Evaluating Bacterial Nanocellulose Interfaces for Recording Surface Biopotentials from Plants.

The study of plant electrophysiology offers promising techniques to track plant health and stress in vivo for both agricultural and environmental monitoring applications. Use of superficial electrodes on the plant body to record surface potentials may provide new phenotyping insights. Bacterial nanocellulose (BNC) is a flexible, optically translucent, and water-vapor-permeable material with low manufacturing costs, making it an ideal substrate for non-invasive and non-destructive plant electrodes. This work presents BNC electrodes with screen-printed carbon (graphite) ink-based conductive traces and pads. It investigates the potential of these electrodes for plant surface electrophysiology measurements in comparison to commercially available standard wet gel and needle electrodes. The electrochemically active surface area and impedance of the BNC electrodes varied based on the annealing temperature and time over the ranges of 50 °C to 90 °C and 5 to 60 min, respectively. The water vapor transfer rate and optical transmittance of the BNC substrate were measured to estimate the level of occlusion caused by these surface electrodes on the plant tissue. The total reduction in chlorophyll content under the electrodes was measured after the electrodes were placed on maize leaves for up to 300 h, showing that the BNC caused only a 16% reduction. Maize leaf transpiration was reduced by only 20% under the BNC electrodes after 72 h compared to a 60% reduction under wet gel electrodes in 48 h. On three different model plants, BNC-carbon ink surface electrodes and standard invasive needle electrodes were shown to have a comparable signal quality, with a correlation coefficient of >0.9, when measuring surface biopotentials induced by acute environmental stressors. These are strong indications of the superior performance of the BNC substrate with screen-printed graphite ink as an electrode material for plant surface biopotential recordings.

A Wireless Multimodal Physiological Monitoring ASIC for Animal Health Monitoring Injectable Devices.

Utilizing injectable devices for monitoring animal health offers several advantages over traditional wearable devices, including improved signal-to-noise ratio (SNR) and enhanced immunity to motion artifacts. We present a wireless application-specific integrated circuit (ASIC) for injectable devices. The ASIC has multiple physiological sensing modalities including body temperature monitoring, electrocardiography (ECG), and photoplethysmography (PPG). The ASIC fabricated using the CMOS 180 nm process is sized to fit into an injectable microchip implant. The ASIC features a low-power design, drawing an average DC power of 155.3 µW, enabling the ASIC to be wirelessly powered through an inductive link. To capture the ECG signal, we designed the ECG analog frontend (AFE) with 0.3 Hz low cut-off frequency and 45-79 dB adjustable midband gain. To measure PPG, we employ an energy-efficient and safe switched-capacitor-based (SC) light emitting diode (LED) driver to illuminate an LED with milliampere-level current pulses. A SC integrator-based AFE converts the current of photodiode with a programmable transimpedance gain. A resistor-based Wheatstone Bridge (WhB) temperature sensor followed by an instrumentation amplifier (IA) provides 27-47 °C sensing range with 0.02 °C inaccuracy. Recorded physiological signals are sequentially sampled and quantized by a 10-bit analog-to-digital converter (ADC) with the successive approximation register (SAR) architecture. The SAR ADC features an energy-efficient switching scheme and achieves a 57.5 dB signal-to-noise-and-distortion ratio (SNDR) within 1 kHz bandwidth. Then, a back data telemetry transmits the baseband data via a backscatter scheme with intermediate-frequency assistance. The ASIC's overall functionality and performance has been evaluated through an in vivo experiment.

A Wearable System for Continuous Monitoring and Assessment of Speech, Gait, and Cognitive Decline for Early Diagnosis of ADRD.

Early detection of cognitive decline is essential to study mild cognitive impairment and Alzheimer's Disease in order to develop targeted interventions and prevent or stop the progression of dementia. This requires continuous and longitudinal assessment and tracking of the related physiological and behavioral changes during daily life. In this paper, we present a low cost and low power wearable system custom designed to track the trends in speech, gait, and cognitive stress while also considering the important human factor needs such as privacy and compliance. In the form factors of a wristband and waist-patch, this multimodal, multi-sensor system measures inertial signals, sound, heart rate, electrodermal activity and pulse transit time. A total power consumption of 2.6 mW without any duty cycling allows for more than 3 weeks of run time between charges when 1500 mAh batteries are used.Clinical Relevance- Much earlier detection of Alzheimer's disease and related dementias may be possible by continuous monitoring of physiological and behavioral state using application specific wearable sensors during the activities of daily life.

Adolescent Asthma Monitoring: A Preliminary Study of Audio and Spirometry Modalities.

Asthma patients' sleep quality is correlated with how well their asthma symptoms are controlled. In this paper, deep learning techniques are explored to improve forecasting of forced expiratory volume in one second (FEV1) by using audio data from participants and test whether auditory sleep disturbances are correlated with poorer asthma outcomes. These are applied to a representative data set of FEV1 collected from a commercially available sprirometer and audio spectrograms collected overnight using a smartphone. A model for detecting nonverbal vocalizations including coughs, sneezes, sighs, snoring, throat clearing, sniffs, and breathing sounds was trained and used to capture nightly sleep disturbances. Our preliminary analysis found significant improvement in FEV1 forecasting when using overnight nonverbal vocalization detections as an additional feature for regression using XGBoost over using only spirometry data.Clinical relevance- This preliminary study establishes up to 30% improvement of FEV1 forecasting using features generated by deep learning techniques over only spirometry-based features.

Robust Cough Detection With Out-of-Distribution Detection.

Cough is an important defense mechanism of the respiratory system and is also a symptom of lung diseases, such as asthma. Acoustic cough detection collected by portable recording devices is a convenient way totrack potential condition worsening for patients who have asthma. However, the data used in building current cough detection models are often clean, containing a limited set of sound categories, and thus perform poorly when they are exposed to a variety of real-world sounds which could be picked up by portable recording devices. The sounds that are not learned by the model are referred to as Out-of-Distribution (OOD) data. In this work, we propose two robust cough detection methods combined with an OOD detection module, that removes OOD data without sacrificing the cough detection performance of the original system. These methods include adding a learning confidence parameter and maximizing entropy loss. Our experiments show that 1) the OOD system can produce dependable In-Distribution (ID) and OOD results at a sampling rate above 750 Hz; 2) the OOD sample detection tends to perform better for larger audio window sizes; 3) the model's overall accuracy and precision get better as the proportion of OOD samples increase in the acoustic signals; 4) a higher percentage of OOD data is needed to realize performance gains at lower sampling rates. The incorporation of OOD detection techniques improves cough detection performance by a significant margin and provides a valuable solution to real-world acoustic cough detection problems.

Preliminary Evaluation of a System with On-Body and Aerial Sensors for Monitoring Working Dogs.

This paper presents a system for behavioral, environmental, and physiological monitoring of working dogs using on-body and aerial sensors. The proof of concept study presented here includes two trained dogs performing nine scent detection tasks in an uncontrolled environment encompassing approximately two acres. The dogs were outfitted with a custom designed wearable harness to monitor their heart rate, activity levels and skin temperature. We utilized a commercially available micro-air vehicle to perform aerial sensing by tracking the terrain and movement of the dog in the outdoor space. The dogs were free to explore the space working at maximal speeds to complete a scent-based search-and-retrieval task. Throughout the experiment, the harness data was transferred to a base station via Wi-Fi in real-time. In this work, we also focused on testing the performance of a custom 3D electrode with application specific ergonomic improvements and adaptive filter processing techniques to recover as much electrocardiography data as possible during high intensity motion activity. We were able to recover and use 84% of the collected data where we observed a trend of heart rate generally increasing immediately after successful target localization. For tracking the dogs in the aerial video footage, we applied a state-of-the-art deep learning algorithm designed for online object tracking. Both qualitative and quantitative tracking results are very promising. This study presents an initial effort towards deployment of on-body and aerial sensors to monitor the working dogs and their environments during scent detection and search and rescue tasks in order to ensure their welfare, enable novel dog-machine interfaces, and allow for higher success rate of remote and automated task performance.

A Multimodal Sensing Platform for Interdisciplinary Research in Agrarian Environments.

Agricultural and environmental monitoring programs often require labor-intensive inputs and substantial costs to manually gather data from remote field locations. Recent advances in the Internet of Things enable the construction of wireless sensor systems to automate these remote monitoring efforts. This paper presents the design of a modular system to serve as a research platform for outdoor sensor development and deployment. The advantages of this system include low power consumption (enabling solar charging), the use of commercially available electronic parts for lower-cost and scaled up deployments, and the flexibility to include internal electronics and external sensors, allowing novel applications. In addition to tracking environmental parameters, the modularity of this system brings the capability to measure other non-traditional elements. This capability is demonstrated with two different agri- and aquacultural field applications: tracking moth phenology and monitoring bivalve gaping. Collection of these signals in conjunction with environmental parameters could provide a holistic and context-aware data analysis. Preliminary experiments generated promising results, demonstrating the reliability of the system. Idle power consumption of 27.2 mW and 16.6 mW for the moth- and bivalve-tracking systems, respectively, coupled with 2.5 W solar cells allows for indefinite deployment in remote locations.

Wireless Wearable Electrochemical Sensing Platform with Zero-Power Osmotic Sweat Extraction for Continuous Lactate Monitoring.

Wearable and wireless monitoring of biomarkers such as lactate in sweat can provide a deeper understanding of a subject's metabolic stressors, cardiovascular health, and physiological response to exercise. However, the state-of-the-art wearable and wireless electrochemical systems rely on active sweat released either via high-exertion exercise, electrical stimulation (such as iontophoresis requiring electrical power), or chemical stimulation (such as by delivering pilocarpine or carbachol inside skin), to extract sweat under low-perspiring conditions such as at rest. Here, we present a continuous sweat lactate monitoring platform combining a hydrogel for osmotic sweat extraction, with a paper microfluidic channel for facilitating sweat transport and management, a screen-printed electrochemical lactate sensor, and a custom-built wireless wearable potentiostat system. Osmosis enables zero-electrical power sweat extraction at rest, while continuous evaporation at the end of a paper channel allows long-term sensing from fresh sweat. The positioning of the lactate sensors provides near-instantaneous sensing at low sweat volume, and the custom-designed potentiostat supports continuous monitoring with ultra-low power consumption. For a proof of concept, the prototype system was evaluated for continuous measurement of sweat lactate across a range of physiological activities with changing lactate concentrations and sweat rates: for 2 h at the resting state, 1 h during medium-intensity exercise, and 30 min during high-intensity exercise. Overall, this wearable system holds the potential of providing comprehensive and long-term continuous analysis of sweat lactate trends in the human body during rest and under exercising conditions.

Simultaneous Localization of Biobotic Insects using Inertial Data and Encounter Information.

Several recent research efforts have shown that the bioelectrical stimulation of their neuro-mechanical system can control the locomotion of Madagascar hissing cockroaches (Gromphadorhina portentosa). This has opened the possibility of using these insects to explore centimeter-scale environments, such as rubble piles in urban disaster areas. We present an inertial navigation system based on machine learning modules that is capable of localizing groups of G. portentosa carrying thorax-mounted inertial measurement units. The proposed navigation system uses the agents' encounters with one another as signals of opportunity to increase tracking accuracy. Results are shown for five agents that are operating on a planar (2D) surface in controlled laboratory conditions. Trajectory reconstruction accuracy is improved by 16% when we use encounter information for the agents, and up to 27% when we add a heuristic that corrects speed estimates via a search for an optimal speed-scaling factor.

A Wearable Patch for Prolonged Sweat Lactate Harvesting and Sensing.

Operating at low sweat rates, such as those experienced by humans at rest, is still an unmet need for state-of-the-art wearable sweat harvesting and testing devices for lactate. Here, we report the on-skin performance of a non-invasive wearable sweat sampling patch that can harvest sweat at rest, during exercise, and post-exercise. The patch simultaneously uses osmosis and evaporation for long-term (several hours) sampling of sweat. Osmotic sweat withdrawal is achieved by skin-interfacing a hydrogel containing a concentrated solute. The gel interfaces with a paper strip that transports the fluid via wicking and evaporation. Proof of concept results show that the patch was able to sample sweat during resting and post-exercise conditions, where the lactate concentration was successfully quantified. The patch detected the increase in sweat lactate levels during medium level exercise. Blood lactate remained invariant with exercise as expected. We also developed a continuous sensing version of the patch by including enzymatic electrochemical sensors. Such a battery-free, passive, wearable sweat sampling patch can potentially provide useful information about the human metabolic activity.

Investigating the Relationship between Cough Detection and Sampling Frequency for Wearable Devices.

Cough detection can provide an important marker to monitor chronic respiratory conditions. However, manual techniques which require human expertise to count coughs are both expensive and time-consuming. Recent Automatic Cough Detection Algorithms (ACDAs) have shown promise to meet clinical monitoring requirements, but only in recent years they have made their way to non-clinical settings due to the required portability of sensing technologies and the extended duration of data recording. More precisely, these ACDAs operate at high sampling frequencies, which leads to high power consumption and computing requirements, making these difficult to implement on a wearable device. Additionally, reproducibility of their performance is essential. Unfortunately, as the majority of ACDAs were developed using private clinical data, it is difficult to reproduce their results. We, hereby, present an ACDA that meets clinical monitoring requirements and reliably operates at a low sampling frequency. This ACDA is implemented using a convolutional neural network (CNN), and publicly available data. It achieves a sensitivity of 92.7%, a specificity of 92.3%, and an accuracy of 92.5% using a sampling frequency of just 750 Hz. We also show that a low sampling frequency allows us to preserve patients' privacy by obfuscating their speech, and we analyze the trade-off between speech obfuscation for privacy and cough detection accuracy.Clinical relevance-This paper presents a new cough detection technique and preliminary analysis on the trade-off between detection accuracy and obfuscation of speech for privacy. These findings indicate that, using a publicly available dataset, we can sample signals at 750 Hz while still maintaining a sensitivity above 90%, suggested to be sufficient for clinical monitoring [1].

Novel 3D-printed Electrodes for Implantable Biopotential Monitoring.

A major bottleneck in the manufacturing process of a medical implant capable of biopotential measurements is the design and assembly of a conductive electrode interface. This paper presents the use of a novel 3D-printing process to integrate conductive metal surfaces on a low-temperature co-fired ceramic base to be deployed as electrodes for electrocardiography (ECG) implants for small animals. In order to fit the ECG sensing system within the size of an injectable microchip implant, the electronics along with a pin-type lithium-ion battery are inserted into a cylindrical glass tube with both ends sealed by these 3D printed composite electrode discs using biomedical epoxy. In the scope of this paper, we present a proof-of-concept in vivo experiment for recording ECG from an avian animal model under local anesthesia to verify the electrode performance. Simultaneous recording with a commercial device validated the measurements, demonstrating promising accuracy in heart rate and breathing rate monitoring. This novel technology could open avenues for the mass manufacturing of miniaturized ECG implants.Clinical relevance- A novel manufacturing process and an implantable system are presented for continuous physiological monitoring of animals to be used by veterinarians, animal scientists, and biomedical researchers with potential future applications in human health monitoring.

Preliminary Evaluation of a Solar-Powered Wristband for Continuous Multi-Modal Electrochemical Monitoring.

Continuous, non-invasive wearable measurement of metabolic biomarkers could provide vital insight into patient condition for personalized health and wellness monitoring. We present our efforts towards developing a wearable solar-powered electrochemical platform for multimodal sweat based metabolic monitoring. This wrist-worn wearable system consists of a flexible photovoltaic cell connected to a circuit board containing ultra low power circuitry for sensor data collection, energy harvesting, and wireless data transmission, all integrated into an elastic fabric wristband. The system continuously samples amperometric, potentiometric, temperature, and motion data and wirelessly transmits these to a data aggregator. The full wearable system is 7.5 cm long and 5 cm in diameter, weighs 22 grams, and can run directly from harvested light energy. Relatively low levels of light such as residential lighting (∼200 lux) are sufficient for continuous operation of the system. Excess harvested energy is stored in a small 37 mWh lithium polymer battery. The battery can be charged in ∼14 minutes under full sunlight and can power the system for ∼8 days when fully charged. The system has an average power consumption of 176 µW. The solar-harvesting performance of the system was characterized in a variety of lighting conditions, and the amperometric and potentiometric electrochemical capabilities of the system were validated in vitro.Clinical relevance-The presented solar-powered wearable system enables continuous wireless multi-modal electrochemical monitoring for uninterrupted sensing of metabolic biomarkers in sweat while harvesting energy from indoor lighting or sunlight.

Textile-based Pressure Sensors for Monitoring Prosthetic-Socket Interfaces.

Amputees are prone to experiencing discomfort when wearing their prosthetic devices. As the amputee population grows this becomes a more prevalent and pressing concern. There is a need for new prosthetic technologies to construct more comfortable and well-fitted liners and sockets. One of the well-recognized impediments to the development of new prosthetic technology is the lack of practical inner socket sensors to monitor the inner socket environment (ISE), or the region between the residual limb and the socket. Here we present a capacitive pressure sensor fabricated through a simple, and scalable sewing process using commercially available conductive yarns and textile materials. This fully-textile sensor provides a soft, flexible, and comfortable sensing system for monitoring the ISE. We provide details of our low-power sensor system capable of high-speed data collection from up to four sensor arrays. Additionally, we demonstrate two custom set-ups to test and validate the textile-based sensors in a simulated prosthetic environment. Finally, we utilize the textile-based sensors to study the ISE of a bilateral transtibial amputee. Results indicate that the textile-based sensors provide a promising potential for seamlessly monitoring the ISE.

In Vivo Evaluation of a Subcutaneously Injectable Implant with a Low-Power Photoplethysmography ASIC for Animal Monitoring.

Photoplethysmography is an extensively-used, portable, and noninvasive technique for measuring vital parameters such as heart rate, respiration rate, and blood pressure. The deployment of this technology in veterinary medicine has been hindered by the challenges in effective transmission of light presented by the thick layer of skin and fur of the animal. We propose an injectable capsule system to circumvent these limitations by accessing the subcutaneous tissue to enable reliable signal acquisition even with lower light brightness. In addition to the reduction of power usage, the injection of the capsule offers a less invasive alternative to surgical implantation. Our current prototype combines two application-specific integrated circuits (ASICs) with a microcontroller and interfaces with a commercial light emitting diode (LED) and photodetector pair. These ASICs implement a signal-conditioning analog front end circuit and a frequency-shift keying (FSK) transmitter respectively. The small footprint of the ASICs is the key in the integration of the complete system inside a 40-mm long glass tube with an inner diameter of 4 mm, which enables its injection using a custom syringe similar to the ones used with microchip implants for animal identification. The recorded data is transferred wirelessly to a computer for post-processing by means of the integrated FSK transmitter and a software-defined radio. Our optimized LED duty cycle of 0.4% at a sampling rate of 200 Hz minimizes the contribution of the LED driver (only 0.8 mW including the front-end circuitry) to the total power consumption of the system. This will allow longer recording periods between the charging cycles of the batteries, which is critical given the very limited space inside the capsule. In this work, we demonstrate the wireless operation of the injectable system with a human subject holding the sensor between the fingers and the in vivo functionality of the subcutaneous sensing on a pilot study performed on anesthetized rat subjects.

A Smart Collar for Assessment of Activity Levels and Environmental Conditions for Guide Dogs.

This paper demonstrates the design and manufacturing of a smart and connected internet-of-things collar system for the collection of behavioral and environmental information from working canines. The environmental factors of ambient light, ambient temperature, ambient noise levels, barometric pressure and relative humidity are recorded by the smart collar system in addition to behavioral information about barking incidences and activity levels. The data are collected from the sensors and transmitted via Bluetooth to the handler's smartphone where the custom app also acquires GPS positioning using the on-board smartphone sensors. The stored data on the smartphone are uploaded to the IBM Cloud once the user is connected to a WiFi network. The low power design of the smart collar system permits it to be used continuously for 27 hours with a 290 mAh lithium polymer battery. The cost of the system is low enough to let the handlers have multiple collars and exchange it if needed or recharge it overnight when not in use. This system is currently being scaled up to be tested on hundreds of canine puppies by a preeminent guide dog school in the US. As a result, the design emphasis here has been on the cost and power reduction, comfortable ergonomics, user friendliness, and robustness of data streaming. We expect the system to provide continuous quantitative data for improving guide dog training programs in addition to contributing the well-being of other working dogs in the future.

A Systematic Literature Review of Animal-Assisted Interventions in Oncology (Part I): Methods and Results.

Animal-assisted interventions (AAIs) use human-animal interactions to positive effect in various contexts including cancer care. As the first installment of a 2-part series, this systematic literature review focuses on the research methods and quantitative results of AAI studies in oncology. We find methodological consistency in the use of canines as therapy animals, in the types of high-risk patients excluded from studies, and in the infection precautions taken with therapy animals throughout cancer wards. The investigated patient endpoints are not significantly affected by AAI, with the exceptions of improvements in oxygen consumption, quality of life, depression, mood, and satisfaction with therapy. The AAI field in oncology has progressed significantly since its inception and has great potential to positively affect future patient outcomes. To advance the field, future research should consistently improve the methodological design of studies, report data more completely, and focus more on the therapy animal's well-being.

Localization of Biobotic Insects Using Low-Cost Inertial Measurement Units.

Disaster robotics is a growing field that is concerned with the design and development of robots for disaster response and disaster recovery. These robots assist first responders by performing tasks that are impractical or impossible for humans. Unfortunately, current disaster robots usually lack the maneuverability to efficiently traverse these areas, which often necessitate extreme navigational capabilities, such as centimeter-scale clearance. Recent work has shown that it is possible to control the locomotion of insects such as the Madagascar hissing cockroach (Gromphadorhina portentosa) through bioelectrical stimulation of their neuro-mechanical system. This provides access to a novel agent that can traverse areas that are inaccessible to traditional robots. In this paper, we present a data-driven inertial navigation system that is capable of localizing cockroaches in areas where GPS is not available. We pose the navigation problem as a two-point boundary-value problem where the goal is to reconstruct a cockroach's trajectory between the starting and ending states, which are assumed to be known. We validated our technique using nine trials that were conducted in a circular arena using a biobotic agent equipped with a thorax-mounted, low-cost inertial measurement unit. Results show that we can achieve centimeter-level accuracy. This is accomplished by estimating the cockroach's velocity-using regression models that have been trained to estimate the speed and heading from the inertial signals themselves-and solving an optimization problem so that the boundary-value constraints are satisfied.

A Systematic Literature Review of Animal-Assisted Interventions in Oncology (Part II): Theoretical Mechanisms and Frameworks.

Animal-assisted interventions (AAIs) can improve patients' quality of life as complementary medical treatments. Part I of this 2-paper systematic review focused on the methods and results of cancer-related AAIs; Part II discusses the theories of the field's investigators. Researchers cite animal personality, physical touch, physical movement, distraction, and increased human interaction as sources of observed positive outcomes. These mechanisms then group under theoretical frameworks such as the social support hypothesis or the human-animal bond concept to fully explain AAI in oncology. The cognitive activation theory of stress, the science of unitary human beings, and the self-object hypothesis are additional frameworks mentioned by some researchers. We also discuss concepts of neurobiological transduction connecting mechanisms to AAI benefits. Future researchers should base study design on theories with testable hypotheses and use consistent terminology to report results. This review aids progress toward a unified theoretical framework and toward more holistic cancer treatments.

Wearable multiplexed biosensor system toward continuous monitoring of metabolites.

Comprehensive metabolic panels are the most reliable and common methods for monitoring general physiology in clinical healthcare. Translation of this clinical practice to personal health and wellness tracking requires reliable, non-invasive, miniaturized, ambulatory, and inexpensive systems for continuous measurement of biochemical analytes. We report the design and characterization of a wearable system with a flexible sensor array for non-invasive and continuous monitoring of human biochemistry. The system includes signal conditioning, processing, and transmission parts for continuous measurement of glucose, lactate, pH, and temperature. The system can operate three discrete electrochemical cells. The system draws 15 mA under continuous operation when powered by a 3.7 V 150 mAh battery. The analog front-end of the electrochemical cells has four potentiostats and three multiplexers for multiplexed and parallel readout from twelve working electrodes. Utilization of redundant working electrodes improves the measurement accuracy of sensors by averaging chronoamperometric responses across the array. The operation of the system is demonstrated in vitro by simultaneous measurement of glucose and lactate, pH, and skin temperature. In benchtop measurements, the sensors are shown to have sensitivities of 26.31 µA mM-1·cm-2 for glucose, 1.49 µA mM-1·cm-2 for lactate, 54 mV·pH-1 for pH, and 0.002 °C-1 for temperature. With the custom wearable system, these values were 0.84 ± 0.03 mV µM-1·cm-2 or glucose, 31.87 ± 9.03 mV mM-1·cm-2 for lactate, 57.18 ± 1.43 mV·pH-1 for pH, and 63.4 µV·°C-1 for temperature. This miniaturized wearable system enables future evaluation of temporal changes of the sweat biomarkers.