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Seizure detection and seizure-type classification are best performed using intra-cranial or full-scalp electroencephalogram (EEG). In embedded wearable systems however, recordings from only a few electrodes are available, reducing the spatial resolution of the signals to a handful of timeseries at most. Taking this constraint into account, we tested the performance of multiple classifiers using a subset of the EEG recordings by selecting a single trace from the montage or performing a dimensionality reduction over each hemispherical space. Our results support that Random Forest (RF) classifiers lead most ef-ficient and stable classification performances over Support Vector Machines (SVM). Interestingly, tracking the feature importances using permutation tests reveals that classical EEG spectrum power bands display different rankings across the classifiers: low frequencies (delta, theta) are most important for SVMs while higher frequencies (alpha, gamma) are more relevant for RF and Decision Trees. We reach up to 94.3% ∓ 5.3% accuracy in classifying absence from tonic-clonic seizures using state-of-art sampling methods for unbalanced datasets and leave-patients-out fold cross-validation policy.

Epilepsy is a common neurological disorder characterized by recurrent epileptic seizures. These seizures have different intensities and might lead to accidents or, in the worst case, to sudden death. Therefore, being able to predict epileptic seizures would allow patients to be prepared, reducing the risk of injury. This paper focuses on epileptic seizure prediction using EEG (Electroencephalogram) signals. In contrast to the standard approach where the preictal state is assumed to have a constant duration in all the seizures of a patient, we propose a new method that labels each seizure individually exploiting clustering. Our labeling approach, which was applicable for 38% of the selected seizures, results in substantial improvements compared to the standard one. In fact, it reduces noise in the labels and improves the performance of the binary classifier used to distinguish the interictal and preictal states. Hence, our results suggest that the preictal duration is seizure-specific, not only patient-specific. Finally, we show that our method is able to predict 17 out of 18 (94%) seizures between 15 and 85 minutes, before seizure onset.

Artificial intelligence (AI) is certainly not a panacea for solving the ‘Pharma Dilemma’, in which the cost of producing new drugs continues to spiral. However, AI can be used to fundamentally change the way we perform essential steps in clinical trial design and execution, from cohort selection to patient monitoring. Merging AI and clinical expertise across engineering and medical disciplines to explore the impact of these changes on trial performance and success rates is one of the most promising leads we have for restoring efficiency and sustainability to the drug development cycle.

Background Assistive automatic seizure detection can empower human annotators to shorten patient monitoring data review times. We present a proof-of-concept for a seizure detection system that is sensitive, automated, patient-specific, and tunable to maximise sensitivity while minimizing human annotation times. The system uses custom data preparation methods, deep learning analytics and electroencephalography (EEG) data. Methods Scalp EEG data of 365 patients containing 171,745 s ictal and 2,185,864 s interictal samples obtained from clinical monitoring systems were analysed as part of a crowdsourced artificial intelligence (AI) challenge. Participants were tasked to develop an ictal/interictal classifier with high sensitivity and low false alarm rates. We built a challenge platform that prevented participants from downloading or directly accessing the data while allowing crowdsourced model development. Findings The automatic detection system achieved tunable sensitivities between 75.00% and 91.60% allowing a reduction in the amount of raw EEG data to be reviewed by a human annotator by factors between 142x, and 22x respectively. The algorithm enables instantaneous reviewer-managed optimization of the balance between sensitivity and the amount of raw EEG data to be reviewed. Interpretation This study demonstrates the utility of deep learning for patient-specific seizure detection in EEG data. Furthermore, deep learning in combination with a human reviewer can provide the basis for an assistive data labelling system lowering the time of manual review while maintaining human expert annotation performance. Funding IBM employed all IBM Research authors. Temple University employed all Temple University authors. The Icahn School of Medicine at Mount Sinai employed Eren Ahsen. The corresponding authors Stefan Harrer and Gustavo Stolovitzky declare that they had full access to all the data in the study and that they had final responsibility for the decision to submit for publication.

Wearable sensors are being used in clinical settings to monitor the condition of patients as well as in recreational environments for routine health monitoring. Some of the most advanced clinical applications include monitoring patients with Parkinson's disease through wearable inertial measuring units (IMUs) and patients with diabetes by means of wearable glucose sensors. Prominent examples of wearable sensors in routine use are fitness trackers, step-and calorie counters. Recently, wearables have evolved to being capable of running artificial intelligence algorithms in real-time at the point of sensing which allows to gain analytical insights directly from measurement data. We call such intelligent wearables with AI-at-the-edge functionality THINKables. First use cases for THINKables have emerged in both clinical and nonclinical applications: real-time seizure prediction or detection systems for epilepsy patients, or digital coaches providing real-time feedback to athletes on performance and injury risks. Technological and regulatory challenges of developing and deploying THINKables are multifold: data privacy and security of monitoring data needs to be ensured at all times, analytical AI models need to be transparent, explainable and fair, and all these features need to be implemented taking the limited computing power of point-of-sensing processors into account. In order for THINKables to become integrated into clinical workflows, all stakeholders in the Health AI ecosystem (regulators, clinicians, biomedical device technologists, pharma and biotech sectors, data scientists, and patients) need to work together to create frameworks for responsible and meaningful use.

This paper presents a novel deep learning enabled, video based analysis framework for assessing the Unified Parkinsons Disease Rating Scale (UPDRS) that can be used in the clinic or at home. We report results from comparing the performance of the framework to that of trained clinicians on a population of 32 Parkinsons disease (PD) patients. In-person clinical assessments by trained neurologists are used as the ground truth for training our framework and for comparing the performance. We find that the standard sit-to-stand activity can be used to evaluate the UPDRS sub-scores of bradykinesia (BRADY) and posture instability and gait disorders (PIGD). For BRADY we find F1-scores of 0.75 using our framework compared to 0.50 for the video based rater clinicians, while for PIGD we find 0.78 for the framework and 0.45 for the video based rater clinicians. We believe our proposed framework has potential to provide clinically acceptable end points of PD in greater granularity without imposing burdens on patients and clinicians, which empowers a variety of use cases such as passive tracking of PD progression in spaces such as nursing homes, in-home self-assessment, and enhanced tele-medicine., Comment: Accepted by CVPR Workshops 2021

Artificial intelligence (AI) technologies have advanced to a level of maturity that allows them to be employed under real-life conditions to assist human decision-makers. AI has the potential to transform key steps of clinical trial design from study preparation to execution towards improving trial success rates, thus lowering the pharma R&D burden. Suboptimal patient cohort selection and recruiting techniques, paired with the inability to monitor patients effectively during trials, are two of the main causes for high trial failure rates: only one of 10 compounds entering a clinical trial reaches the market. This session will explain in layman's terms some of the foundations of AI methodology, such as Machine Learning and Deep Learning, highlighting how recent advances can be applied at specific stages of the clinical trial design process to improve cohort composition, patient recruitment, medication compliance and patient retention. A special focus will be given to describing how patients in neurology trials could be monitored more efficiently through Digital Disease Diaries, which use wearable devices, machine learning at the edge and cloud technology to automatically detect and log disease episodes and patient adherence to trial protocols. Like all technical revolutions, this comes with challenges and risks, both technical and regulatory. In particular, we will discuss scalability, data encryption and patient privacy.

Background Seizure prediction can increase independence and allow preventative treatment for patients with epilepsy. We present a proof-of-concept for a seizure prediction system that is accurate, fully automated, patient-specific, and tunable to an individual's needs. Methods Intracranial electroencephalography (iEEG) data of ten patients obtained from a seizure advisory system were analyzed as part of a pseudoprospective seizure prediction study. First, a deep learning classifier was trained to distinguish between preictal and interictal signals. Second, classifier performance was tested on held-out iEEG data from all patients and benchmarked against the performance of a random predictor. Third, the prediction system was tuned so sensitivity or time in warning could be prioritized by the patient. Finally, a demonstration of the feasibility of deployment of the prediction system onto an ultra-low power neuromorphic chip for autonomous operation on a wearable device is provided. Results The prediction system achieved mean sensitivity of 69% and mean time in warning of 27%, significantly surpassing an equivalent random predictor for all patients by 42%. Conclusion This study demonstrates that deep learning in combination with neuromorphic hardware can provide the basis for a wearable, real-time, always-on, patient-specific seizure warning system with low power consumption and reliable long-term performance., Highlights • We use deep learning and long-term neural data to develop an automated, patient-tunable epileptic seizure prediction system. • We deploy our prediction system on a low-power neuromorphic chip to form the basis of a wearable device. Predicting and treating the debilitating seizures suffered by epileptic patients has challenged medical researchers for over fifty years. A new way forward was opened when Cook and colleagues, in 2013, collected a large longitudinal and continuous dataset recorded directly from patients' brains for one to three years. Harnessing the recent breakthroughs in deep learning techniques and in building specialized processing chips, we have demonstrated that seizures can now be predicted by a portable device. Our system automatically learns patient-specific pre-seizure signatures, and, in real time, warns of oncoming seizures.

Automatic classification of epileptic seizure types in electroencephalograms (EEGs) data can enable more precise diagnosis and efficient management of the disease. This task is challenging due to factors such as low signal-to-noise ratios, signal artefacts, high variance in seizure semiology among epileptic patients, and limited availability of clinical data. To overcome these challenges, in this paper, we present SeizureNet, a deep learning framework which learns multi-spectral feature embeddings using an ensemble architecture for cross-patient seizure type classification. We used the recently released TUH EEG Seizure Corpus (V1.4.0 and V1.5.2) to evaluate the performance of SeizureNet. Experiments show that SeizureNet can reach a weighted F1 score of up to 0.95 for seizure-wise cross validation and 0.62 for patient-wise cross validation for scalp EEG based multi-class seizure type classification. We also show that the high-level feature embeddings learnt by SeizureNet considerably improve the accuracy of smaller networks through knowledge distillation for applications with low-memory constraints.

Accurate classification of seizure types plays a crucial role in the treatment and disease management of epileptic patients. Epileptic seizure types not only impact the choice of drugs but also the range of activities a patient can safely engage in. With recent advances being made towards artificial intelligence enabled automatic seizure detection, the next frontier is the automatic classification of seizure types. On that note, in this paper, we explore the application of machine learning algorithms for multi-class seizure type classification. We used the recently released TUH EEG seizure corpus (V1.4.0 and V1.5.2) and conducted a thorough search space exploration to evaluate the performance of a combination of various pre-processing techniques, machine learning algorithms, and corresponding hyperparameters on this task. We show that our algorithms can reach a weighted $F1$ score of up to 0.901 for seizure-wise cross validation and 0.561 for patient-wise cross validation thereby setting a benchmark for scalp EEG based multi-class seizure type classification., 5 pages, 2 figure, 4 table

Neuroethics is central to the Australian Brain Initiative’s aim to sustain a thriving and responsible neurotechnology industry. Diverse and inclusive community and stakeholder engagement and a trans-disciplinary approach to neuroethics will be key to the success of the Australian Brain Initiative. Refereed/Peer-reviewed

Brain-related disorders such as epilepsy can be diagnosed by analyzing electroencephalograms (EEG). However, manual analysis of EEG data requires highly trained clinicians, and is a procedure that is known to have relatively low inter-rater agreement (IRA). Moreover, the volume of the data and the rate at which new data becomes available make manual interpretation a time-consuming, resource-hungry, and expensive process. In contrast, automated analysis of EEG data offers the potential to improve the quality of patient care by shortening the time to diagnosis and reducing manual error. In this paper, we focus on one of the first steps in interpreting an EEG session - identifying whether the brain activity is abnormal or normal. To address this specific task, we propose a novel recurrent neural network (RNN) architecture termed ChronoNet which is inspired by recent developments from the field of image classification and designed to work efficiently with EEG data. ChronoNet is formed by stacking multiple 1D convolution layers followed by deep gated recurrent unit (GRU) layers where each 1D convolution layer uses multiple filters of exponentially varying lengths and the stacked GRU layers are densely connected in a feed-forward manner. We used the recently released TUH Abnormal EEG Corpus dataset for evaluating the performance of ChronoNet. Unlike previous studies using this dataset, ChronoNet directly takes time-series EEG as input and learns meaningful representations of brain activity patterns. ChronoNet outperforms previously reported results on this dataset thereby setting a new benchmark.

Importance: Mammography screening currently relies on subjective human interpretation. Artificial intelligence (AI) advances could be used to increase mammography screening accuracy by reducing missed cancers and false positives. Objective: To evaluate whether AI can overcome human mammography interpretation limitations with a rigorous, unbiased evaluation of machine learning algorithms. Design, Setting, and Participants: In this diagnostic accuracy study conducted between September 2016 and November 2017, an international, crowdsourced challenge was hosted to foster AI algorithm development focused on interpreting screening mammography. More than 1100 participants comprising 126 teams from 44 countries participated. Analysis began November 18, 2016. Main Outcomes and Measurements: Algorithms used images alone (challenge 1) or combined images, previous examinations (if available), and clinical and demographic risk factor data (challenge 2) and output a score that translated to cancer yes/no within 12 months. Algorithm accuracy for breast cancer detection was evaluated using area under the curve and algorithm specificity compared with radiologists' specificity with radiologists' sensitivity set at 85.9% (United States) and 83.9% (Sweden). An ensemble method aggregating top-performing AI algorithms and radiologists' recall assessment was developed and evaluated. Results: Overall, 144 231 screening mammograms from 85 580 US women (952 cancer positive ≤12 months from screening) were used for algorithm training and validation. A second independent validation cohort included 166 578 examinations from 68 008 Swedish women (780 cancer positive). The top-performing algorithm achieved an area under the curve of 0.858 (United States) and 0.903 (Sweden) and 66.2% (United States) and 81.2% (Sweden) specificity at the radiologists' sensitivity, lower than community-practice radiologists' specificity of 90.5% (United States) and 98.5% (Sweden). Combining top-performing algorithms and US radiologist assessments resulted in a higher area under the curve of 0.942 and achieved a significantly improved specificity (92.0%) at the same sensitivity. Conclusions and Relevance: While no single AI algorithm outperformed radiologists, an ensemble of AI algorithms combined with radiologist assessment in a single-reader screening environment improved overall accuracy. This study underscores the potential of using machine learning methods for enhancing mammography screening interpretation. CA extern

Clinical trials consume the latter half of the 10 to 15 year, 1.5–2.0 billion USD, development cycle for bringing a single new drug to market. Hence, a failed trial sinks not only the investment into the trial itself but also the preclinical development costs, rendering the loss per failed clinical trial at 800 million to 1.4 billion USD. Suboptimal patient cohort selection and recruiting techniques, paired with the inability to monitor patients effectively during trials, are two of the main causes for high trial failure rates: only one of 10 compounds entering a clinical trial reaches the market. We explain how recent advances in artificial intelligence (AI) can be used to reshape key steps of clinical trial design towards increasing trial success rates.

In hospitals, physicians diagnose brain-related disorders such as epilepsy by analyzing electroencephalograms (EEG). However, manual analysis of EEG data requires highly trained clinicians or neurophysiologists and is a procedure that is known to have relatively low inter-rater agreement (IRA). Moreover, the volume of the data and rate at which new data is acquired makes interpretation a time-consuming, resource hungry, and expensive process. In contrast, automated analysis offers the potential to improve the quality of patient care by shortening the time to diagnosis, reducing manual error, and automatically detecting debilitating events. In this paper, we focus on one of the early decisions made in this process which is identifying whether an EEG session is normal or abnormal. Unlike previous approaches, we do not extract hand-engineered features but employ deep neural networks that automatically learn meaningful representations. We undertake a holistic study by exploring various pre-processing techniques and machine learning algorithms for addressing this problem and compare their performance. We have used the recently released “TUH Abnormal EEG Corpus” dataset for evaluating the performance of these algorithms. We show that modern deep gated recurrent neural networks achieve 3.47% better performance than previously reported results.

Motor imagery (MI) based Brain-Computer Interfaces (BCIs) are a viable option for giving locked-in syndrome patients independence and communicability. BCIs comprising expensive medical-grade EEG systems evaluated in carefully-controlled, artificial environments are impractical for take-home use. Previous studies evaluated low-cost systems; however, performance was suboptimal or inconclusive. Here we evaluated a low-cost EEG system, OpenBCI, in a natural environment and leveraged neurofeedback, deep learning, and wider temporal windows to improve performance. $\mu-$rhythm data collected over the sensorimotor cortex from healthy participants performing relaxation and right-handed MI tasks were used to train a multi-layer perceptron binary classifier using deep learning. We showed that our method outperforms previous OpenBCI MI-based BCIs, thereby extending the BCI capabilities of this low-cost device.

Recent research on grasp detection has focused on improving accuracy through deep CNN models, but at the cost of large memory and computational resources. In this paper, we propose an efficient CNN architecture which produces high grasp detection accuracy in real-time while maintaining a compact model design. To achieve this, we introduce a CNN architecture termed GraspNet which has two main branches: i) An encoder branch which downsamples an input image using our novel Dilated Dense Fire (DDF) modules - squeeze and dilated convolutions with dense residual connections. ii) A decoder branch which upsamples the output of the encoder branch to the original image size using deconvolutions and fuse connections. We evaluated GraspNet for grasp detection using offline datasets and a real-world robotic grasping setup. In experiments, we show that GraspNet achieves superior grasp detection accuracy compared to the stateof-the-art computation-efficient CNN models with real-time inference speed on embedded GPU hardware (Nvidia Jetson TX1), making it suitable for low-powered devices.

Brain-computer interfaces are commonly proposed to assist individuals with locked-in syndrome to interact with the world around them. In this paper, we present a pipeline to move from recorded brain signals to real-time classification on a low-power platform, such as IBM's TrueNorth Neurosynaptic System. Our results on a EEG-based hand squeeze task show that using a convolutional neural network and a time preserving signal representation strategy provides a good balance between high accuracy and feasibility in a real-time application. This pathway can be adapted to the management of a variety of conditions, including spinal cord injury, epilepsy and Parkinson's disease.

© 2019 Elsevier Inc. Neuroethics is central to the Australian Brain Initiative's aim to sustain a thriving and responsible neurotechnology industry. Diverse and inclusive community and stakeholder engagement and a trans-disciplinary approach to neuroethics will be key to the success of the Australian Brain Initiative.

Deep learning technology is uniquely suited to analyse neurophysiological signals such as the electroencephalogram (EEG) and local field potentials (LFP) and promises to outperform traditional machine-learning based classification and feature extraction algorithms. Furthermore, novel cognitive computing platforms such as IBM's recently introduced neuromorphic TrueNorth chip allow for deploying deep learning techniques in an ultra-low power environment with a minimum device footprint. Merging deep learning and TrueNorth technologies for real-time analysis of brain-activity data at the point of sensing will create the next generation of wearables at the intersection of neurobionics and artificial intelligence.

The DNA-Transistor is a device designed to control the translocation of single-stranded DNA through a solid-state nanopore. Functionality of the device is enabled by three electrodes exposed to the DNA-containing electrolyte solution within the pore and the application of a dynamic electrostatic potential well between the electrodes to temporarily trap a DNA molecule. Optimizing the surface chemistry and electrochemical behavior of the device is a necessary (but by no means sufficient) step toward the development of a functional device. In particular, effects to be eliminated are (i) electrochemically induced surface alteration through corrosion or reduction of the electrode surface and (ii) formation of hydrogen or oxygen bubbles inside the pore through water decomposition. Even though our motivation is to solve problems encountered in DNA transistor technology, in this paper we report on generic surface chemistry results. We investigated a variety of electrode-electrolyte-solvent systems with respect to their capability of suppressing water decomposition and maintaining surface integrity. We employed cyclic voltammetry and long-term amperometry as electrochemical test schemes, X-ray photoelectron spectroscopy, atomic force microscopy, and scanning, as well as transmission electron microscopy as analytical tools. Characterized electrode materials include thin films of Ru, Pt, nonstoichiometric TiN, and nonstoichiometric TiN carrying a custom-developed titanium oxide layer, as well as custom-oxidized nonstoichiometric TiN coated with a monolayer of hexadecylphosphonic acid (HDPA). We used distilled water as well as aqueous solutions of poly(ethylene glycol) (PEG-300) and glycerol as solvents. One millimolar KCl was employed as electrolyte in all solutions. Our results show that the HDPA-coated custom-developed titanium oxide layer effectively passivates the underlying TiN layer, eliminating any surface alterations through corrosion or reduction within a voltage window from -2 V to +2 V. Furthermore, we demonstrated that, by coating the custom-oxidized TiN samples with HDPA and increasing the concentration of PEG-300 or glycerol in aqueous 1 mM KCl solutions, water decomposition was suppressed within the same voltage window. Water dissociation was not detected when combining custom-oxidized HDPA-coated TiN electrodes with an aqueous 1 mM KCl-glycerol solution at a glycerol concentration of at least 90%. These results are applicable to any system that requires nanoelectrodes placed in aqueous solution at voltages that can activate electrochemical processes.

We report novel strategies to integrate block copolymer self-assembly with 193 nm water immersion lithography. These strategies employ commercially available positive tone chemically amplified photoresists to spatially encode directing information into precise topographical or chemical prepatterns for the directed self-assembly of block copolymers. Each of these methods exploits the advantageous solubility and thermal properties of polarity-switched positive tone photoresist materials. Precisely registered, sublithographic self-assembled structures are fabricated using these versatile integration schemes which are fully compatible with current optical lithography patterning materials, processes, and tooling.

We have demonstrated direct nanoscale transfer printing (nTP) of PdAu lines from a hard mold onto a hard substrate at room temperature without employing any flexible buffer layers or organic adhesion promoters or release agent layers. PdAu was evaporated onto the mold surface, and a Ti layer was deposited on top of the PdAu layer. By pressing the mold against a Si/SiO2 substrate, the PdAu/Ti sandwich structure was directly transferred onto the SiO2 surface. The molds used in these experiments were GaAs/AlGaAs sandwich structures fabricated by molecular beam epitaxy that we cleaved and selectively etched afterwards in order to generate 3-D grating structures with nanometer resolution on their edges. We fabricated positive multiline molds with different aspect ratios, linewidths between 15 and 100 nm, and spacings between lines ranging from 5 to 70 nm. We also fabricated negative single-line molds with a positive supporting structure comprising a single 16-nm-wide groove feature. The experiments revealed that direct hard-on-hard transfer of nanoscale structures from a mold onto a substrate can be used to fabricate PdAu gaps with widths down to 9 nm. We also performed electronic measurements on transfer patterns and demonstrated that transferred structures can be used as electrodes, which are electrically isolated by these gaps. Since isolation characteristics of gaps improved with decreasing gap length, we partitioned longer gap segments into multiple shorter ones by focused ion beam lithography and conventional optical lithography in combination with wet chemical or plasma etching of the mold or the substrate, respectively. In this paper, we give a detailed description of all technological aspects of the developed direct nTP technique, including mold preparation, patterning efficiency, short reduction techniques, and yield.

We have demonstrated single-step room temperature nanoimprint lithography (RTNIL) using polystyrene (PS, average molecular weights ranging from 13 to 97 kg/mol) as the imprint polymer layer on a silicon substrate for imprinting rectangular line patterns with varying aspect ratios, ranging from 11 to 500 nm wide. To accomplish this demonstration, we designed and built a tool that controllably pressed a mold into a stationary imprint sample applying imprint pressures between 280 and 700 MPa. The molds used in these experiments were GaAs/AlGaAs sandwich structures fabricated by molecular beam epitaxy (MBE) that we cleaved and selectively etched afterward in order to generate 3-D grating structures with nanometer resolution on their edges. We fabricated positive and negative molds comprising single- line as well as multiline patterns with different aspect ratios and linewidths between 9 and 300 nm.

We have demonstrated multistep room-temperature nanoimprint lithography (RTNIL) using polystyrene (PS, average molecular weight 97 kg/mol) as the imprint polymer layer on a silicon substrate for imprinting complex patterns. Single, double, and multiple (up to ten) sequential imprint steps were performed at imprint pressures between 1 to 30 MPa in separate experiments. We also transferred the imprinted patterns from the PS layer into the silicon substrate by means of an reactive-ion etching (RIE) process. To accomplish this demonstration, we designed and built a tool that controllably and repeatedly translated and pressed a sample into a stationary mold. The demonstrated interstep alignment accuracy of this tool ranged between 80 nm and 380 nm. These experiments revealed that polymer deformation results when nanoimprint is used to further deform a previously structured surface. The molds used in these experiments consisted of 400-nm-period diffraction gratings, as well as of rectangular structures of varying aspect ratios, ranging from 150 to 300 nm wide.

The first industrial revolution focused on machines, the second one was data-centric - a third revolution combining the power of devices and information has just started and transforms our understanding of life itself. Thereby novel sensors and networks from wearable biometric devices to lab-on-a-chip platforms for exploratory fundamental research on single-biomolecule characterization and design occupy a key role. In combination with recent advances in big data analytics for life sciences, healthcare and genomics such sensors are essential tools for moving from fast and cheap personalized DNA-sequencing via smart genomics towards one-off prevention and treatment plans. Replacing state-of-the-art, one-fits-all approaches, this paradigm shifting individual "assess & response" scheme commonly referred to as precision medicine merges biomedical engineering, systems biology, systems genomics, and information technology. Integrated sensors for isolating, investigating and eventually manipulating single biomolecules are important experimental tools for developing next-generation DNA-sequencing platforms and for conducting ‘omics research which is a defining part of systems biology. In that context resistive pulse sensing has emerged as a powerful technology at the intersection of biotechnology and nanotechnology allowing electrical, label-free screening of biological compounds such as proteins or DNA with single-molecule, single-nucleotide and even single binding site resolution. Resistive pulse sensing technology has been at the center of recent commercial $100Ms investments in the next-generation DNA-sequencing sector. While next-generation sequencing platforms based on resistive pulse sensing techniques will mature further, the technology is also increasingly used for screening other biomolecules such as for example proteins. This allows for developing novel diagnostics and ultra-high throughput pre-clinical drug screening systems which might help to transform the pharma pipeline similarly to how the $1000-genome has revolutionized DNA-sequencing.

Employing integrated nano- and microfluidic circuits for detecting and characterizing biological compounds through resistive pulse sensing technology is a vibrant area of research at the interface of biotechnology and nanotechnology. Resistive pulse sensing platforms can be customized to study virtually any particle of choice which can be threaded through a fluidic channel and enable label-free single-particle interrogation with the primary read-out signal being an electric current fingerprint. The ability to perform label-free molecular screening with single-molecule and even single binding site resolution makes resistive pulse sensing technology a powerful tool for analyzing the smallest units of biological systems and how they interact with each other on a molecular level. This task is at the core of experimental systems biology and in particular 'omics research which in combination with next-generation DNA-sequencing and next-generation drug discovery and design forms the foundation of a novel disruptive medical paradigm commonly referred to as personalized medicine or precision medicine. DNA-sequencing has approached the 1000-Dollar-Genome milestone allowing for decoding a complete human genome with unmatched speed and at low cost. Increased sequencing efficiency yields massive amounts of genomic data. Analyzing this data in combination with medical and biometric health data eventually enables understanding the pathways from individual genes to physiological functions. Access to this information triggers fundamental questions for doctors and patients alike: what are the chances of an outbreak for a specific disease? Can individual risks be managed and if so how? Which drugs are available and how should they be applied? Could a new drug be tailored to an individual's genetic predisposition fast and in an affordable way? In order to provide answers and real-life value to patients, the rapid evolvement of novel computing approaches for analyzing big data in systems genomics has to be accompanied by an equally strong effort to develop next-generation DNA-sequencing and next-generation drug screening and design platforms. In that context lab-on-a-chip devices utilizing nanopore- and nanochannel based resistive pulse-sensing technology for DNA-sequencing and protein screening applications occupy a key role. This paper describes the status quo of resistive pulse sensing technology for these two application areas with a special focus on current technology trends and challenges ahead.

Electroencephalography (EEG) is a noninvasive way to record brain activity by means of measuring electrical fields arising from neural activation. Being relatively inexpensive, safe, and readily available, EEG-based techniques have been studied as potential methods for controlling brain-machine interfaces. Previous attempts to analyze EEG signals have focused on well-characterized sensorimotor data features. However, the brain-machine interface field seems to have stagnated in improving motor decoding using this method. One way to overcome this hurdle is to use neural-network-based classification methods to analyze brain-activity data. In this paper, we describe the novel neural networks we created for analyzing existing EEG data. Although these neural networks were programmed, trained, and tested in a conventional central processing unit or graphics processing unit environment, their novelty lies in their full compatibility with IBM's recently introduced ultralow power, neuromorphic TrueNorth chip infrastructure, thus, constituting the analytical units in the next generation of neurobionic mobile devices. We report on the development of a new EEG signal classifier built on a spiking neural network that runs on the TrueNorth platform. Using a modified back-propagation training method that employs trinary weights, we demonstrate state-of-the-art classification accuracy.

Solid-state nanopores have been shown to be suitable for single molecule detection. While numerous modeling investigations exist for DNA within nanopores, there are few simulations of protein translocations. In this paper, we use atomistic molecular dynamics to investigate the translocation of proteins through a silicon nitride nanopore. The nanopore dimensions and profile are representative of experimental systems. We are able to calculate the change in blockade current and friction coefficient for different positions of the protein within the pore. The change in ionic current is found to be negligible until the protein is fully within the pore and the current is lowest when the protein is in the pore center. Using a simple theory that gives good quantitative agreement with the simulation results we are able to show that the variation in current with position is a function of the pore shape. In simulations that guide the protein through the nanopore we identify the effect that confinement has on the friction coefficient of the protein. This integrated view of translocation at the nanoscale provides useful insights that can be used to guide the design of future devices.

Background: Injuries of an extraocular muscle or of the globe due to a dog-bite are rare. Patient: An 43-year-old patient presented with an orbital dog bite. He had a complete avulsion of the inferior rectus muscle. During the primary surgical repair, the proximal part of the muscle could not be found. The distal part of the inferior rectus muscle was sutured to the tissue in the original region of the retracted inferior rectus muscle. After surgical repair the patient could read without complaint. We explain the unexpected remarkable postoperative result on the basis of high resolution MRI: Although the muscle belly was lost, some parts of the muscle sheath forming connections to Tenon's capsule had remained. These allowed a certain function of the muscle. Conclusions: If during the primary surgical repair of a traumatic avulsion of an extraocular muscle the proximal part of the muscle could not be found, it is worth to suture the distal part of the muscle to tissues (empty muscle sheath, connections to Tenon's capsule) in the original region of the lost muscle. This prevents a further retraction of the proximal part of the muscle and helps the finding and differentiation of the structures of the muscle during a prospectivly following surgical repair.

Background: In cases of severe palsy or paralysis of eye muscles, it is sometimes necessary to extensively weaken the homolateral antagonist. Large recessions reduce the ocular motility by shortening the arc of contact between muscle and sclera. The lengthening of the muscle tendon with Fascia lata, Lyo-Dura®, or the free transplantation of an excided piece of autologous eye muscle tendon does not ensure predictable precise results. We used polytetrafluoroethylene (PTFE, Gore-Tex®) for seven years and report on our results. Method: A new technique is introduced in which the muscle is lengthened by PTFE. Ten patients (7 sixth nerve palsies, 1 strabismus fixus, 2 Duane’s retraction syndromes) underwent lengthening of the medial rectus muscle with a Gore-Tex® soft tissue patch between 8 and 14mm in size. The two patients with the Duane's retraction syndrome were not operated on the rectus externus muscle. All other patients had a resection of the paralytic muscle. Results: In most cases, good results were ob...

One promising path for future drug screening technologies is to examine the binding of ligands to target proteinsat the single molecule level by passing them through nanometer sized pores and measuring the change in porecurrent during translocation. With the aim of evaluating such technologies we perform virtual experiments onthe translocation of proteins through silicon nitride nanopores. These simulations consist of large scale, fullyatomistic models of the translocation process that involve steering a test protein through the nanopore on atimescale of tens of nanoseconds. We make a comparison between theoretically expected and simulated values ofthe current drop that is seen when the protein occupies the pore. Details of the stability of the protein and thepreservation of its function as measured by its secondary and tertiary structure will be presented to validate boththe simulation results and the fundamental design of the proposed device. Finally, the results will be placed inthe context of experimental work that combines nanofabrication and microuidics to create a high throughput,low cost, drug screening device.Keywords: nanosensors, single molecule sensing, molecular dynamics, molecular simulation, drug screening,nanotechnology, microuidics

Channels formed by membrane proteins regulate the transport of water, ions or nutrients that are essential to cells' metabolism. Recent advances in nanotechnology allow us to fabricate solid-state nanopores for transporting and analyzing biomolecules. However, uncontrollable surface properties of a fabricated nanopore cause irregular transport of biomolecules, limiting potential biomimetic applications. Here we show that a nanopore functionalized with a self-assembled monolayer (SAM) can potentially regulate the transport of a DNA molecule by changing functional groups of the SAM. We found that an enhanced interaction between DNA and a SAM-coated nanopore can slow down the translocation speed of DNA molecules and increase the DNA capture-rate. Our results demonstrate that the transport of DNA molecules inside nanopores could be modulated by coating a SAM on the pore surface. Our method to control the DNA motion inside a nanopore may find its applications in nanopore-based DNA sequencing devices.

Even today much time is lost in bringing effective help when an accident with a truck carrying dangerous goods occurs. The location of the accident is often not well described by the people reporting it. Often it is difficult to determine what sort of freight a lorry is carrying, for example when the vehicle is burning, and sometimes the fire brigade does not bring the appropriate extinguishing equipment for the first help approach. Now an emergency location system is under development which will remove all these disadvantages. For immediate help on car accidents, an automatic alert message is transferred from the crashed vehicle via the emergency call centre to the fire brigade, the police station or the headquarters of an ambulance. The alert is initiated by a crash sensor within the car or the truck. The data channel between vehicle and base can be a radio link or, for longer distances, a mobile telephone connection using CNET or GSM. AS help is faster and more effective with this system, environmental damage caused by dangerous goods can be reduced.In Stuttgart the mobile part of the DGPS-based emergency location system has been integrated in several vehicles. The full system is being tested within a field trial of a pilot project called STORM (Stuttgart Transport Operation by Regional Management), which is part of a programme of the European Community. The results of these tests are summarized in this paper. The integration of the system into fleet management applications is also considered.

We use thermal and room temperature nanoimprint lithography (NIL) for directly patterning the photoactive polymer poly(3-hexylthiophene-2,5-diyl) (P3HT) in the sub-50-nm region. Different types of molds were used to directly imprint the desired structures into P3HT thin films. Good pattern transfer is achieved independent of the presence of other underlying polymer layers or the type of substrate incorporated. Further, we discuss the future application of this technology to the fabrication of ordered heterojuction organic photovoltaic devices and demonstrate that the NIL process involved does not damage the polymer or alter its chemical or electrical properties.

A novel back-end-of-line (BEOL) patterning and integration process termed Multi-Level Multiple Exposure (MLME) technique is herein introduced. The MLME technique simplifies BEOL dual damascene (DD) integration while simultaneously being applicable to all BEOL levels. It offers a patterning resolution reaching into the sub-100nm region and improves semiconductor manufacturing cost and throughput. MLME employs a dual-layer imaging stack (via + trench resists) cast onto a customized etch transfer multilayer stack. This process implements a strict litho-litho-etch sequence for transferring the trench- and via-patterns into the dielectric layer. Under the MLME scheme, two imaging steps (i.e. via- and trench-level patterning) are executed consecutively followed by a dry etch process that transfers the lithographically-formed patterns into the customized etch transfer multilayer stack and further into the dielectric layer. The MLME integration scheme not only decreases the number of overall process steps for the full DD BEOL process but also eliminates several inter-tool wafer exchange sequences as performed in a conventional litho-etch-litho-etch process flow. All MLME process steps were demonstrated i.e. combined 193nm-dry dual-resist layer MLME via- and trench-lithography, full pattern transfer of via- and trench-patterns into the dielectric layer using reactive ion etching (RIE), as well as electroplating and polishing of the DD patterns. This paper provides a detailed description of both post-lithography steps of the DD process for a DD BEOL structure, i.e. (i) the RIE-pattern transfer process with the customized multilayer stack, and (ii) the metallization process completing the DD process for one BEOL layer. Furthermore, the integration capabilities of the MLME technique were demonstrated and characterized by generating an electrically functional via-chain connecting two neighboring BEOL layers fabricated by subsequently applying the MLME approach to both layers. An exhaustive description and evaluation of MLME lithographic patterning is given in an accompanying paper.

In this work, the conventional via-first dual damascene (DD) patterning scheme is replaced by a cost-efficient Multi-Level Multiple Exposure (MLME) patterning and etching approach. A two-layer positive-tone photoresist stack is sequentially imaged using 193 nm dry lithography, to produce a DD resist structure that is subsequently transferred into an auxiliary dual organic underlayer stack, and then further into a dielectric layer. This novel integration approach eliminates inter-tool wafer exchange sequences as performed in a conventional litho-etch-litho-etch process flow, while simultaneously being applicable to all back-end-of-the-line (BEOL) levels, ensuring throughput increase. The top and bottom resist layers are chemically designed in such a way that they feature differential solubility in organic solvents making it possible to coat the top photoresist onto the bottom resist layer without intermixing to enable a strict litholitho- etch processing sequence. Independent registration of the via and trench structures in the bottom and top resist layers is achieved by selective photospeed decoupling of the respective layers, so that the bottom resist is largely insensitive at nominal resist exposure dose for the top resist. Imaging performance evaluation of the newly introduced MLME technology includes the resist materials selection process and their required properties (solvent compatibility, adhesion, photospeed, defectivity and correction of via dose bias due to trench exposure) as well as metrology work. Image transfer of the patterned DD resist structure into an underlying transfer layer stack and then further into a dielectric layer using Reactive Ion Etching (RIE) followed by electroplating, polishing and electrical testing was also thoroughly investigated and is described in detail in an accompanying paper.

In this paper, we will outline the approach for optimizing the illumination conditions to print three-dimensional images in resist stacks of varying sensitivity in a single exposure. The algorithmic approach for acheiving both optimal common and weakest window is presented. Results will be presented which demonstrate the ability of the technique to create threedimensional structures. The performance of the common and weakest window formulation will be explored using this approach. Additionally, due to physical restrictions there are limitations to the type of patterns that can be printed with a single exposure in this manner, thus the abilities of such a technique will be explored.

Planar nanogap electrodes of predetermined spacing are fabricated using direct high-resolution metal nanotransfer printing on a solid substrate (see image). A GaAs/AlGaAs heterostructure featuring a nanoscale groove on a cleaved plane is the mold. Successful transfer experiments yield sectioned metal thin films with gap separation down to about 10 nm having excellent electrical properties.

We have demonstrated direct nanoscale transfer printing of PdAu lines from a hard mold onto a hard substrate at room-temperature without employing any flexible buffer layers or organic adhesion promoters or release agent layers. The molds used in these experiments were GaAs/AlGaAs sandwich structures fabricated by molecular-beam epitaxy that we cleaved and selectively etched afterwards in order to generate 3D grating structures with nanometer resolution on their edges. We fabricated positive multi-line molds with different aspect ratios, linewidths between 15 nm and 100 nm, and spacings between lines ranging from 9 nm to 70 nm. PdAu was evaporated onto the mold surface, and a titanium layer was deposited on top of the PdAu layer. By pressing the mold against a Si/SiO2 substrate the Ti/PdAu sandwich structure was directly transferred onto the SiO2 surface. The experiments revealed that direct hard-on-hard transfer of nanoscale structures from a mold onto a substrate can be used to fabricate PdAu gaps with widths of down to 9 nm.

Challenges and issues of Nanoimprint Lithography (NIL) are addressed and discussed. In particular, imprinting properties of an innovative epoxy-based polymer have been investigated, which can be used for combined thermal and ultraviolet nanoimprinting (TUV-NIL) processes aiming at high-throughput nanoimprint lithography. Our recent progress in developing a new room-temperature nanoimprint (RTNIL) tool for the sub-10-nm region is shown.

Nanoimprint lithography (NIL) is a new technique for lithographic patterning of nanostructures, which has advantages of high resolution, high throughput and low cost. This technique is presenting great opportunities for innovation and discovery in many engineering and scientific areas, such as electronic, optoelectronic and magnetism. We will show how the combination of different NIL techniques can lead to the realization of optical components such as waveguide gratings, sub-wavelength gratings, wavelength filters and photonic crystals, which in turn can lead to the design of optical devices with enhanced performance.

We performed combined thermal and ultraviolet nanoimprint lithography (TUV-NIL) using a recently developed nanoimprint polymer (mr-NIL 6000 from Micro Resist technology GmbH) and achieved an imprinted feature size of 50 nm. We used commercially available 2-inch-diameter transparent quartz molds (NIL Technology, Denmark and Obducat, Sweden) comprising 150 nm to 190 nm-deep features of various shapes and aspect ratios with lateral dimensions ranging between 50 nm and 300 nm. The imprint polymer was spun onto a silicon substrate, covered with an oxide layer. After the TUV-NIL step, residual polymer layers at the bottom of the imprinted features were removed by oxygen plasma etching. Imprinted patterns were then transferred into the silicon oxide layer underneath by reactive ion etching (RIE). In a final step the residual polymer was stripped off the silicon oxide surface in an oxygen asher. All imprinted features as well as the corresponding pattern transfer results showed good surface and sidewall characteristics.

We have demonstrated multi-step room-temperature nanoimprint lithography (RTNIL) using polystyrene (PS, average molecular weight 97 kg/mol) as the polymer layer for imprinting complex patterns. In separate experiments, single, double, and multiple (up to 10) sequential imprint steps were performed at imprint pressures between 1 to 30 MPa. To accomplish this demonstration, we designed and built a tool that controllably and repeatedly translated and pressed a sample into a stationary mold. The demonstrated inter-step alignment accuracy of this tool was ∼500 nm. Results of these experiments revealed the polymer deformation that results when nanoimprint is used to further deform a previously structured surface. The molds used in these experiments consisted of 400-nm-period diffraction gratings, as well as of rectangular structures of varying aspect ratios, ranging from 150 to 300 nm wide.

Purpose To determine whether radial transsection of the inner lamina of the phaco tunnel (T-cut) allows intraocular pressure (IOP) control in cases of co-existing cataract and glaucoma and to evaluate the results of this glaucoma triple procedure. Setting Department of Ophthalmology, Hanusch Hospital, Vienna, Austria. Methods In a prospective study, a T-shaped incision in the tunnel floor was performed in 43 eyes (Group A); 48 eyes (Group B) had phacotrabeculectomy (phacoemulsification and trabeculectomy of a 3.0 × 2.0 mm tissue block). Results At the end of a minimum follow-up of 24 months in Group A (range 24 to 30 months) and of 28 months in Group B (range 28 to 44 months), there was no significant difference in the extent of IOP reduction between groups. Intraocular pressure was well controlled (≤20 mm Hg) without antiglaucoma therapy in 27 eyes (62.8%) in Group A and 30 (62.5%) in Group B. Eleven eyes (25.6%) in Group A and 14 (29.2%) in Group B achieved an IOP of 20 mm Hg or less with antiglaucoma therapy. Five eyes (11.6%) in Group A and 4 (8.3%) in Group B required surgical revision. Complications included hypotony (Group A, 5 eyes; Group B, 3 eyes), hyphema (Group A, 8 eyes; Group B, 6 eyes), malignant glaucoma (Group B, 1 eye), in-the-bag hematoma (Group B, 1 eye), and fibrin exudation (Group B, 1 eye). Conclusion Phacoemulsification with a T-cut in the tunnel floor was a safe and effective combined procedure and, in this regard, equivalent to phacotrabeculectomy.

Nanopore-based technologies have attracted much attention recently for their promising use in low-cost and high-throughput genome sequencing. To achieve single-base resolution of DNA sequencing, it is critical to slow and control the translocation of DNA, which has been achieved in a protein nanopore but not yet in a solid-state nanopore. Using all-atom molecular dynamics simulations, we investigated the dynamics of a single-stranded DNA (ssDNA) molecule in an aqueous glycerol solution confined in a SiO(2) nanopore. The friction coefficient ξ of the ssDNA molecule is found to be approximately 18 times larger in glycerol than in water, which can dramatically slow the motion of ssDNA. The electrophoretic mobility μ of ssDNA in glycerol, however, decreases by almost the same factor, yielding the effective charge (ξμ) of ssDNA being roughly the same as in water. This is counterintuitive since the ssDNA effective charge predicted from the counterion condensation theory varies with the dielectric constant of a solvent. Due to the larger friction coefficient of ssDNA in glycerol, we further show that glycerol can improve trapping of ssDNA in the DNA transistor, a nanodevice that can be used to control the motion of ssDNA in a solid-state nanopore. Simulation results of slowing ssDNA translocation were confirmed in our nanopore experiment.

Solid state nanopores are a core element of next-generation single molecule tools in the field of nano-biotechnology. Thin film electrodes integrated into a pore can interact with charges and fields within the pore. In order to keep the nanopore open and thus functional electrochemically induced surface alteration of electrode surfaces and bubble formation inside the pore have to be eliminated. This paper provides electrochemical analyses of nanopores drilled into TiN membranes which in turn were employed as thin film electrodes. We studied physical pore integrity and the occurrence of water decomposition yielding bubble formation inside pores by applying voltages between -4.5 and +4.5 V to membranes in various protection stages continuously for up to 24 h. During potential application pores were exposed to selected electrolyte-solvent systems. We have investigated and successfully eliminated electrochemical pore oxidation and reduction as well as water decomposition inside nanopores of various diameters ranging from 3.5 to 25 nm in 50 nm thick TiN membranes by passivating the nanopores with a plasma-oxidized layer and using a 90% solution of glycerol in water as KCl solvent. Nanopore ionic conductances were measured before and after voltage application in order to test for changes in pore diameter due to electrochemical oxidation or reduction. TEM imaging was used to confirm these observations. While non-passivated pores were electrochemically oxidized, neither electrochemical oxidation nor reduction was observed for passivated pores. Bubble formation through water decomposition could be detected in non-passivated pores in KCl/water solutions but was not observed in 90% glycerol solutions. The use of a protective self-assembled monolayer of hexadecylphosphonic acid (HDPA) was also investigated.