MARKERS FOR DIAGNOSIS AND MONITORING OF GCA: EARLY RESULTS FROM THE PROSPECTIVE HAS-GCA STUDY

Background:

EULAR recommends doppler ultrasound (US) as the first line imaging in patients with Giant Cell Arteritis (GCA) suspect. Traditionally, US non-compressive halo sign has been used for diagnosis but prospective studies on response and disease monitoring are lacking

Objectives:

The HAS GCA study has the objective of prospectively assessing role of US in diagnosis, prognosis and monitoring in newly diagnosed GCA. We report early baseline and up to month 3 data on our current recruitment in a study that has suffered disruption from the pandemic

Methods:

HAS GCA (IRAS#264294) is an ongoing, prospective, multicentre study recruiting from referrals of suspected GCA to fast track clinics. The objective is to recruit 270 patients, including 68 GCA patients. Based on the Southend GCA clinical pre-test probability score (SPTPS)1, patients were stratified in to low, intermediate and high risk categories2. Temporal and axillary US Halo Scores were calculated from the halo thickness and extent in bilateral temporal arteries, parietal and frontal branches and axillary arteries. These individual scores were summed (TA Halo Score x1 plus; AA Halo Score x3) to generate a Total Halo Score (THS)3.

Mann Whitney U test and Fisher’s exact test were used to compare baseline features between GCA and controls. Wilcoxon signed rank test was used to evaluate disease features at baseline and at 3 months in GCA patients. Sensitivity (Sn) and Specificity (Sp) were calculated, where applicable. P value <0.05 is statistically significant

Results:

Ninety-three patients (29 GCA, 64 controls) have been recruited thus far: 18 completed 3-month follow up assessment; 4 were lost to follow up (2 died, 2 withdrew consent due to pandemic). Demographics, clinical features, and US results are shown (Table 1).

Table 1.

Baseline features of GCA patients and controls.

GCA (n=29)Controls (n=64)P-valueAge, median (IQR)75 (71-80)67 (61.25 – 75.0)0.001Female, n (%)15 (42)50 (78)0.01SPTPS category, n (%) Low risk0 (0)31 (48)<0.001 Intermediate risk7 (24)25 (39)0.24 High risk22 (76)8 (13)<0.001Halo score (HS), median (range) Temporal artery HS10 (1-21)1 (0-9)<0.001 Axillary artery HS12 (0-18)6 (0-18)<0.001 Total HS21 (2-38)6 (0-19)<0.001Clinical features, n (%) Temporal headache21 (72)40 (63)0.48 Scalp tenderness17 (59)31 (48)0.38 Jaw claudication19 (66)4 (6)<0.001 PMR symptoms16 (55)6 (9)<0.001 Constitutional symptoms17 (59)18 (28)0.006 Visual disturbance18 (62)38 (59)1 Vision loss7 (24)4 (6)0.03

Among GCA patients, 23 had cranial, 2 large-vessel and 4 mixed phenotypes (cranial plus large vessel) disease.

Jaw claudication (66%) and polymyalgic symptoms (55%) were the dominant features in GCA patients. Median age 75 years in GCA (42% females) and 67 years in controls (78% females). GCA and controls were stratified by SPTPS to Low risk (0% vs 48%; Sn-undefined, Sp-97), Intermediate risk (24% vs 39%; Sn-100, Sp-100) and High risk (76% vs 13%; Sn-95, Sp-88). Optimal SPTPS cut-off point was ≥12 (Sn-93, Sp-86); ≥10 (Sn-100 & Sp-69).

Median THS was 21 in GCA and 6 in controls. Optimal cut-off Halo Score in diagnosis was TAHS ≥5 (Sn-90, Sp-98), AAHS ≥11 (Sn-55, Sp-80), THS ≥18 (Sn-72%, Sp-98%). Among the 18 patients who completed 3-months follow up, median TAHS, AAHS and THS reduced from 10 to 2.5, 12 to 6 and 21 to 10, respectively (Figure 1).

Conclusion:

Along with SPTPS, Halo Score successfully discriminates GCA from non GCA mimics. HS is effective in showing 3-month response and may be a useful marker to monitor GCA disease activity.

References:

[1]Laskou F et al. A probability score to aid the diagnosis of suspected giant cell arteritis. Clin Exp Rheumatol. 2019

[2]Sebastian A et al. Probability-based algorithm using ultrasound and additional tests for suspected GCA in a fast-track clinic. RMD Open. 2020

[3]Sebastian A et al. Halo score (temporal artery, its branches and axillary artery) as a diagnostic, prognostic and disease monitoring tool for Giant Cell Arteritis (GCA). BMC Rheumatol. 2020

Disclosure of Interests:

Alwin Sebastian: None declared, Alessandro Tomelleri: None declared, Abdul Kayani: None declared, Mohammad Tariq: None declared, Diana Prieto-Peña: None declared, Sue Inness: None declared, Jo Jackson: None declared, Kornelis van der Geest Speakers bureau: Roche, Bhaskar Dasgupta Speakers bureau: Roche, GSK, BMS, Sanofi, Abbie, Grant/research support from: Roche

Publisher Correction: Memory devices and applications for in-memory computing

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Memory devices and applications for in-memory computing

Traditional von Neumann computing systems involve separate processing and memory units. However, data movement is costly in terms of time and energy and this problem is aggravated by the recent explosive growth in highly data-centric applications related to artificial intelligence. This calls for a radical departure from the traditional systems and one such non-von Neumann computational approach is in-memory computing. Hereby certain computational tasks are performed in place in the memory itself by exploiting the physical attributes of the memory devices. Both charge-based and resistance-based memory devices are being explored for in-memory computing. In this Review, we provide a broad overview of the key computational primitives enabled by these memory devices as well as their applications spanning scientific computing, signal processing, optimization, machine learning, deep learning and stochastic computing.

SAT0249 A PROBABILITY-BASED DIAGNOSTIC ALGORITHM FOR SUSPECTED GCA

Background:

Clinical presentation of GCA is protean. It is vital to make a secure diagnosis, exclude mimics urgently and avoid inappropriate steroids to minimise side effects. Fast track GCA clinics (FTC) provide rapid specialist assessment with temporal and axillary US (1). EULAR recommendations support US as first-choice test. A pre-test probability score (PTPS) stratifies patients to low (LC), intermediate (IC) and high-risk (HC) categories.

Objectives:

To validate a diagnostic GCA algorithm based on stratification by PTPS, with sequential US and additional tests (AT), if necessary

Methods:

For the algorithm (Figure) retrospective data was extracted from case records of cases seen in 2019. PTPS overall showed median (Q2) score of 9,75thpercentile (Q3) score 12. Based on this and reported cut-off 9.5 (2) we classified LC as PTPS <9, IC 9-12 and HC >12 (Graph). GCA diagnosis was by modified GiACTA including US (Halo), CRP > 5 mg/L and AT if necessary. The algorithm performance was assessed overall and in individual categories.

Results:

Of 187 consecutive cases, 13 were excluded for incomplete data (tertiary referrals). In remaining 174, GCA confirmed 33%, mean age 72.4 years, 69% females,45% LC, 35% IC, and 20% HC. 130 (75%) had US whereas 44 did not (41 LC, 3 IC) (Figure)

In HC, 25/31 (81%) were US +ve, 19 treated as GCA without AT, 6 with AT (Table 2). Of 6 US -ve 3 had GCA confirmed by AT (PET-CT 2, TAB 1). US in HC showed sensitivity 89%, specificity 75%, accuracy 87%, GCA prevalence 87%, mean CRP 65.52 (SEM+/- 8.67).

Table 1.

US performance with PTPS

Category(n)USGCA, nNon-GCA, nSensitivity (%)Specificity (%)PPV (%)NPV(%)Prevalence (%)Accuracy(%)HC (31)+24124/27(89)3/4(75)24/25(96)3/6(50)27/31(87)(24 + 3)/31(87)-33IC (65)+30030/30(100)35/35(100)30/30(100)35/35(100)30/65(46)(30 + 35)/65(100)-035LC (78)+010/0 (undefined)77/78(99)0/1(0)77/77(100)0/78(0)(0 + 77)/78(99)-077Total (174)+54254/57(95)115/117(98)54/56(96)115/118(97)57/174(33)(54 + 115)/174(97)-3115

Abbreviations: GCA, Giant cell arteritis; NPV, Negative predictive value; PPV, Positive predictive value; US, Ultrasound

Table 2.

US, AT & confirmed diagnosis

CategoryUltrasoundNo of ATType of ATFinal Diagnosis+veNot done-veLC(78)1393871x TAB (-), CTB (-)Fibromyalgia1x TAB (-), MRA (-), MR neck (+)Tongue cancer1x CTA (+)Stroke1x CTCAP (-)IA1x PET (-)PMR1xTAB (-)NA AION1x PET (-)CVAIC(65)30332155x TAB (-), 2x PET (-)Not GCA2x TAB (+), 6x PET (+)GCAHC(31)2506101x PET (-)URTI1x TAB (-)NAAION2x PET (+)1x TAB (+)1x CTA (+)1x MRA (+)GCA1x PET (-)2x CTA (-)1x CTCAP (-)

Abbreviations: AT, Additional test; CTA, Computed tomography angiogram; CTB, Computed tomography of brain; CTCAP, Computed tomography of chest, abdomen and pelvis; GCA, Giant cell arteritis; IA, Inflammatory arthritis; MRA, Magnetic resonant angiogram; NA AION, Non arteritic anterior ischemic optic neuritis; PET, Position emission tomography; TAB, Temporal artery biopsy; URTI, Upper respiratory tract infection

In LC, 38 (49%) were US - ve, of whom 5 had AT. US not done on 39 (50%) for either PTPS very low or urgent alternative diagnosis. 1 went on to AT. 1 was US positive and had GCA excluded with AT. US in LC showed specificity 99%, sensitivity 0/0 (undefined), accuracy 99%, GCA prevalence 0%, mean CRP 21.79 (SEM+/- 3.80)

In IC, 30/65 (46%) were US +ve 8 had AT (all GCA confirmed) while on treatment. 32 (49%) US negative where 7 had AT (all GCA excluded). 3 did not have US. Sensitivity, specificity, accuracy of US was all 100%, GCA prevalence 46%, mean CRP 39.05 (SEM+/- 5.04)

US test performance overall sensitivity 95%, specificity 98%, accuracy 97%

Conclusion:

PTPS successfully stratifies GCA, excludes mimics and enhances US performance. The algorithm interprets correctly US findings and choice of AT.

References:

[1]Patil et al Clin Exp Rheumatol 2015;33(Suppl 89): S103–6.

[2]Laskou et al. Clin Exp Rheumatol. 2019 Feb 15

Disclosure of Interests:

Alwin Sebastian: None declared, Abdul Kayani: None declared, Chavini Ranasinghe: None declared, Bhaskar Dasgupta Grant/research support from: Roche, Consultant of: Roche, Sanofi, GSK, BMS, AbbVie, Speakers bureau: Roche

State dependence and temporal evolution of resistance in projected phase change memory

Phase change memory (PCM) is being actively explored for in-memory computing and neuromorphic systems. The ability of a PCM device to store a continuum of resistance values can be exploited to realize arithmetic operations such as matrix-vector multiplications or to realize the synaptic efficacy in neural networks. However, the resistance variations arising from structural relaxation, 1/f noise, and changes in ambient temperature pose a key challenge. The recently proposed projected PCM concept helps to mitigate these resistance variations by decoupling the physical mechanism of resistance storage from the information-retrieval process. Even though the device concept has been proven successfully, a comprehensive understanding of the device behavior is still lacking. Here, we develop a device model that captures two key attributes, namely, resistance drift and the state dependence of resistance. The former refers to the temporal evolution of resistance, while the latter refers to the dependence of the device resistance on the phase configuration of the phase change material. The study provides significant insights into the role of interfacial resistance in these devices. The model is experimentally validated on projected PCM devices based on antimony and a metal nitride fabricated in a lateral device geometry and is also used to provide guidelines for material selection and device engineering.

Accurate deep neural network inference using computational phase-change memory

In-memory computing using resistive memory devices is a promising non-von Neumann approach for making energy-efficient deep learning inference hardware. However, due to device variability and noise, the network needs to be trained in a specific way so that transferring the digitally trained weights to the analog resistive memory devices will not result in significant loss of accuracy. Here, we introduce a methodology to train ResNet-type convolutional neural networks that results in no appreciable accuracy loss when transferring weights to phase-change memory (PCM) devices. We also propose a compensation technique that exploits the batch normalization parameters to improve the accuracy retention over time. We achieve a classification accuracy of 93.7% on CIFAR-10 and a top-1 accuracy of 71.6% on ImageNet benchmarks after mapping the trained weights to PCM. Our hardware results on CIFAR-10 with ResNet-32 demonstrate an accuracy above 93.5% retained over a one-day period, where each of the 361,722 synaptic weights is programmed on just two PCM devices organized in a differential configuration.

Experimental Demonstration of Supervised Learning in Spiking Neural Networks with Phase-Change Memory Synapses

Spiking neural networks (SNN) are computational models inspired by the brain’s ability to naturally encode and process information in the time domain. The added temporal dimension is believed to render them more computationally efficient than the conventional artificial neural networks, though their full computational capabilities are yet to be explored. Recently, in-memory computing architectures based on non-volatile memory crossbar arrays have shown great promise to implement parallel computations in artificial and spiking neural networks. In this work, we evaluate the feasibility to realize high-performance event-driven in-situ supervised learning systems using nanoscale and stochastic analog memory synapses. For the first time, the potential of analog memory synapses to generate precisely timed spikes in SNNs is experimentally demonstrated. The experiment targets applications which directly integrates spike encoded signals generated from bio-mimetic sensors with in-memory computing based learning systems to generate precisely timed control signal spikes for neuromorphic actuators. More than 170,000 phase-change memory (PCM) based synapses from our prototype chip were trained based on an event-driven learning rule, to generate spike patterns with more than 85% of the spikes within a 25 ms tolerance interval in a 1250 ms long spike pattern. We observe that the accuracy is mainly limited by the imprecision related to device programming and temporal drift of conductance values. We show that an array level scaling scheme can significantly improve the retention of the trained SNN states in the presence of conductance drift in the PCM. Combining the computational potential of supervised SNNs with the parallel compute power of in-memory computing, this work paves the way for next-generation of efficient brain-inspired systems.

Mixed-Precision Deep Learning Based on Computational Memory

Deep neural networks (DNNs) have revolutionized the field of artificial intelligence and have achieved unprecedented success in cognitive tasks such as image and speech recognition. Training of large DNNs, however, is computationally intensive and this has motivated the search for novel computing architectures targeting this application. A computational memory unit with nanoscale resistive memory devices organized in crossbar arrays could store the synaptic weights in their conductance states and perform the expensive weighted summations in place in a non-von Neumann manner. However, updating the conductance states in a reliable manner during the weight update process is a fundamental challenge that limits the training accuracy of such an implementation. Here, we propose a mixed-precision architecture that combines a computational memory unit performing the weighted summations and imprecise conductance updates with a digital processing unit that accumulates the weight updates in high precision. A combined hardware/software training experiment of a multilayer perceptron based on the proposed architecture using a phase-change memory (PCM) array achieves 97.73% test accuracy on the task of classifying handwritten digits (based on the MNIST dataset), within 0.6% of the software baseline. The architecture is further evaluated using accurate behavioral models of PCM on a wide class of networks, namely convolutional neural networks, long-short-term-memory networks, and generative-adversarial networks. Accuracies comparable to those of floating-point implementations are achieved without being constrained by the non-idealities associated with the PCM devices. A system-level study demonstrates 172 × improvement in energy efficiency of the architecture when used for training a multilayer perceptron compared with a dedicated fully digital 32-bit implementation.

Osteogenic preconditioning in perfusion bioreactors improves vascularization and bone formation by human bone marrow aspirates

Cell-derived extracellular matrix (ECM) provides a niche to promote osteogenic differentiation, cell adhesion, survival, and trophic factor secretion. To determine whether osteogenic preconditioning would improve the bone-forming potential of unfractionated bone marrow aspirate (BMA), we perfused cells on ECM-coated scaffolds to generate naïve and preconditioned constructs, respectively. The composition of cells selected from BMA was distinct on each scaffold. Naïve constructs exhibited robust proangiogenic potential in vitro, while preconditioned scaffolds contained more mesenchymal stem/stromal cells (MSCs) and endothelial cells (ECs) and exhibited an osteogenic phenotype. Upon implantation into an orthotopic calvarial defect, BMA-derived ECs were present in vessels in preconditioned implants, resulting in robust perfusion and greater vessel density over the first 14 days compared to naïve implants. After 10 weeks, human ECs and differentiated MSCs were detected in de novo tissues derived from naïve and preconditioned scaffolds. These results demonstrate that bioreactor-based preconditioning augments the bone-forming potential of BMA.

In-memory computing on a photonic platform

Collocated data processing and storage are the norm in biological computing systems such as the mammalian brain. As our ability to create better hardware improves, new computational paradigms are being explored beyond von Neumann architectures. Integrated photonic circuits are an attractive solution for on-chip computing which can leverage the increased speed and bandwidth potential of the optical domain, and importantly, remove the need for electro-optical conversions. Here we show that we can combine integrated optics with collocated data storage and processing to enable all-photonic in-memory computations. By employing nonvolatile photonic elements based on the phase-change material, Ge₂Sb₂Te₅, we achieve direct scalar and matrix-vector multiplication, featuring a novel single-shot Write/Erase and a drift-free process. The output pulse, carrying the information of the light-matter interaction, is the result of the computation. Our all-optical approach is novel, easy to fabricate and operate, and sets the stage for development of entirely photonic computers.

Monatomic phase change memory

Phase change memory has been developed into a mature technology capable of storing information in a fast and non-volatile way^1-3, with potential for neuromorphic computing applications^4-6. However, its future impact in electronics depends crucially on how the materials at the core of this technology adapt to the requirements arising from continued scaling towards higher device densities. A common strategy to fine-tune the properties of phase change memory materials, reaching reasonable thermal stability in optical data storage, relies on mixing precise amounts of different dopants, resulting often in quaternary or even more complicated compounds^6-8. Here we show how the simplest material imaginable, a single element (in this case, antimony), can become a valid alternative when confined in extremely small volumes. This compositional simplification eliminates problems related to unwanted deviations from the optimized stoichiometry in the switching volume, which become increasingly pressing when devices are aggressively miniaturized^9,10. Removing compositional optimization issues may allow one to capitalize on nanosize effects in information storage.

Neuromorphic computing with multi-memristive synapses

Neuromorphic computing has emerged as a promising avenue towards building the next generation of intelligent computing systems. It has been proposed that memristive devices, which exhibit history-dependent conductivity modulation, could efficiently represent the synaptic weights in artificial neural networks. However, precise modulation of the device conductance over a wide dynamic range, necessary to maintain high network accuracy, is proving to be challenging. To address this, we present a multi-memristive synaptic architecture with an efficient global counter-based arbitration scheme. We focus on phase change memory devices, develop a comprehensive model and demonstrate via simulations the effectiveness of the concept for both spiking and non-spiking neural networks. Moreover, we present experimental results involving over a million phase change memory devices for unsupervised learning of temporal correlations using a spiking neural network. The work presents a significant step towards the realization of large-scale and energy-efficient neuromorphic computing systems.

Memristive technology is a promising avenue towards realizing efficient non-von Neumann neuromorphic hardware. Boybat et al. proposes a multi-memristive synaptic architecture with a counter-based global arbitration scheme to address challenges associated with the non-ideal memristive device behavior.

Signal and noise extraction from analog memory elements for neuromorphic computing

Dense crossbar arrays of non-volatile memory (NVM) can potentially enable massively parallel and highly energy-efficient neuromorphic computing systems. The key requirements for the NVM elements are continuous (analog-like) conductance tuning capability and switching symmetry with acceptable noise levels. However, most NVM devices show non-linear and asymmetric switching behaviors. Such non-linear behaviors render separation of signal and noise extremely difficult with conventional characterization techniques. In this study, we establish a practical methodology based on Gaussian process regression to address this issue. The methodology is agnostic to switching mechanisms and applicable to various NVM devices. We show tradeoff between switching symmetry and signal-to-noise ratio for HfO₂-based resistive random access memory. Then, we characterize 1000 phase-change memory devices based on Ge₂Sb₂Te₅ and separate total variability into device-to-device variability and inherent randomness from individual devices. These results highlight the usefulness of our methodology to realize ideal NVM devices for neuromorphic computing.

Memristive effects in oxygenated amorphous carbon nanodevices

Computing with resistive-switching (memristive) memory devices has shown much recent progress and offers an attractive route to circumvent the von-Neumann bottleneck, i.e. the separation of processing and memory, which limits the performance of conventional computer architectures. Due to their good scalability and nanosecond switching speeds, carbon-based resistive-switching memory devices could play an important role in this respect. However, devices based on elemental carbon, such as tetrahedral amorphous carbon or ta-C, typically suffer from a low cycling endurance. A material that has proven to be capable of combining the advantages of elemental carbon-based memories with simple fabrication methods and good endurance performance for binary memory applications is oxygenated amorphous carbon, or a-CO _x . Here, we examine the memristive capabilities of nanoscale a-CO _x devices, in particular their ability to provide the multilevel and accumulation properties that underpin computing type applications. We show the successful operation of nanoscale a-CO _x memory cells for both the storage of multilevel states (here 3-level) and for the provision of an arithmetic accumulator. We implement a base-16, or hexadecimal, accumulator and show how such a device can carry out hexadecimal arithmetic and simultaneously store the computed result in the self-same a-CO _x cell, all using fast (sub-10 ns) and low-energy (sub-pJ) input pulses.

Temporal correlation detection using computational phase-change memory

Conventional computers based on the von Neumann architecture perform computation by repeatedly transferring data between their physically separated processing and memory units. As computation becomes increasingly data centric and the scalability limits in terms of performance and power are being reached, alternative computing paradigms with collocated computation and storage are actively being sought. A fascinating such approach is that of computational memory where the physics of nanoscale memory devices are used to perform certain computational tasks within the memory unit in a non-von Neumann manner. We present an experimental demonstration using one million phase change memory devices organized to perform a high-level computational primitive by exploiting the crystallization dynamics. Its result is imprinted in the conductance states of the memory devices. The results of using such a computational memory for processing real-world data sets show that this co-existence of computation and storage at the nanometer scale could enable ultra-dense, low-power, and massively-parallel computing systems.

Probing the micromechanics of the fastest growing plant cell - the pollen tube

The pollen tube is a fast growing cellular protrusion that plays a key role in the reproductive process of flowering plants. It serves as an important model for studying cellular morphogenesis, anisotropic growth mechanisms, and cellular signaling in the plant sciences. The anisotropic growth of pollen tubes is driven by a finely tuned control of the intracellular turgor pressure and the extensibility of the cell wall. To decipher this internal feedback loop and mathematically model the growth process, a quantitative understanding of the mechanical properties of the cell wall is crucial, in addition to biochemical investigations. We report an integrated microfluidic-MEMS force sensor system that allows for high-throughput optical and mechanical investigations of pollen tubes. The system permits large-scale germination, growth, and optical phenotyping of pollen tubes empowering rapid micro-indentation measurements on these cells.

Massively Parallelized Pollen Tube Guidance and Mechanical Measurements on a Lab-on-a-Chip Platform

Pollen tubes are used as a model in the study of plant morphogenesis, cellular differentiation, cell wall biochemistry, biomechanics, and intra- and intercellular signaling. For a "systems-understanding" of the bio-chemo-mechanics of tip-polarized growth in pollen tubes, the need for a versatile, experimental assay platform for quantitative data collection and analysis is critical. We introduce a Lab-on-a-Chip (LoC) concept for high-throughput pollen germination and pollen tube guidance for parallelized optical and mechanical measurements. The LoC localizes a large number of growing pollen tubes on a single plane of focus with unidirectional tip-growth, enabling high-resolution quantitative microscopy. This species-independent LoC platform can be integrated with micro-/nano-indentation systems, such as the cellular force microscope (CFM) or the atomic force microscope (AFM), allowing for rapid measurements of cell wall stiffness of growing tubes. As a demonstrative example, we show the growth and directional guidance of hundreds of lily (Lilium longiflorum) and Arabidopsis (Arabidopsis thaliana) pollen tubes on a single LoC microscopy slide. Combining the LoC with the CFM, we characterized the cell wall stiffness of lily pollen tubes. Using the stiffness statistics and finite-element-method (FEM)-based approaches, we computed an effective range of the linear elastic moduli of the cell wall spanning the variability space of physiological parameters including internal turgor, cell wall thickness, and tube diameter. We propose the LoC device as a versatile and high-throughput phenomics platform for plant reproductive and development biology using the pollen tube as a model.

Iron plaque decreases cadmium accumulation in Oryza sativa L. and serves as a source of iron

Cadmium (Cd) contamination occurs in paddy soils; hence it is necessary to reduce Cd content of rice. Application and mode of action of ferrous sulphate in minimizing Cd in rice was monitored in the present study. Pot culture with Indian rice variety Swarna (MTU 7029) was maintained in Cd-spiked soil containing ferrous sulphates, which is expected to reduce Cd accumulation in rice. Responses in rhizosphere pH, root surface, metal accumulation in plant and molecular physiological processes were monitored. Iron plaque was induced on root surfaces after FeSO4 application and the amount of Fe in plaque reduced with increases in Cd in the soil. Rhizosphere pH decreased during plaque formation and became more acidic due to secretion of organic acids from the roots under Cd treatment. Moreover, iron chelate reductase activity increased with Cd treatment, but in the absence of Cd, activity of this enzyme increased in plaque-induced plants. Cd treatment caused expression of OsYSL18, whereas OsYSL15 was expressed only in roots without iron plaque. Fe content of plants increased during plaque formation, which protected plants from Cd-induced Fe deficiency and metal toxicity. This was corroborated with increased biomass, chlorophyll content and quantum efficiency of photo-synthesis among plaque-induced plants. We conclude that ferrous sulphate-induced iron plaque prevents Cd accumulation and Fe deficiency in rice. Iron released from plaque via organic acid mediated dissolution during Cd stress.

De novo transcriptome assembly facilitates characterisation of fast-evolving gene families, MHC class I in the bank vole (Myodes glareolus)

The major histocompatibility complex (MHC) plays a central role in the adaptive immune response and is the most polymorphic gene family in vertebrates. Although high-throughput sequencing has increasingly been used for genotyping families of co-amplifying MHC genes, its potential to facilitate early steps in the characterisation of MHC variation in nonmodel organism has not been fully explored. In this study we evaluated the usefulness of de novo transcriptome assembly in characterisation of MHC sequence diversity. We found that although de novo transcriptome assembly of MHC I genes does not reconstruct sequences of individual alleles, it does allow the identification of conserved regions for PCR primer design. Using the newly designed primers, we characterised MHC I sequences in the bank vole. Phylogenetic analysis of the partial MHC I coding sequence (2-4 exons) of the bank vole revealed a lack of orthology to MHC I of other Cricetidae, consistent with the high gene turnover of this region. The diversity of expressed alleles was characterised using ultra-deep sequencing of the third exon that codes for the peptide-binding region of the MHC molecule. High allelic diversity was demonstrated, with 72 alleles found in 29 individuals. Interindividual variation in the number of expressed loci was found, with the number of alleles per individual ranging from 5 to 14. Strong signatures of positive selection were found for 8 amino acid sites, most of which are inferred to bind antigens in human MHC, indicating conservation of structure despite rapid sequence evolution.

Liver Transplantation in a Patient at Psychosocial Risk

Psychosocial assessment of transplant candidates is a challenging task. Securing adequate information is made more difficult when patients present with fulminant hepatic failure. When the patient cannot be interviewed and the family is reluctant to provide vital information, a comprehensive pretransplant psychosocial evaluation is virtually impossible. However, even the most difficult cases have the potential for a positive result when a good psychosocial profile of the patient is obtained after transplantation, a team treatment plan is developed and carried out which addresses current and anticipated problems, and the patient obtains mental health treatment.

Stochastic phase-change neurons

Artificial neuromorphic systems based on populations of spiking neurons are an indispensable tool in understanding the human brain and in constructing neuromimetic computational systems. To reach areal and power efficiencies comparable to those seen in biological systems, electroionics-based and phase-change-based memristive devices have been explored as nanoscale counterparts of synapses. However, progress on scalable realizations of neurons has so far been limited. Here, we show that chalcogenide-based phase-change materials can be used to create an artificial neuron in which the membrane potential is represented by the phase configuration of the nanoscale phase-change device. By exploiting the physics of reversible amorphous-to-crystal phase transitions, we show that the temporal integration of postsynaptic potentials can be achieved on a nanosecond timescale. Moreover, we show that this is inherently stochastic because of the melt-quench-induced reconfiguration of the atomic structure occurring when the neuron is reset. We demonstrate the use of these phase-change neurons, and their populations, in the detection of temporal correlations in parallel data streams and in sub-Nyquist representation of high-bandwidth signals.

Oxygenated amorphous carbon for resistive memory applications

Carbon-based electronics is a promising alternative to traditional silicon-based electronics as it could enable faster, smaller and cheaper transistors, interconnects and memory devices. However, the development of carbon-based memory devices has been hampered either by the complex fabrication methods of crystalline carbon allotropes or by poor performance. Here we present an oxygenated amorphous carbon (a-COx) produced by physical vapour deposition that has several properties in common with graphite oxide. Moreover, its simple fabrication method ensures excellent reproducibility and tuning of its properties. Memory devices based on a-COx exhibit outstanding non-volatile resistive memory performance, such as switching times on the order of 10 ns and cycling endurance in excess of 10(4) times. A detailed investigation of the pristine, SET and RESET states indicates a switching mechanism based on the electrochemical redox reaction of carbon. These results suggest that a-COx could play a key role in non-volatile memory technology and carbon-based electronics.

Projected phase-change memory devices

Nanoscale memory devices, whose resistance depends on the history of the electric signals applied, could become critical building blocks in new computing paradigms, such as brain-inspired computing and memcomputing. However, there are key challenges to overcome, such as the high programming power required, noise and resistance drift. Here, to address these, we present the concept of a projected memory device, whose distinguishing feature is that the physical mechanism of resistance storage is decoupled from the information-retrieval process. We designed and fabricated projected memory devices based on the phase-change storage mechanism and convincingly demonstrate the concept through detailed experimentation, supported by extensive modelling and finite-element simulations. The projected memory devices exhibit remarkably low drift and excellent noise performance. We also demonstrate active control and customization of the programming characteristics of the device that reliably realize a multitude of resistance states.

Endogenous event-related potentials in patients with primary Sjögren's syndrome without central nervous system involvement

OBJECTIVES

Endogenous cognitive event-related potentials (CERPs) reflect higher-level processing of sensory information and can be used to evaluate cognitive functions. The aim of this paper was to determine whether there are any abnormalities in the electrophysiological parameters of CERPs in patients with primary Sjögren's syndrome (pSS) but without symptoms of central nervous system (CNS) involvement or mental disorder. The analysis of CERP parameters was then correlated with the clinical status of the patients and with some of the immunological parameters in the patient group.

METHOD

Thirty consecutive patients with pSS (29 females, one male) were included in the study. All the patients underwent CERP examination.

RESULTS

There was a significant prolongation of the latency of P300 and N200 potentials in patients with pSS. Abnormalities in electrophysiological parameters of CERPs correlated with the duration of the disease, salivary gland abnormalities, and elevated erythrocyte sedimentation rate (ESR) values. Patients with coexisting chronic fatigue syndrome (CFS) had larger P300 amplitudes. There were no statistically significant changes in the electrophysiological parameters of CERPs in patients with pSS dependent on the presence of peripheral nervous system (PNS) lesions, skin changes, arthritis, abnormalities in white blood cells and the immune system or the levels of blood lipids.

CONCLUSIONS

The results of the study suggest the presence of a minor cognitive dysfunction in patients with pSS without symptoms of CNS involvement or mental disorder. Cognitive dysfunction correlated with the disease duration time and the severity of inflammatory changes (salivary gland abnormalities and inflammatory markers in the blood). Further and larger longitudinal studies are necessary for confirmation of this correlation.

Arterialized venous bicarbonate is associated with lower bone mineral density and an increased rate of bone loss in older men and women

CONTEXT

Higher dietary net acid loads have been associated with increased bone resorption, reduced bone mineral density (BMD), and increased fracture risk.

OBJECTIVE

The objective was to compare bicarbonate (HCO3) measured in arterialized venous blood samples to skeletal outcomes.

DESIGN

Arterialized venous samples collected from participants in the Health, Aging and Body Composition (Health ABC) Study were compared to BMD and rate of bone loss.

SETTING

The setting was a community-based observational cohort.

PARTICIPANTS

A total of 2287 men and women age 74 ± 3 years participated.

INTERVENTION

Arterialized venous blood was obtained at the year 3 study visit and analyzed for pH and pCO2. HCO3 was determined using the Henderson-Hasselbalch equation.

MAIN OUTCOME MEASURE

BMD was measured at the hip by dual-energy x-ray absorptiometry at the year 1 (baseline) and year 3 study visits.

RESULTS

Plasma HCO3 was positively associated with BMD at both year 1 (P = .001) and year 3 (P = .001) in models adjusted for age, race, sex, clinic site, smoking, weight, and estimated glomerular filtration rate. Plasma HCO3 was inversely associated with rate of bone loss at the total hip over the 2.1 ± 0.3 (mean ± SD) years between the two bone density measurements (P < .001). Across quartiles of plasma HCO3, the rate of change in BMD over the 2.1 years ranged from a loss of 0.72%/y in the lowest quartile to a gain of 0.15%/y in the highest quartile of HCO3.

CONCLUSIONS

Arterialized plasma HCO3 was associated positively with cross-sectional BMD and inversely with the rate of bone loss, implying that systemic acid-base status is an important determinant of skeletal health during aging. Ongoing bone loss was linearly related to arterialized HCO3, even after adjustment for age and renal function. Further research in this area may have major public health implications because reducing dietary net acid load is possible through dietary intervention or through supplementation with alkaline potassium compounds.

A high-bandwidth spintronic position sensor

Position sensing with resolution down to the scale of a single atom is of key importance in nanoscale science and engineering. However, only optical-sensing methods are currently capable of non-contact sensing at such resolution over a high bandwidth. Here, we report a new non-contact, non-optical position-sensing concept based on detecting changes in a high-gradient magnetic field of a microscale magnetic dipole by means of spintronic sensors. Experimental measurements show a sensitivity of up to 40 Ω/μm, a linear range greater than 10 μm and a noise floor of 0.5 pm/√[Hz]. Also shown is the use of the sensor for position measurements for closed-loop control of a high-speed atomic force microscope with a frame rate of more than 1 frame/s.