pwc-archive/papers-with-abstracts huggingface dataset:
Analysis of bio-electro-chemical signals from passive sweat-based wearable electro-impedance spectroscopy (EIS) towards assessing blood glucose modulations
Authors: Devangsingh Sankhala, Madhavi Pali, Kai-Chun Lin, Badrinath Jagannath, Sriram Muthukumar, Shalini Prasad
Date: 2021-04-05 00:00:00
Tasks: Time Series Analysis

There has been a recent tremendous interest in label-free detection of biomarkers which is a critical enabler of point-of-need diagnostics. A low-power, small form factor, multiplexed wearable system is proposed for continuous detection of glucose in passively expressed sweat using electrochemical impedance spectroscopy (EIS) measurement. The wearable EIS system consists of a sensing analog front end integrated with low-volume (1-5 $\mu$L) ultra-sensitive flexible biosensors. A passive sweat sensor was designed to integrate a glucose oxidase electrochemical system on active semiconducting material. The non-faradaic EIS response of the biosensor was used to calibrate the analog front end response using ratiometric Discrete Fourier Transform (DFT) for a shorter measurement time. In this work, a stringent assessment of a continuous glucose sensing platform is performed in a bottom-up approach, going from the biosensor to the system to the interaction with a human subject. The active semiconductor-based biosensors are dosed with glucose concentrations ranging from 5-200 mg/dL and detection is performed using the analog front end. In addition, a detailed analysis of battery life and performance of a wearable EIS system is discussed to define a figure of merit for an optimally integrated design. Moreover, a continuous glucose detection test is performed on a healthy human subject cohort to investigate the stability of the sensor-system mechanism for an 8-hour period, and a time-series-based, auto-regressive (AR) model was created for the system.
Perception Through 2D-MIMO FMCW Automotive Radar Under Adverse Weather
Authors: Xiangyu Gao, Sumit Roy, Guanbin Xing, Sian Jin
Date: 2021-04-04 00:00:00

Millimeter-wave (mmWave) radars are being increasingly integrated in commercial vehicles to support new Adaptive Driver Assisted Systems (ADAS) features that require accurate location and Doppler velocity estimates of objects, independent of environmental conditions. To explore radar-based ADAS applications, we have updated our test-bed with Texas Instrument's 4-chip cascaded FMCW radar (TIDEP-01012) that forms a non-uniform 2D MIMO virtual array. In this paper, we develop the necessary received signal models for applying different direction of arrival (DoA) estimation algorithms and experimentally validating their performance on formed virtual array under controlled scenarios. To test the robustness of mmWave radars under adverse weather conditions, we collected raw radar dataset (I-Q samples post demodulated) for various objects by a driven vehicle-mounted platform, specifically for snowy and foggy situations where cameras are largely ineffective. Initial results from radar imaging algorithms to this dataset are presented.
Transceiver Noise Characterization based on Perturbations
Authors: F. J. Vaquero-Caballero, D. J. Ives, S. J. Savory
Date: 2021-04-03 00:00:00

In this paper, we discuss a new technique for measuring transceiver noise, skew, and for the detection of uncompensated transceiver impairments. The method consists on the introduction of frequency domain notch or notches at the transmitter, which allows to estimate of the Signal-to-Noise and Distortion Ratio (SNDR) at different stages over the transmission chain. The proposed technique requires the detection of the signal spectrum using either: a modem receiver, an Optical Spectrum Analyzer (OSA), an oscilloscope, or an Electrical Spectrum Analyzer (ESA), depending on the stage where the spectrum is measured and requiring minimal processing of the received signal. Our analysis is focused on Ciena's WaveLogic Ai commercial transceiver and a 95 Gbaud DAC. We demonstrate that symmetrically placed frequency domain notches can be used to mitiguate the influence of crosstalk on the SNDR estimates. Finally, we show how a single notch spectrum can be used to detect and compensate for impairments, and we perform skew estimation as an example.
Prototype Filters and Filtered Waveforms for Radio Air-Interfaces: A Review
Authors: M. Y. Bendimerad, S. Senhadji, F. T. Bendimerad
Date: 2021-04-03 00:00:00

The perspective for the next generation of wireless communications prognosticates that the communication should take place with high heterogeneity in terms of services and specifications. The emergence of new communication networks, especially between devices like internet of things (IoT) networks, machine type communications (MTC) and unmanned aerial vehicles (UAV), makes the redesign of the radio air-interfaces even higher significant. High data rate, efficient use of the spectrum, low latency, flexibility, scalable ability, energy efficiency and reduced complexity are in the vanguard of the challenging needs. This highlights the necessity to overrides classical waveforms and propose new air interface that best fits these requirements. Several attempts were conducted to propose the most suitable prototype filters and their associated waveforms that support the future applications with these crucial prerequisites. In the present paper, we analyze the most important marked prototype filters reported in literature, and their corresponding waveforms proposed as prominent candidates for wireless air interface. We will show that the filter properties impact the waveform performances and that a close filter-waveform interrelationship is there. The principal contribution is to give a deep insight on the structure of these filters and waveforms and to come up with a fair comparison regarding different criteria and distinct figures of merit.
DeepMI: Deep Multi-lead ECG Fusion for Identifying Myocardial Infarction and its Occurrence-time
Authors: Girmaw Abebe Tadesse, Hamza Javed, Yong liu, Jin Liu, Jiyan Chen, Komminist Weldemariam, Tingting Zhu
Date: 2021-03-31 00:00:00
Tasks: Transfer Learning

Myocardial Infarction (MI) has the highest mortality of all cardiovascular diseases (CVDs). Detection of MI and information regarding its occurrence-time in particular, would enable timely interventions that may improve patient outcomes, thereby reducing the global rise in CVD deaths. Electrocardiogram (ECG) recordings are currently used to screen MI patients. However, manual inspection of ECGs is time-consuming and prone to subjective bias. Machine learning methods have been adopted for automated ECG diagnosis, but most approaches require extraction of ECG beats or consider leads independently of one another. We propose an end-to-end deep learning approach, DeepMI, to classify MI from normal cases as well as identifying the time-occurrence of MI (defined as acute, recent and old), using a collection of fusion strategies on 12 ECG leads at data-, feature-, and decision-level. In order to minimise computational overhead, we employ transfer learning using existing computer vision networks. Moreover, we use recurrent neural networks to encode the longitudinal information inherent in ECGs. We validated DeepMI on a dataset collected from 17,381 patients, in which over 323,000 samples were extracted per ECG lead. We were able to classify normal cases as well as acute, recent and old onset cases of MI, with AUROCs of 96.7%, 82.9%, 68.6% and 73.8%, respectively. We have demonstrated a multi-lead fusion approach to detect the presence and occurrence-time of MI. Our end-to-end framework provides flexibility for different levels of multi-lead ECG fusion and performs feature extraction via transfer learning.
Data augmentation for dealing with low sampling rates in NILM
Authors: Tai Le Quy, Sergej Zerr, Eirini Ntoutsi, Wolfgang Nejdl
Date: 2021-03-30 00:00:00
Tasks: Data Augmentation

Data have an important role in evaluating the performance of NILM algorithms. The best performance of NILM algorithms is achieved with high-quality evaluation data. However, many existing real-world data sets come with a low sampling quality, and often with gaps, lacking data for some recording periods. As a result, in such data, NILM algorithms can hardly recognize devices and estimate their power consumption properly. An important step towards improving the performance of these energy disaggregation methods is to improve the quality of the data sets. In this paper, we carry out experiments using several methods to increase the sampling rate of low sampling rate data. Our results show that augmentation of low-frequency data can support the considered NILM algorithms in estimating appliances' consumption with a higher F-score measurement.
Dispersal-induced growth in a time-periodic environment
Authors: Guy Katriel
Date: 2021-04-04 00:00:00

Dispersal-induced growth (DIG) occurs when two populations with time-varying growth rates, each of which, when isolated, would become extinct, are able to persist and grow exponentially when dispersal among the two populations is present. This work provides a mathematical exploration of this surprising phenomenon, in the context of a deterministic model with periodic variation of growth rates, and characterizes the factors which are important in generating the DIG effect and the corresponding conditions on the parameters involved.
Deep Learning of Conjugate Mappings
Authors: Jason J. Bramburger, Steven L. Brunton, J. Nathan Kutz
Date: 2021-04-01 00:00:00
Tasks: Deep Learning, Dimensionality Reduction

Despite many of the most common chaotic dynamical systems being continuous in time, it is through discrete time mappings that much of the understanding of chaos is formed. Henri Poincar\'e first made this connection by tracking consecutive iterations of the continuous flow with a lower-dimensional, transverse subspace. The mapping that iterates the dynamics through consecutive intersections of the flow with the subspace is now referred to as a Poincar\'e map, and it is the primary method available for interpreting and classifying chaotic dynamics. Unfortunately, in all but the simplest systems, an explicit form for such a mapping remains outstanding. This work proposes a method for obtaining explicit Poincar\'e mappings by using deep learning to construct an invertible coordinate transformation into a conjugate representation where the dynamics are governed by a relatively simple chaotic mapping. The invertible change of variable is based on an autoencoder, which allows for dimensionality reduction, and has the advantage of classifying chaotic systems using the equivalence relation of topological conjugacies. Indeed, the enforcement of topological conjugacies is the critical neural network regularization for learning the coordinate and dynamics pairing. We provide expository applications of the method to low-dimensional systems such as the R\"ossler and Lorenz systems, while also demonstrating the utility of the method on infinite-dimensional systems, such as the Kuramoto--Sivashinsky equation.
Deprenyl, an old drug with new anticancer potential: Mini review
Authors: Nelson Duran, Joao C. C. Alonso, Wagner J. Favaro
Date: 2021-04-05 00:00:00

The anticancer potential of monoamine oxidase (MAO) was observed in pre-clinical assays conducted with cell cultures and animals. L-Deprenyl (DEP) causes apoptosis in melanoma, leukemia and mammary cells. High-dose DEP has shown toxicity in mammary and pituitary cancers, as well as in monoblastic leukemia, in rats. DEP accounts for immune-stimulant effect capable of increasing natural killer cell activity, IL-2 generation, as well as of inhibiting tumor growth. DEP administration in old female rats has increased IL-2 generation and inverted the age-related depletion of IFN-{\gamma} generation in the spleen. Co-adjuvant DEP administration helped preventing/mitigating symptoms associated with peripheral neuropathy in cancer treatment. It also enhanced the cytotoxic effects of antineoplastic drugs - such as doxorubicin, cisplatin, among others - in cancer cells while they protected healthy cells from being damaged. DEP presented effect against dysfunctions such as debilitating hormone imbalance triggered by pituitary gland tumor; this gland produces the stimulatory hormone of adrenocorticotropic hormone which was related to the exacerbation of this disease. Thus, DEP emerges as an excellent potential drug against several cancer types and it also presents low toxicity in Parkinson`s disease patients subjected to long treatment with it.
Modeling ADHD in Drosophila: Investigating the Effects of Glucose on Dopamine Production Demonstrated by Locomotion
Authors: Myung Suh Choi
Date: 2021-04-03 00:00:00

Hyperactivity is one of the hallmakrs of ADHD. Aberrant dopamine signaling is a major theme in ADHD and dopamine production is directly linked to the intensity and persistence of hyperactive conduct. The strength and persistence of hyperactivity responses in Drosophila to startle stimuli were measured in a study to determine the effects of sugar on dopamine development. A total of four experimental groups, namely 1%,3%, and 5% glucose, as well as a control group were taken for the diet of Drosophila, and these four different amounts of glucose were introduced to the growth medium where Drosophila was cultured. The movements of Drosophila in the four treatment groups were captured using a camera. This experiment was carried out five times, each time using a different batch of Drosophila. Each group's average velocity over time was also reported. The web adaptation of Drosophila Activity Monitor(DAM) was used to analyze the captured movies from the camera. Furthermore, when it came to hyperactivity persistence, all four treatment classes were statistically different (p0.05). Since the strength and persistence of hyperactive behavior are directly correlated to dopamine output, this study shows that higher glucose intake is associated with more hyperactivity, for both the intensity({\Delta}V) and persistence.
Effect of Strain Rate on Single Tau, Dimerized Tau and Tau-Microtubule Interface: a Molecular Dynamics Simulation Study
Authors: Md Ishak Khan, Kathleen Gilpin, Fuad Hasan, Khandakar Abu Hasan Al Mahmud, Ashfaq Adnan
Date: 2021-04-03 00:00:00

In this study, the mechanical behavior of single tau, dimerized tau, and tau-microtubule interface subjected to high strain rate is investigated by molecular dynamics simulation.
Unexpected novel Merbecovirus discoveries in agricultural sequencing datasets from Wuhan, China
Authors: Daoyu Zhang, Adrian Jones, Yuri Deigin, Karl Sirotkin, Alejandro Sousa
Date: 2021-04-04 00:00:00
Tasks: Virology

In this study we document the unexpected discovery of multiple coronaviruses and a BSL-3 pathogen in agricultural cotton and rice sequencing datasets. In particular, we have identified a novel HKU5-related Merbecovirus in a cotton dataset sequenced by the Huazhong Agricultural University in 2017. We have also found an infectious clone sequence containing a novel HKU4-related Merbecovirus related to MERS coronavirus in a rice dataset sequenced by the Huazhong Agricultural University in early 2020. Another HKU5-related Merbecovirus, as well as Japanese encephalitis virus, were identified in a cotton dataset sequenced by the Huazhong Agricultural University in 2018. An HKU3-related Betacoronavirus was found in a Mus musculus sequencing dataset from the Wuhan Institute of Virology in 2017. Finally, a SARS-WIV1-like Betacoronavirus was found in a rice dataset sequenced by the Fujian Agriculture and Forestry University in 2017. Using the contaminating reads we have extracted from the above datasets, we were able to assemble complete genomes of two novel coronaviruses which we disclose herein. In light of our findings, we raise concerns about biosafety protocol breaches, as indicated by our discovery of multiple dangerous human pathogens in agricultural sequencing laboratories in Wuhan and Fouzou City, China.
DRAGON: Determining Regulatory Associations using Graphical models on multi-Omic Networks
Authors: Katherine H. Shutta, Deborah Weighill, Rebekka Burkholz, Marouen Ben Guebila, Dawn L. DeMeo, Helena U. Zacharias, John Quackenbush, Michael Altenbuchinger
Date: 2021-04-04 00:00:00

The increasing quantity of multi-omics data, such as methylomic and transcriptomic profiles, collected on the same specimen, or even on the same cell, provide a unique opportunity to explore the complex interactions that define cell phenotype and govern cellular responses to perturbations. We propose a network approach based on Gaussian Graphical Models (GGMs) that facilitates the joint analysis of paired omics data. This method, called DRAGON (Determining Regulatory Associations using Graphical models on multi-Omic Networks), calibrates its parameters to achieve an optimal trade-off between the network's complexity and estimation accuracy, while explicitly accounting for the characteristics of each of the assessed omics "layers." In simulation studies, we show that DRAGON adapts to edge density and feature size differences between omics layers, improving model inference and edge recovery compared to state-of-the-art methods. We further demonstrate in an analysis of joint transcriptome - methylome data from TCGA breast cancer specimens that DRAGON can identify key molecular mechanisms such as gene regulation via promoter methylation. In particular, we identify Transcription Factor AP-2 Beta (TFAP2B) as a potential multi-omic biomarker for basal-type breast cancer. DRAGON is available as open-source code in Python through the Network Zoo package (netZooPy v0.8; netzoo.github.io).
Boolean function metrics can assist modelers to check and choose logical rules
Authors: John Zobolas, Pedro T. Monteiro, Martin Kuiper, Åsmund Flobak
Date: 2021-04-02 00:00:00

Computational models of biological processes provide one of the most powerful methods for a detailed analysis of the mechanisms that drive the behavior of complex systems. Logic-based modeling has enhanced our understanding and interpretation of those systems. Defining rules that determine how the output activity of biological entities is regulated by their respective inputs has proven to be challenging, due to increasingly larger models and the presence of noise in data, allowing multiple model parameterizations to fit the experimental observations. We present several Boolean function metrics that provide modelers with the appropriate framework to analyze the impact of a particular model parameterization. We demonstrate the link between a semantic characterization of a Boolean function and its consistency with the model's underlying regulatory structure. We further define the properties that outline such consistency and show that several of the Boolean functions under study violate them, questioning their biological plausibility and subsequent use. We also illustrate that regulatory functions can have major differences with regard to their asymptotic output behavior, with some of them being biased towards specific Boolean outcomes when others are dependent on the ratio between activating and inhibitory regulators. Application results show that in a specific signaling cancer network, the function bias can be used to guide the choice of logical operators for a model that matches data observations. Moreover, graph analysis indicates that the standardized Boolean function bias becomes more prominent with increasing numbers of regulators, confirming the fact that rule specification can effectively determine regulatory outcome despite the complex dynamics of biological networks.
Thalamocortical contribution to solving credit assignment in neural systems
Authors: Mien Brabeeba Wang, Michael M. Halassa
Date: 2021-04-03 00:00:00
Tasks: Meta-Learning

Animal brains evolved to optimize behavior in dynamically changing environments, selecting actions that maximize future rewards. A large body of experimental work indicates that such optimization changes the wiring of neural circuits, appropriately mapping environmental input onto behavioral outputs. A major unsolved scientific question is how optimal wiring adjustments, which must target the connections responsible for rewards, can be accomplished when the relation between sensory inputs, action taken, environmental context with rewards is ambiguous. The computational problem of properly targeting cues, contexts and actions that lead to reward is known as structural, contextual and temporal credit assignment respectively. In this review, we survey prior approaches to these three types of problems and advance the notion that the brain's specialized neural architectures provide efficient solutions. Within this framework, the thalamus with its cortical and basal ganglia interactions serve as a systems-level solution to credit assignment. Specifically, we propose that thalamocortical interaction is the locus of meta-learning where the thalamus provides cortical control functions that parametrize the cortical activity association space. By selecting among these control functions, the basal ganglia hierarchically guide thalamocortical plasticity across two timescales to enable meta-learning. The faster timescale establishes contextual associations to enable rapid behavioral flexibility while the slower one enables generalization to new contexts. Incorporating different thalamic control functions under this framework clarifies how thalamocortical-basal ganglia interactions may simultaneously solve the three credit assignment problems.
Adaptive rewiring of random neural networks generates convergent-divergent units
Authors: Ilias Rentzeperis, Steeve Laquitaine, Cees van Leeuwen
Date: 2021-04-03 00:00:00
Tasks: Distributed Computing

Brain networks are adaptively rewired continually, adjusting their topology to bring about functionality and efficiency in sensory, motor and cognitive tasks. In model neural network architectures, adaptive rewiring generates complex, brain-like topologies. Present models, however, cannot account for the emergence of complex directed connectivity structures. We tested a biologically plausible model of adaptive rewiring in directed networks, based on two algorithms widely used in distributed computing: advection and consensus. When both are used in combination as rewiring criteria, adaptive rewiring shortens path length and enhances connectivity. When keeping a balance between advection and consensus, adaptive rewiring produces convergent-divergent units consisting of convergent hub nodes, which collect inputs from pools of sparsely connected, or local, nodes and project them via densely interconnected processing nodes onto divergent hubs that broadcast output back to the local pools. Convergent-divergent units operate within and between sensory, motor, and cognitive brain regions as their connective core, mediating context-sensitivity to local network units. By showing how these structures emerge spontaneously in directed networks models, adaptive rewiring offers self-organization as a principle for efficient information propagation and integration in the brain.